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AISI 5140 steel is mostly used in gear manufacturing for variety of industries. Those gears can be manufactured via casting, powder metallurgy or forging techniques. Nevertheless, machining (via turning and milling processes) remains the most common manufacturing method to fabricate them. Milling of gears made from 5140 steel can be challenging due to the excessive energy consumption, rapid tool wear and poor surface finish. Therefore, traditional and environmentally friendly coolants are usually applied during machining to improve the surface finish and prolong tool life. The current study aims to investigate machinability performance of 5140 steel under different cooling/lubrication conditions. Several machinability metrics were investigated and analyzed (surface roughness, cutting temperature, tool wear, chip morphology, and energy consumption). Milling tests were performed under different cutting speeds (75 and 100 m/min), different feed rates (0.15 and 0.2 mm/rev) and dry, minimum quantity lubrication (MQL), and cryogenic liquid cooling/lubrication conditions (dry, MQL and cryo-LN2). The results showed that using Cryo-LN2 cooling/lubrication tended to improve all the investigated machinability metrics compared to dry condition. The surface roughness was reduced by approximately 54%, while the cutting temperature was reduced by 87%. Similarity, the cutting tool flank wear was reduced by 20% thus energy consumption was minimized by 15%. The current study shows the importance of cryogenic machining in industry for difficult to cut materials.

The cutting parameters in drilling operations are important for high-quality holes and productivity improvement in any manufacturing industry. This study investigates the effects of spindle speed and feed rate on temperature, surface roughness, hole size, circularity, and chip formation during dry drilling of gray cast iron ASTM A48. The results showed that the temperature increased as spindle speed and feed rate increased. The surface roughness had an inverse relationship with the spindle speed and direct relation with the feed rate. Furthermore, hole size increased with increased spindle speed and decreased as the feed rate increased, while hole circularity decreased with increasing both the spindle speed and feed rate. The analysis of variance (ANOVA) indicated that the spindle speed had the highest percentage contribution of 56.24% on temperature, followed by the feed rate with 42.35%. The surface roughness was highly influenced by the feed rate and the spindle speed with 55% and 44.12%, respectively. While the hole size was highly influenced by the feed rate with a 74.18% percentage contribution, and the contribution of spindle speed was 21.36%. In addition, the feed rate has a percentage contribution of 70.82% on circularity, which is higher than the spindle speed of 24.26% percentage contribution. The results also showed that thick and discontinuous chips were generated at higher feed rates, while long continuous chips were produced at high spindle speeds.

This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed.

ABSTRACT: Industry 4.0 emphasizes developing an innovative approach to eliminating the problems caused by environmental and shop floor waste, which is accomplished by a suitable process optimization approach. The process optimization approach is used to maximize productivity within limited constraints by observing end-to-end management systems. The present research work developed an innovative agile model using the lean, smart, and green approach to improve operational performance within limited constraints in Industry 4.0. The proposed model was developed by thoroughly reviewing research articles conducted over the past decades on process optimization approaches that include lean manufacturing, smart manufacturing, kaizen, and lean six sigma. The model was validated through two real production case studies in the mining machinery and automobile industries. The present article concluded that overall operational performance was enhanced in both case studies by improvement in different factors, including working environment, worker efficiency, environmental evolution, logistics management, and resources utilization. The authors of the present article strongly believe that the proposed innovative agile model would help people in industry make aesthetic and smart sustainable production systems in Industry 4.0 within limited constraints.

ABSTRACT: The results of the development of a modified quality function deployment (QFD) method, obtained for the first time to improve the product quality planning process in the metal cutting of laser technologies confirming the dynamics of open innovation, are presented. When using new marketing technologies, the requirements of consumers are established; a factorial model of customer satisfaction was determined with after an assessment by expert methods, their wishes were transformed into indicators of product quality, and its concept was developed. Product quality is enhanced by optimizing project specifications. The method of functional cost analysis (FCA) was applied for the manufacturing processes, which made it possible to reduce the costs of their implementation. New methods of product quality control were applied. The developed planning process is described by a sequence diagram, an algorithm, and a responsibility matrix. The research used search methods including Internet resources required for open innovation, namely functional modeling methods (IDEF) and the method of advanced product quality planning (APQP). The results of the achieved advantages of open innovation (reduction in research and development costs, implementation of the principles of total quality management (TQM), customer orientation, process approach, improvement) are recommended for further application both at the enterprise and in the metalworking of mechanical engineering. The practical significance of the results of this work is the reduction in internal and external failures by 12%; reduction in time for new product development by 9%; reduction in costs for online resources by 11% and quality control during production by 7%; reduction in labor costs for process management by 25%; and the stability of the process, which improved by 25%.

Highly tensile manganese steel is in great demand owing to its high tensile strength under shock loads. All workpieces are produced through casting, because it is highly difficult to machine. The probabilistic aspects of its casting, its variable composition, and the different casting techniques must all be considered for the optimisation of its mechanical properties. A hybrid strategy is therefore proposed which combines decision trees and artificial neural networks (ANNs) for accurate and reliable prediction models for ore crushing plate lifetimes. The strategic blend of these two high-accuracy prediction models is used to generate simple decision trees which can reveal the main dataset features, thereby facilitating decision-making. Following a complexity analysis of a dataset with 450 different plates, the best model consisted of 9 different multilayer perceptrons, the inputs of which were only the Fe and Mn plate compositions. The model recorded a low root mean square error (RMSE) of only 0.0614 h for the lifetime of the plate: a very accurate result considering their varied lifetimes of between 746 and 6902 h in the dataset. Finally, the use of these models under real industrial conditions is presented in a heat map, namely a 2D representation of the main manufacturing process inputs with a colour scale which shows the predicted output, i.e. the expected lifetime of the manufactured plates. Thus, the hybrid strategy extracts core training dataset information in high-accuracy prediction models. This novel strategy merges the different capabilities of two families of machine-learning algorithms. It provides a high-accuracy industrial tool for the prediction of the full lifetime of highly tensile manganese steel plates. The results yielded a precision prediction of (RMSE of 0.061 h) for the full lifetime of (light, medium, and heavy) crusher plates manufactured with the three (experimental, classic, and highly efficient (new)) casting methods. Keywords: Hadfield steel, Resource savings, Lifetime prediction, Regression trees, Multi-layer perceptrons, Artificial intelligence

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (Ton), and pulse-off-time (Toff) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and Ton were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm3/s at Ton of 95 µs, Toff of 9 µs, current of 6 A. Least SR was obtained at Ton of 35 µs, Toff of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

Because of their high thermal conductivity, good corrosion resistance, and great mechanical qualities, copper matrix composites are appealing materials utilized in a variety of industries. This study investigates the mechanical properties of copper–graphite (Cu–Gr) matrix composites reinforced with silicon carbide (SiC) and tungsten carbide (WC) particles by hot pressing using powder metallurgy method. The goal is to investigate the influence of the reinforcement ratio on the mechanical characteristics of copper composite materials generated (density, hardness, flexural strength, and wear resistance). SEM, EDS, and X-RD analysis were used to perform metallographic examinations. The highest relative density with a value of 98.558% was determined in the C3 sample. The findings revealed that when the reinforcement ratio was raised, the hardness rose. The highest hardness value was observed in the C6 sample with an increase of 12.52%. Sample C4 (with the lowest SiC and WC particles ratio) had the highest bending stress (233.18 MPa). Bending stress increased by 35.56% compared to the C1 sample. The lowest specific wear rates were found in the C4 sample, with a decrease of 82.57% compared to the C1 sample. The lowest wear rate (6.853 × 10−7 mm3/Nm) also occurred in the C4 sample. The microstructural analysis showed that the hybrid reinforcement particles exhibited a homogeneous distribution in the copper matrix. X-RD analysis showed that there was no intermediate reaction between the parent matrix and the hybrid reinforcements. A good interfacial bond was observed between the matrix structure and the hybrid reinforcements. The motivation of this research was to utilise the advantages of the unique features of SiC–WC hybrid particles to improve the performance of newly developed Cu-6Gr composites for wear-resistance applications.

The development of the best properties in polyester composite from pineapple leaf fiber (PALF) as a reinforcing material is a subject of interest. The properties of PALF are reliant upon fiber length, wherein technical difficulties in production of long fibers and processing for better characteristics in polyester composites possess inherent challenges. The PALFs are subjected to silane treatment for altering fiber properties. This research attempts to analyze the impact of silane-treated PALF with varying fiber lengths (5, 10, 15, 20, and 25 mm) on the performance of natural fiber composites (NFC) properties. Open mold and hand lay-up techniques were employed to develop the polyester composites. The prepared PALF-based polyester composites were examined for different properties (impact, flexural, tensile strength, and wear rate). Coefficient of friction and wear studies are performed on the prepared composites subjected to different loads (10, 20, and 30 N) via a pin on disc test rig. Polymer composite fracture surfaces were analyzed to observe the interfacial bonding between fibers and matrix via scanning electron microscopy (SEM). SEM results showed that the application of silane treatment resulted in better surface topography (fiber length of 5–10 mm showed smooth surface resulted in crack proliferation possessing low fracture toughness of 15–32 MPa; whereas a 15–20 mm fiber length resulted in better fiber–matrix bonding, improving the fracture toughness from 42–55 MPa) as a result of change in chemical structure in PALF. The 20 mm length of PALF resulted in better properties (flexural, tensile, impact, and wear resistance) which are attributed to fiber–matrix interfacial bonding. These properties ensure the developed polymer composites can be applied to walls, building insulation, and artificial ceilings.

Currently, there is an intensive development of bipedal walking robots. The most known solutions are based on the use of the principles of human gait created in nature during evolution. Modernbipedal robots are also based on the locomotion manners of birds. This review presents the current state of the art of bipedal walking robots based on natural bipedal movements (human and bird) as well as on innovative synthetic solutions. Firstly, an overview of the scientific analysis of human gait is provided as a basis for the design of bipedal robots. The full human gait cycle that consists of two main phases is analysed and the attention is paid to the problem of balance and stability, especially in the single support phase when the bipedal movement is unstable. The influences of passive or active gait on energy demand are also discussed. Most studies are explored based on the zero moment. Furthermore, a review of the knowledge on the specific locomotor characteristics of birds, whose kinematics are derived from dinosaurs and provide them with both walking and running abilities, is presented. Secondly, many types of bipedal robot solutions are reviewed, which include nature-inspired robots (human-like and birdlike robots) and innovative robots using new heuristic, synthetic ideas for locomotion. Totally 45 robotic solutions are gathered by thebibliographic search method. Atlas was mentioned as one of the most perfect human-like robots, while the birdlike robot cases were Cassie and Digit. Innovative robots are presented, such asslider robot without knees, robots with rotating feet (3 and 4 degrees of freedom), and the hybrid robot Leo, which can walk on surfaces and fly. In particular, the paper describes in detail the robots’ propulsion systems (electric, hydraulic), the structure of the lower limb (serial, parallel, mixed mechanisms), the types and structures of control and sensor systems, and the energy efficiency of the robots. Terrain roughness recognition systems using different sensor systems based on light detection and ranging or multiple cameras are introduced. A comparison of performance, control and sensor systems, drive systems, and achievements of known human-like and birdlike robots is provided. Thirdly, for the first time, the review comments on the future of bipedal robots in relation to the concepts of conventional (natural bipedal) and synthetic unconventional gait. We critically assess and compare prospective directions for further research that involve the development of navigation systems, artificial intelligence, collaboration with humans, areas for the development of bipedal robot applications in everyday life, therapy, and industry.

The current study focuses on minimising the bubbles in polyester-insulated ignition coils, which were produced with a defect level of ~21–25% or 210–250 coils per 1000 batch size by using the potting method. This high-level rejection makes a substantial financial impact by increasing waste material, manufacturing, and after-sales costs. Hence, to control the bubbled problem without using expensive and maintenance-heavy techniques, the process parameters in the potting method were alternated and investigated using one factor at a time, which played a vital role in the formation/reduction of bubbles in the ignition coil insulation. Process parameters, including pre/process heating, the appropriate MEKP/cobalt naphthenate ratio, the pouring amount/increments, and the stirring speeds, reduced the bubble formation per lot from 205 ± 30 to 146 ± 25, 108 ± 21, 61 ± 17, and 10 ± 2 per 1000 lot accordingly. In addition, a comparative study was conducted in terms of performance and life cycle endurance, using Japanese and Indian standards. Furthermore, an after-sale warranty claim also supports the proposed changes in the potting technique. This modification may reduce the after-sales rejection within two years to approximately ~85%. This modification in the potting technique is extremely cost-effective in comparison to expensive processes, i.e., vacuum-pressure impregnation and vacuum impregnation, which require extensive labour and maintenance.

The fabrication of components involves the deposition of multiple beads in multiple layers for wire-arc additive manufacturing (WAAM). WAAM performed using gas metal arc welding (GMAW) allows for the manufacturing of parts through multiple-bead multi-layer deposition, which depends on the process variables. Thus, the selection of process parameters along with their required levels is mandatory to deposit multiple layers for WAAM. To obtain the desired levels of parameters, bead-on-plate trials were taken on the base plate of low alloy steel by following an experimental matrix produced through the Box–Behnken design (BBD) on GMAW-based WAAM. Wire feed speed, travel speed, and voltage were chosen as the input parameters and bead width and bead height were chosen as the output parameters. Furthermore, the robustness and adequacy of the obtained regression equations were analyzed by using analysis of variance (ANOVA). For both responses of BW and BH, values of R2 and adj. R2 were found to be near unity, which has shown the fitness of the model. Teaching–learning-based optimization (TLBO) technique was then employed for optimization. Within the selected range of process variables, the single-objective optimization result showed a maximum bead height (BH) of 7.81 mm, and a minimum bead width (BW) of 4.73 mm. To tackle the contradicting nature of responses, Pareto fronts were also generated, which provides a unique non-dominated solution. Validation trials were also conducted to reveal the ability and suitability of the TLBO algorithm. The discrepancy between the anticipated and measured values was observed to be negligible, with a deviation of less than 5% for all the validation trials. This demonstrates the success of the established model and TLBO algorithm. The optimum feasible settings for multi-layer metal deposition were determined after further tuning. A multi-layer structure free from any disbonding was successfully manufactured at the optimized variables. The authors suggest that the optimum parametric settings would be beneficial for the deposition of layer-by-layer weld beads for additive manufacturing of components.

Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy.

Engineering materials are expected to contain physical and mechanical properties to meet the requirements and to improve the functionality according to their application area. In this direction, hybrid composites stand as an excellent option to fulfill these requests thanks to their production procedure. Despite the powder metallurgy method that allows for manufacturing products with high accuracy, machining operations are still required to obtain a final product. On the other hand, such materials are characterized with uncertainties in the structure and extremely hard reinforcement particles that aggravate the machinability. One of the prominent solutions for better machinability of composites is to use evolutionary cooling and lubricating strategies. This study focuses on the determination of tribological behavior of Cu-based, B-Ti-SiCP reinforced, about 5% wt. hybrid composites under milling of several environments, such as dry, minimum quantity lubrication (MQL)-assisted and cryogenic LN2-assisted. Comprehensive evaluation was carried out by considering tool wear, temperature, energy, surface roughness, surface texture and chips morphology as the machinability characteristics. The findings of this experimental research showed that cryogenic cooling improves the tribological conditions by reducing the cutting temperatures, flank wear tendency and required cutting energy. On the other hand, MQL based lubricating strategy provided the best tool wear index and surface characteristics, i.e., surface roughness and surface topography, which is related to spectacular ability in developing the friction conditions in the deformation zones. Therefore, this paper offers a novel milling strategy for Cu-based hybrid composites with the help of environmentally-friendly techniques.

In the rail industry, there are four types of steel grades used for monoblock wheels, namely ER6, ER7, ER8 and ER9. ER7 steel is manufactured in accordance with the EN13262 standard and is utilized in European railway lines. These train wheels are formed by pressing and rolling after which they are machined using turning process to achieve their final dimensions. However, machining ER7 steels can be challenging due to their high mechanical properties, which can facilitate rapid tool wear and thermal cracking. Therefore, while the use of coolants is critical to improving their machinability, using conventional flood coolants adds extra operational costs, energy and waste. An alternative is to use minimum quantity lubrication (MQL) cooling technology, which applies small amounts of coolant mixed with air to the cutting zone, leaving a near-dry machined surface. In the current study, preliminary tests were undertaken under dry conditions and using coated carbide inserts to determine the optimal cutting parameters for machining ER7 steel. The impact of the cutting speed and feed rate on surface roughness (Ra), energy consumption and cutting temperature were investigated and used as a benchmark to determine the optimal cutting parameters. Next, additional machining tests were conducted using MQL and nano-MQL cooling technologies to determine their impact on the aforementioned machining outputs. According to preliminary tests, and within the tested range of the cutting parameters, using a cutting speed of 300 m/min and a feed rate of 0.15 mm/rev resulted in minimal surface roughness. As a result, using these optimal cutting parameters with MQL and Nano-MQL (NMQL) cooling technologies, the surface roughness was further reduced by 24% and 34%, respectively, in comparison to dry conditions. Additionally, tool wear was reduced by 34.1% and 37.6%, respectively. The overall results from this study demonstrated the feasibility of using MQL coolants as a sustainable machining alternative for steel parts for rail wheel applications. In addition, the current study highlight the enhanced performance of MQL cooling technology with the addition of nano additives.

Prognostics and health management (PHM) is a framework to identify damage prior to its occurrence which leads to the reduction of both maintenance costs and safety hazards. Based on the data collected in condition monitoring, the degradation of the part is predicted. Studies show that most failures are caused by faults in rolling element bearing, which highlights that a bearing is one of the most important mechanical components of any machine. Thus, it becomes important to monitor bearing degradation to make sure that it is utilized properly. Generally, machine learning (ML) or deep learning (DL) techniques are utilized to predict bearing degradation using a data-driven approach, where signals are captured from the machine. There should be a large amount of data to apply either ML or DL techniques, but it is difficult to collect that amount of data directly from any machine. In this study, health assessment is carried out using the correlation coefficient to divide the bearing life into two degradation stages. The raw signal is processed using discrete wavelet transform (DWT), where mutual information (MI) is used to rank and select the base wavelet, after which tabular generative adversarial networks (TGAN) are used to generate the artificial coefficients. Statistical features are calculated from the real data (DWT coefficients) and the artificial data (generated from TGAN). The constructed feature vector is then used as an input to train machine learning models, namely ensemble bagged tree (EBT) and Gaussian process regression with the squared exponential kernel function (SEGPR), to estimate bearing degradation conditions. Both the machine learning models were validated on the publicly available experimental data of FEMTO bearing. Obtained results showed that the developed EBT and SEGPR models accurately predicted the bearing degradation conditions with the average lowest RMSE value of 0.0045 and MAE value of 0.0037.

In the food industry, there are many varieties of technical blades with different contours as well as different cutting edge geometries. The evaluation of the ability of technical blades to separate (cut) animal tissues is not a simple task and is usually based on the evaluation of the cutting effects in a technological process. This paper presents a methodology for evaluating the cutting force of technical blades used in food processing. A specially made test stand with numerical control was used in the study. Its application enabled a comparison of cutting force values for four different cutting edge geometries of planar knives used in the skinning operation of flat fishes. A unique feature of the conducted research was the use of a relatively high cutting speed value of vf = 214 mm/s, which corresponded to the real conditions of this process carried out in the industry. Obtained test results allow unambiguously choosing the most advantageous variant of knife geometry from among four different variants used for the tests. The results showed a clear relationship between the cutting force value and the value of the tip angle of the blades tested: for blades with the lowest tip angle, the lowest cutting force values were obtained.

In this study, the skull bone thicknesses of 150 patients ranging in age from 0 to 72 years were calculated using a novel approach (thermal analysis), and thickness changes were analyzed. Unlike conventional thickness calculation approaches (Beam Propagation, Hildebrand), a novel heat transfer-based approach was developed. Firstly, solid 3D objects with different thicknesses were modeled, and thermal analyses were performed on these models. To better understand the heat transfer of 3D object models, finite element models (FEM) of the human head have been reported in the literature. The FEM can more accurately model the complex geometry of a 3D human head model. Then, thermal analysis was performed on human skulls using the same methods. Thus, the skull bone thicknesses at different ages and in different genders from region to region were determined. The skull model was transferred to ANSYS, and it was meshed using different mapping parameters. The heat transfer results were determined by applying different heat values to the inner and outer surfaces of the skull mesh structure. Thus, the average thicknesses of skull regions belonging to a certain age group were obtained. With this developed method, it was observed that the temperature value applied to the skull was proportional to the thickness value. The average thickness of skull bones for men (frontal: 7.8 mm; parietal: 9.6 mm; occipital: 10.1 mm; temporal: 6 mm) and women (frontal: 8.6 mm; parietal: 10.1 mm; occipital: 10 mm; temporal: 6 mm) are given. The difference (10%) between men and women appears to be statistically significant only for frontal bone thickness. Thanks to the developed method, bone thickness information at any desired point on the skull can be obtained numerically. Therefore, the proposed method can be used to help pre-operative planning of surgical procedures.

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

Minimum quantity lubrication (MQL) is a sustainable method that has been efficiently applied to achieve machinability improvements with various materials in recent years, such as hardened steels, superalloys, soft metals, and composites. This study is the first to focus on the performance evaluation of MQL and dry milling environments with AISI 1040 steel. The tool wear, cutting temperature, and power consumption were considered as the quality responses while cutting speed, feed rate and machining environment are taken as input parameters. The effects of the influential factors are analyzed using analysis of variance (ANOVA) and bar charts. Additionally, Taguchi signal-to-noise (S/N) ratios are utilized in order to determine the optimum parameters for the best quality responses. The results show that the MQL system provides better performance compared to dry milling by reducing the tool wear, cutting temperature, and power consumption. According to the ANOVA results, the cutting environment affects the cutting temperature (37%) and power consumption (94%), while cutting speed has importance effects on the tool wear (74%). A lower cutting speed (100 m/min) and feed rate (0.10 mm/rev) should be selected under MQL conditions to ensure minimum tool wear and power consumption; however, a higher feed rate (0.15 mm/rev) needs to be selected along with a low cutting speed and MQL conditions to ensure better temperatures. A comparative evaluation is carried out on the tool wear, cutting temperature, and power consumption under MQL and dry environments. This investigation is expected to contribute to the current literature, highlighting the superiority of sustainable methods in the milling of industrially important materials.

Industrial practitioners are working on predictive solutions for the precise evaluation of input parameters and processed surfaces of engineering materials. To aid the aeronautical industry, this study is an effort to develop the mathematical modelling for comprehensive surface analysis of input parameters and surface finish after dry machining of CLARM HBR, a steel alloy with attractive mechanical properties and wide applications in large caliber gun barrels and high-pressure vessels. Feed rate, rotational speed, and depth of cut were taken as quantitative parameters, whereas machining time was considered as a categorical factor with a classification of three levels. Response surface methodology (RSM) with a central component design has been used for the constitution of the experimental design, mathematical modelling, and analysis of developed models. Eighteen samples were prepared to perform the experimentation for the development of prediction models. The adequacy of the developed models was verified using analysis of variance (ANOVA), and the models were validated using confirmatory trial experiments, which revealed the experimental results agreeing with predictions. The feed rate was the most significant parameter in achieving the desired surface finish. An increase in rotational speed at a low feed rate resulted in very fine surface texture, as though it deteriorated the surface finish at higher feed rates. The superior surface quality obtained was 0.137 μm at parametric settings of 0.19 mm/rev feed, 90 rpm speed, 3 mm depth of cut, and 4 min time. Overall, higher values of surface roughness were frecorded in the third level of process variable time. The developed empirical models are expected to aid manufacturers and machining practitioners in the prediction of the desired surface finish concerning different parameters before the experimentations.

The present study investigates the thermo-hydraulic characteristics of a microchannel sink with novel trefoil Shaped ribs. The motivation for this form of rib shape is taken from the design of lung alveoli that exchange oxygen and carbon dioxide. This study has been conducted numerically by using a code from the commercially available Fluent software. The trefoil shaped ribs were mounted on the centerline of different walls of the microchannel in three different configurations. These consisted of base wall trefoil ribs (MC-BWTR), sidewall trefoil ribs (MC-SWTR), all wall trefoil ribs (MC-AWTR) and smooth channel (MC-SC) having no ribs on its wall. The streamline distance between the ribs was kept constant at 0.4 mm, and the results were compared by using pressure drop (∆p), Nusselt number (Nu), thermal resistance (Rth) and thermal enhancement factor (η). The results indicated that the addition of trefoil ribs to any wall improved heat transfer characteristics at the expense of an increase in the friction factor. The trends of the pressure drop and heat transfer coefficient were the same, which indicated higher values for MC-AWTR followed by MC-SWTR and a lower value for MC-BWTR. In order to compare the thermal and hydraulic performance of all the configurations simultaneously, the overall performance was quantified in terms of the thermal enhancement factor, which was higher than one in each case, except for MC-AWTR, in 100 < Re < 200 regimes. The thermal enhancement factor in the ribbed channel was the highest for MC-SWTR followed by MC-BWTR, and it was the lowest for MC-AWTR. Moreover, the thermal enhancement factor increases with the Reynolds number (Re) for each case. This confirms that the increment in the Nusselt number with velocity is more significant than the pressure drop. The highest thermal enhancement factor of 1.6 was attained for MC-SWTR at Re = 1000, and the lowest value of 0.87 was achieved for MC-AWTR at Re = 100.

In the current study, laser cutting of Ti6Al4V was accomplished using Taguchi’s L9 orthogonal array (OA). Laser power, cutting speed, and gas pressure were selected as input process parameters, whereas surface roughness (SR), kerf width, dross height, and material removal rate (MRR) were considered as output variables. The effects of input variables were analyzed through the analysis of variance (ANOVA), main effect plots, residual plots, and contour plots. A heat transfer search algorithm was used to optimize the parameters for the single objective function including higher MRR, minimum SR, minimum dross, and minimum kerf. A multi-objective heat transfer search algorithm was used to create non-dominant optimal Pareto points, giving unique optimal solutions with the corresponding input parameters. For better understanding and ease of selection of input parameters in industry and by scientists, a Pareto graph (2D and 3D graph) is generated from the Pareto points.

Increasing the energy efficiency of machining operations can contribute to more sustainable manufacturing. Therefore, there is a necessity to investigate, evaluate, and optimize the energy consumed during machining operations. The research highlights a method employed to prioritize the most energy-intensive machining operation and highlights the significance of electric parameters as predictors in power estimation of machining operations. Multi regression modeling with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations. The absolute error and the relative error both decreased when the active power was measured by the power analyzer for each of the identified machining operations, compared to the standard power equation and that obtained from the modeled regression equations. Furthermore, to determine energy-intensive machining operation, a hybrid decision-making technique based on TOPSIS (a technique for order preference by similarity to ideal solution) and DoM (degree of membership) was utilized. Allocation of weights to energy responses was carried out using three methods, i.e., equal importance, entropy weights, and the AHP (analytical hierarchy process). Results revealed that a drilling process carried out on material ST 52.3 is energy-intensive. This accentuates the significance of electric parameters in the assessment of active power during machining operations.

In this work, wire electrical discharge machining (WEDM) of aluminum (LM25) reinforced with fly ash and boron carbide (B4C) hybrid composites was performed to investigate the influence of reinforcement wt% and machining parameters on the performance characteristics. The hybrid composite specimens were fabricated through the stir casting process by varying the wt% of reinforcements from 3 to 9. In the machinability studies, the WEDM process control parameters such as gap voltage, pulse-on time, pulse-off time, and wire feed were varied to analyze their effects on machining performance including volume removal rate and surface roughness. The WEDM experiments were planned and conducted through the L27 orthogonal array approach of the Taguchi methodology, and the corresponding volume removal rate and surface roughness were measured. In addition, the multi-parametric ANOVA was performed to examine the statistical significance of the process control parameters on the volume removal rate and surface roughness. Furthermore, the spatial distribution of the parameter values for both the responses were statistically analyzed to confirm the selection of the range of the process control parameters. Finally, the quadratic multiple linear regression models (MLRMs) were formulated based on the correlation between the process control parameters and output responses. The Grass–Hooper Optimization (GHO) algorithm was proposed in this work to identify the optimal process control parameters through the MLRMs, in light of simultaneously maximizing the volume removal rate and minimizing the surface roughness. The effectiveness of the proposed GHO algorithm was tested against the results of the particle swarm optimization and moth-flame optimization algorithms. From the results, it was identified that the GHO algorithm outperformed the others in terms of maximizing volume removal rate and minimizing the surface roughness values. Furthermore, the confirmation experiment was also carried out to validate the optimal combination of process control parameters obtained through the GHO algorithm.

The Activated Tungsten Inert Gas welding (A-TIG) technique is characterized by its capability to impart enhanced penetration in single pass welding. Weld bead shape achieved by A-TIG welding has a major part in deciding the final quality of the weld. Various machining variables influence the weld bead shape and hence an optimum combination of machining variables is of utmost importance. The current study has reported the optimization of machining variables of A-TIG welding technique by integrating Response Surface Methodology (RSM) with an innovative Heat Transfer Search (HTS) optimization algorithm, particularly for attaining full penetration in 6 mm thick carbon steels. Welding current, length of the arc and torch travel speed were selected as input process parameters, whereas penetration depth, depth-to-width ratio, heat input and width of the heat-affected zone were considered as output variables for the investigations. Using the experimental data, statistical models were generated for the response characteristics. Four different case studies, simulating the real-time fabrication problem, were considered and the optimization was carried out using HTS. Validation tests were also carried out for these case studies and 3D surface plots were generated to confirm the effectiveness of the HTS algorithm. It was found that the HTS algorithm effectively optimized the process parameters and negligible errors were observed when predicted and experimental values compared. HTS algorithm is a parameter-less optimization technique and hence it is easy to implement with higher effectiveness.

Distribution of temperature during the welding process is essential for predicting and realizing some important welding features such as microstructure of the welds, heat-affected zone (HAZ), residual stresses, and their effects. In this paper, a numerical model was developed using COMSOL Multiphysics of dissimilar laser welding (butt joint) of AISI 316L and Ti6Al4V thin sheet of 2.5 mm thickness. A continuous mode (CW) fiber laser heat source of 300 W laser power was used for the present study. A time-dependent prediction of temperature distributions was attempted. The heat source was assumed as a Hermit–Gaussian analytical function with a moving velocity of 120 mm/min. Both convective and radiant heat loss and phase change of the materials were considered for the analysis. In addition, variation of temperature-dependent material properties was also considered. The maximum and minimum temperature for the two materials at different times and the temperature in the different penetration depths were also predicted. It was found that the average temperature that can be achieved in the bottom-most surface near the weld line was more than 2400 K, which justifies the penetration. Averages of maximum temperatures on the weld line at different times at the laser spot irradiation were identified near 3000 K.The temperature fluctuation near the weld line was minimal and decreased more in the traverse direction. Scanning with a displaced laser relative to the interface toward the Ti6Al4V side reduces the maximum temperature at the interface and the HAZ of the 316L side. All of these predictions agree well with the experimental results reported in current literature studies.

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation.

Drilling is an important machining process in various manufacturing industries. High-quality holes are possible with the proper selection of tools and cutting parameters. This study investigates the effect of spindle speed, feed rate, and drill diameter on the generated thrust force, the formation of chips, post-machining tool condition, and hole quality. The hole surface defects and the top and bottom edge conditions were also investigated using scan electron microscopy. The drilling tests were carried out on AA2024-T3 alloy under a dry drilling environment using 6 and 10 mm uncoated carbide tools. Analysis of Variance was employed to further evaluate the influence of the input parameters on the analysed outputs. The results show that the thrust force was highly influenced by feed rate and drill size. The high spindle speed resulted in higher surface roughness, while the increase in the feed rate produced more burrs around the edges of the holes. Additionally, the burrs formed at the exit side of holes were larger than those formed at the entry side. The high drill size resulted in greater chip thickness and an increased built-up edge on the cutting tools.

Millions of holes are produced in many industries where efficient drilling is considered the key factor in their success. High-quality holes are possible with the proper selection of drilling process parameters, appropriate tools, and machine setup. This paper deals with the effects of drilling parameters such as spindle speed and feed rate on the chips analysis and the hole quality like surface roughness, hole size, circularity, and burr formation. Al7075-T6 alloy, commonly used in the aerospace industry, was used for the drilling process, and the dry drilling experiments were performed using high-speed steel drill bits. Results have shown that surface roughness decreased with the increase in spindle speed and increased with the increase in the feed rate. The hole size increased with the high spindle speed, whereas the impact of spindle speed on circularity error was found insignificant. Furthermore, short and segmented chips were achieved at a high feed rate and low spindle speed. The percentage contribution of each input parameter on the output drilling parameters was evaluated using analysis of variance (ANOVA).

The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR.

Industries demand stringent requirements towards economical machining without hindering the surface quality while cutting high carbon high chromium (HcHcr) steel. Electrical discharge machining (EDM) of HcHcr steel aims at reducing machining cost (i.e., maximize material removal rate (MRR) and minimize tool wear rate (TWR)) with good surface quality (i.e., minimize surface roughness (SR)). A comparative study was carried out on EDM of HcHcr D2 steel (DIN EN ISO 4957) by applying Taguchi L18 experimental design considering different electrode materials (copper, graphite, and brass), dielectric fluids (distilled water and kerosene), peak current, and pulse-on-time. The process performances were analyzed with respect to material removal rate, surface roughness, and tool wear rate. Pareto analysis of variance was employed to estimate the significance of the process variables and their optimal levels for achieving lower SR and TWR and higher MRR. Hybrid Taguchi-CRITIC-Utility and Taguchi-PCA-Utility methods were implemented to determine the optimal EDM parameters. Higher MRR of 0.0632 g/min and lower SR of 1.68 µm and TWR of 0.012 g/min was attained by graphite electrode in presence of distilled water as dielectric fluid compared to the brass and copper. Additionally, a metallographic analysis was carried out to study the surface integrity on the machined surfaces. Micrographic analysis of the optimal conditions showed lower surface roughness and fewer imperfections (lesser impression, waviness surface, and micro-cracks) compared to worst conditions.

Air-leakage detection is among the most important processes at the assembly stage of unclosed components, especially for large aircraft. A series of air-leakage detecting methods are generally applied during the final assembly, nevertheless, many of them are less effective to detect the leakage at the assembly stage. The present study aims to discuss the principles of ultrasonic generation in negative pressure conditions to detect the air-leakage. An ultrasonic-based detection method is proposed and designed to detect the air-leakage of unclosed components for aircraft. A relationship between the acoustic power, sound pressure, and the leak aperture detection distance was identified and discussed. A leakage rate model related to leakage rate, leak aperture, and system pressure was implemented and confirmed through experiments. Findings have indicated that the air-leakage can be detected effectively within a detection distance of 0.8 m and a leak aperture greater or equal to 0.4 mm with this method. Besides, the leak location, leak aperture, and leakage rate was acquired in an accurate and fast way. It is an effective method of detecting the air-leakage of unclosed components at the aircraft assembly stage reducing the testing time, energy consumption, and cost for the air-leakage detection in the final assembly stage of large aircraft.

Composite materials are used in various industries such as marine, aircraft, automotive, etc. In marine applications, composites are exposed to seawater, which can affect their mechanical properties due to moisture absorption. This work focuses on the durability of composite materials under the short-term effect of seawater ageing. The specimens were prepared from glass fiber/epoxy using a hand lap-up method and stitched in the z-direction with Kevlar fiber. The specimens were submerged in seawater for 24 and 35 days. A significant decrease in maximum load was found as specimen immersion time in seawater increased. The seawater ageing also affected fracture toughness with a reduction of 30% for 24 days immersion and 55% for 35 days. The ageing also caused the swelling of composites due to moisture absorption, which increased the weight of the specimens. Compared to the dry specimens, the weight of the specimen for 24 days increases to 5.2% and 7.89% for 35 days’ seawater ageing. The analysis also showed that due to seawater ageing, the de-bonding rate increased as the number of days increased.

Cutting forces are strongly associated with the mechanics of the cutting process. Hence, machining forces measurements are very important to investigate the machining process, and numerous methods of cutting forces measurements have been applied. Nowadays, a dynamometer is the most popular tool for cutting forces measurements. However, the natural frequency of a dynamometer has a direct impact on the accuracy of measured cutting forces in the machining process. Therefore, few dynamometers are appropriate and reliable to measure the cutting forces at high frequencies. In this work, a new strain-gauge-based dynamometer (SGBD) with a special structure was designed, manufactured, and put to the test to ensure the measurement of high-frequency dynamic forces in the milling process. The main structure of the SGBD is symmetrical and mainly consists of a center quadrangular prism surrounded by four force sensing elastic elements, an upper support plate, and a lower support plate. The dynamic identification test was conducted and indicated that the SGBD′s natural frequency could be stabilized at a high value of 9.15 kHz. To automatically obtain the milling force data with a computer during high rotational speed milling, a data acquisition system was devised for the dynamometer. To reduce the effects of cross-sensitivity and acting point of force, an innovative model based on a conversion matrix was established for the dynamometer. Furthermore, the cutting tests were conducted at high rotational speeds (10,000–18,000 rpm), and it was found that the difference of cutting forces between the SGBD and a Kistler dynamometer are 2.3–5.8% in the X direction and 3.5–8.8% in the Y direction. The experimental findings disclosed that the new kind of dynamometer is reliably for the measurement of high-frequency dynamic forces in milling at high rotational speeds.

Surface roughness and burr formation are among the most important surface quality metrics which determine the quality of the fabricated parts. High precision machined microparts with complex features require micromachining process to achieve the desired yet stringent surface finish and dimensional accuracy. In this research, the effect of cutting speed (m/min), feed rate (µm/tooth), depth of cut (µm) and three types of tool coating (AlTiN, nACo and TiSiN) were analyzed to study their effect on surface roughness and burr formation during the micromachining of Inconel 718. The analysis was carried out using an optical profilometer, scanning electron microscope and statistical technique. Machining tests were performed at low speed with a feed rate (µm/tooth) below the cutting-edge radius for 10 mm cutting length using a carbide tool of 0.5 mm diameter on a CNC milling machine. From this research, it was determined that the depth of cut was the main factor affecting burr formation, while cutting velocity was the main factor affecting the surface roughness. In addition, cutting tool coating did not significantly affect either surface roughness or burr formation due to the difference in coefficient of friction. The types of burr formed during micromilling of Inconel 718 were mainly influenced by the depth of cut and feed rate (µm/tooth) and were not affected by the cutting velocity. It was also concluded that the results for the surface finish at low-speed machining are comparable to that of transition and high-speed machining, while the burr width found during confirmation experiments at low-speed machining was also within an acceptable range.

The hardened tool steel AISI O1 has increased strength, hardness, and wear resistance, which affects the complexity of the machining process. AISI O1 has also been classified as difficult to cut material hence optimum cutting parameters are required for the sustainable machining of the alloy. In this work, the effect of feed peer tooth (fz), cutting speed (vc), cutting of depth (ap) on surface roughness (Ra, Rt), cutting force (Fx, Fy), cutting power (Pc), machining cost (Ci), and carbon dioxide (Ene) were investigated during the slot milling process of AISI O1 hardened steel. A regression analysis was carried out on the obtained experimental results and the induction of nonlinear mathematical equations of surface roughness, cutting force, cutting power, and machining cost with a high coefficient of determination (R2 = 90.62–98.74%) were deduced. A sustainability assessment model is obtained for optimal and stable levels of design variables when slot milling AISI O1 tool steel. Stable indicators to ensure personal health and safety of operation, P1 values were set to “1” at a cutting speed of 20 m/min or 43.3 m/min and “2” at a cutting speed of 66.7 m/min or 90 m/min. It is revealed that for eco-benign machining of AISI O1, the optimum parameters of 0.01 mm/tooth, 20 m/min, and 0.1 mm should be adopted for feed rate, cutting speed, and depth of cut respectively.

Machining of the nickel-based alloy is very demanding due to its extreme mechanical properties, for example, higher fatigue strength, better corrosion and creep resistance feature, substantial work hardening capability, and appreciable tensile and shear strength. Owing to these properties, the selection of machining parameters is a major challenge for modern machining industries. Therefore, the present experimental work is carried out to select the best parametric combination of the wire electrical discharge machining (WEDM) machine for reducing machining cost and human effort. The Trapezoidal Interval Type-2 fuzzy number (T2FS) integrated Analytical Hierarchy Process (AHP)-based Additive Ratio Assessment (ARAS) method is used for selecting the best WEDM process parameters of Inconel-800 superalloy. Finally, the results were compared with some existing multi-criteria decision-making methods to confirm the validity of the adopted method. The comparison shows that Type-2 Fuzzy AHP-ARAS synergy can help to formulate the problem and facilitate the assessment and ranking of WEDM process parameters when multiple criteria are jointly considered.

The purpose of this investigation is to compute overall equipment effectiveness (OEE) in the small-scale industry. The novel approach is introduced to detect bottlenecks by which OEE can be improved. This study attempts to help small-medium enterprises in analyzing performance in a better way. The automotive industry was chosen for conducting the research. The present study is comprised of three phases. In the first phase, OEE was computed and compared with world-class manufacturing. The second phase included three-level of Pareto analysis followed by making fishbone diagram to mitigate the losses. The third phase conducted improved OEE in the industry. There are seven major losses present in the industry that adversely affect the effectiveness of machine in any industry. This approach can reduce these losses and improve the quality, asset utilization (AU), OEE, total effective equipment performance (TEEP) and productivity of the machine. The study exposes that Pareto analysis uncovers all the losses and works on the principle of 80/20 rule. The major losses were thoroughly explored with the help of the fishbone diagram and solutions were implemented at the shop floor. As a result, availability, performance, quality, OEE, AU, and TEPP show improvements by 4.6%, 8.06%, 6.66%, 16.23%, 4.16%, and 14.58%, respectively. The approach offers a good opportunity for both researchers and small-medium enterprises around the world to analyze the indicators of production losses, performance, and productivity in the manufacturing industry.

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

Sustainability assessments of cooling/lubrication-assisted advanced machining processes has been demanded by environment control agencies because it is an effective management tool for improving process sustainability. To achieve an effective and efficient sustainability evolution of machining processes, there is a need to develop a new method that can incorporate qualitative indicators to create a quantifiable value. In the present research work, a novel quantifiable sustainability value assessment method was proposed to provide performance quantification of the existing sustainability assessment methods. The proposed method consists of three steps: establishing sustainable guidelines and identifying new indicators, data acquisition, and developing an algorithm, which creates the Overall Performance Assessment Indicator (OPAI) from the sustainability assessment method. In the proposed algorithm, initially, both quantitative and qualitative sustainability indicators are normalized. After weight assignment and aggregation, the OPAI is obtained. The developed algorithm was validated from three literature case studies, and optimal cutting parameters were obtained. The present methodology provides effective guidelines for a machinist to enhance process performance and achieve process optimization. The study also offers a relationship between sustainable and machining metrics for the support of industrial sustainability.

Design and manufacturing are the key steps in the sustainable manufacturing of any product to be produced. Within the perspective of injection molds production, increased competitiveness and repeated changes in the design require a complete optimized manufacturing process. Local and minor improvements in the milling process do not generally lead to an optimized manufacturing process. The goal of the new geometry and parametric analysis of the mould is to reduce the quality issues in mild steel grade 60. In this explicit research, the surface roughness (smoothness) of indigenously produced injection moulds in the local market in Pakistan is investigated. The CNC milling machine (five-axis) is used for the manufacturing of an injection mould, and the Taguchi method of the design of the experiment is applied for parameters optimization. Hence, the overall process is assisted in balancing the milling machine parameters to trim down the surface roughness issue in mild steel moulds and increase their sustainability. The spindle speed (rpm), the depth of cut (mm), and the feed rate (mm/rev) are considered as input variables for process optimization, and the experiments are performed on mild steel grade 60. It is deduced that the combination of a spindle speed of 800 rpm, feed rate of 10 mm/rev and depth of cut of 0.5 mm is the best case in case of minimum surface roughness, which leads to sustainable products. It is also deduced from ANOVA, that the spindle speed is a factor that affects the surface roughness of mild steel products, while the feed rate turns out to be insignificant.

In this article, the results of preliminary experimental studies related to a fast, non-contact assessment of the AISI 316L stainless austenitic steel surfaces after electrochemical polishing in a magnetic field have been presented. The experiments were realized with the use of a modified angle-resolved scattering (ARS) method based on the analysis of angular distribution of the scattered light intensity. Digital images of such distribution were acquired for selected areas of examined samples—base surface and surface after magnetoelectropolishing (MEP) process. Parametric analysis oriented toward the calculation of selected key geo- and photometric parameters carried out in Image Pro®-Plus software allowed for characterization of the surface conditions of the assessed samples in terms of their scattering properties. The obtained experimental results confirmed the usefulness of the ARS method used in the presented studies as well as the possibility of its practical use (after appropriate modifications) on a wider scale, especially in industrial applications.

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

The development of modern jet engines would not be possible without dynamically developed nickel−chromium-based superalloys, such as INCONEL® The effective abrasive machining of above materials brings with it many problems and challenges, such as intensive clogging of the grinding wheel active surface (GWAS). This extremely unfavorable effect causes a reduction in the cutting ability of the abrasive tool as well as increase to grinding forces and friction in the whole process. The authors of this work demonstrate that introduction of a synthetic organosilicon polymer-based impregnating substance to the GWAS can significantly improve the effects of carrying out the abrasive process of hard-to-cut materials. Experimental studies were carried out on a set of a silicon-treated small-sized sol−gel alumina 1-35×10×10-SG/F46G10VTO grinding wheels. The set contained abrasive tools after the internal cylindrical grinding process of INCONEL® alloy 600 rings and reference abrasive tools. The condition of the GWAS after the impregnation process was studied, including imaging and measurements of its microgeometry using confocal laser scanning microscopy (CLSM), microanalysis of its elemental distribution using energy dispersive X-ray fluorescence (EDXRF), and the influence of impregnation process on the grinding temperature using infrared thermography (IRT). The obtained results confirmed the correctness of introduction of the impregnating substance into the grinding wheel structure, and it was possible to obtain an abrasive tool with a recommended characteristic. The main favorable features of treated grinding wheel concerning the reduction of adhesion between the GWAS and grinding process products (limitation of the clogging phenomenon) as well as reduction of friction in the grinding process, which has a positive effect on the thermal conditions in the grinding zone.

This work demonstrates that molybdenum disulfide can be successfully used as an impregnating substance that is introduced in the abrasive tool structure for improving its cutting properties and favorably affecting the effects of the abrasive process. For the experimental studies, a set of MoS2-treated small-sized grinding wheels with a technical designation 1-35×10×10×109A5X60L10VE0 PI-50 before and after the reciprocating internal cylindrical grinding process of rings made from INCONEL® alloy 718 was prepared. The condition of grinding wheel active surface was analyzed using an advanced observation measurement system based on stylus/optical profilometry, as well as confocal and electron microscopy. The obtained results confirmed the correctness of introduction of the impregnating substance into the grinding wheel structure, and it was possible to obtain an abrasive tool with a given characteristic.

A novel design of a universal form tool is presented for machining complex planar and axisymmetric surfaces. The geometric and kinematic flexibility (GKF) of this tool type means that it can machine a wide range of profiles using a single setup. The operating principle of this multi-insert tool is its combination of single cutting edges, each of which form the individual details of the machined surface. The structure and application of both turning and milling multi-insert tools with combinations of single edges are described in the paper. Complex shapes and forms can be machined by changing the cutting-edge positions. The number of combinations of a given tool set can be determined by using the relations detailed in this study. Both turning and milling tools are utilized in a unique tool holder that clamps the inserts into position and allows their adjustment that is facilitated by special prepared analytical software to the desired object profiles. It is possible to use these tools in machining both wood and aluminum alloys. Finally, the design of the multi-insert tools for turning and milling and its functional features were experimentally verified with positive results.

In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter’s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.