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The influence of textured patterns on polycrystalline diamond (PCD) tool inserts during dry turning of Ti-6Al-4V was investigated. Four different texture patterns were created on the rake face of the insert by laser irradiation. The results showed that the textured tools improved the machinability of Ti-6Al-4V. The cutting force was less and the surface finish improved, while tool wear was minimized. The enhancement in machining performance was attributed to shortening of contact length, increased shear angle, better anti-adhesion, debris removal, and lower vibration. The typical serrations on the chip surface were brought down. The machined surface displayed minimal material transfer and feed mark ridges. The diagonal pattern (DP) tool provided the best machining characteristics (an improvement of 34% in cutting force and 37% in surface finish at 150 m/min), when compared to other textured patterns considered. [ABSTRACT FROM AUTHOR]

Micro electrical discharge machining (μ-EDM) stands out as a widely employed non-traditional machining process with diverse applications in microfluidics, electronics, medical, aeronautics, and the automotive industry. This study is dedicated to the exploration of the single spark μ-EDM process utilizing a titanium tool electrode fabricated using a μm-turning process. Intriguingly, it is established that achieving a single spark necessitates a minimum tool diameter of 8 μm. Further, the effect of critical parameters such as voltage and capacitance on material removal rate (MRR), tool wear rate (TWR), as well as the shape and size of the resultant craters is investigated. A 7 μm tool electrode when subjected to a voltage of 100 V and a capacitance of 33 pF manifests a remarkably refined surface finish, demonstrating controlled erosion on the workpiece surface. The study also indicates that MRR and TWR vary exponentially with supplied discharge energy. This comprehensive analysis not only identifies optimal conditions for μm turning during tool fabrication but also elucidates the prerequisites for generating a controlled single spark during workpiece erosion. In essence, the findings presented here contribute valuable insights to the advancement of precision μ-EDM processes. [ABSTRACT FROM AUTHOR]

This study presents an unconventional flow processing technique that uses glass microspheres instead of abrasives. The fundamentals of flow machining with glass microspheres, including microsphere flow burnishing (MFB) and the conditions necessary for cutting or plastic deformation of a surface to occur during MFB, are determined. Details on such conditions are lacking, and therefore, this study attempts to define these conditions. To this end, MFB was experimentally confirmed to be a burnishing process, and the effects of MFB and abrasive flow machining (AFM) on 2017A aluminum alloy were compared. Both processes achieved a surface roughness (Sa) of < 0.5 μm, while MFB yielded higher values of surface microhardness and compressive stress. The effects of basic process parameters on the MFB and AFM results (workpiece weight loss and Sa) were compared experimentally, and the corresponding mathematical models were established. Utilizing these relationships, MFB parameters can be selected so as to obtain the most favorable surface layer for the expected operating conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of high-speed machining with heat-assisted machining (HAM) on the machinability of heat-treated SKD61 steel, utilizing alloy cutting tools. The study encompasses microstructure analysis, mechanical properties evaluation, chip color examination, surface roughness assessment and cutting force measurement during the machining process. Experiments are conducted under controlled conditions with advanced equipment. Findings indicate that at 200°C and 350°C, SKD61 steel's microstructure remains unchanged, while at 500°C, there's a superficial alteration of approximately 0.05 mm thickness. Material hardness remains consistent across temperatures. Unique chip color variations serve as temperature indicators. Cutting force consistently decreases with rising temperature, highlighting the intricate relationship between temperature, material behavior and cutting mechanics. Surface roughness improves with higher temperatures, emphasizing the potential benefits of HAM in precision manufacturing. [ABSTRACT FROM AUTHOR]

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining (μ EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in μ EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by μ EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the μ EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160 V, a capacitance of 10,000 pF and a tool rotation of 600 RPM, the research contributes valuable insights into the intricate realm of μ EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation. [ABSTRACT FROM AUTHOR]

This study investigates the surface topography of microfinishing abrasive films and their machining capability on the Nimonic 80A superalloy, a high-performance nickel-based alloy commonly used in aerospace and gas turbine engine applications. Surface analysis was conducted on three abrasive films with nominal grain sizes of 30, 15, and 9 μm, exploring wear patterns, contact frequency, and distribution. To assess the distribution of grain apexes, Voronoi cells were employed. Results revealed distinct wear mechanisms, including torn abrasive grains and cracked bond surfaces, highlighting the importance of efficient chip removal mechanisms in microfinishing processes. Larger grain sizes exhibited fewer contacts with the workpiece but provided more storage space for machining products, while smaller grain sizes facilitated smoother surface finishes. The research demonstrated the effectiveness of microfinishing abrasive films in reducing surface irregularities. Additionally, surface analysis of worn abrasive tools provided insights into wear mechanisms and chip formation, with the segmentation of microchips contributing to efficient chip removal. These findings underscore the significance of selecting appropriate abrasive films and implementing effective chip removal mechanisms to optimize microfinishing processes and improve surface finishing quality in advanced material machining applications. It is worth emphasizing that no prior research has investigated the microfinishing of components crafted from Nimonic 80A utilizing abrasive films, rendering this study truly unique in its contribution to the field. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is a rapidly evolving method in modern industry that manufactures highly complex components. The physical properties of a tool electrode material are significant factors in determining the effectiveness of the process, as well as the characteristics of the machined surfaces. The current trend of implementing graphite tool electrodes in manufacturing processes is observed. Innovative material engineering solutions enable graphite production with miniaturized grain size. However, the correlation between the graphite electrode grain size and the mechanism of the process removal in the EDM is a challenge for its widespread implementation in the industry. This research introduces a new method to evaluate the impact of the graphite electrode grain size and machining parameters on the material removal effectiveness, relative tool wear rate, and surface roughness (Ra) of Hastelloy C-22 following EDM with negative polarity. The study utilized new graphite materials with a grain size of 1 µm (POCO AF-5) and 10 µm (POCO EDM-180). An assessment of the impact of the EDM process parameters on the technological parameters and the development of the surface roughness was carried out. Electrical discharge machining with fine-grained graphite electrodes increases process efficiency and reduces tool wear. Graphite grains detached from the tool electrode affect the stability of electrical discharges and the efficiency of the process. Based on the experimental results, mathematical models were developed, enabling the prediction of machining effects to advance state-of-the-art manufacturing processes. The obtained mathematical models can be implemented in modern industrial EDM machines as guidelines for selecting adequate machining parameters depending on the desired process efficiency, tool wear rate, and surface roughness for advanced materials. [ABSTRACT FROM AUTHOR]

Machining nickel-based super alloys such as Inconel 718 generates a high thermal load induced via friction and plastic deformation, causing these alloys to be among most difficult-to-cut materials. Localized heat generation occurring in machining induces high temperature gradients. Experimental techniques for determining cutting tool temperature are challenging due to the small dimensions of the heat source and the chips produced, making it difficult to observe the tool–chip interface. Therefore, theoretical analysis of cutting temperatures is crucial for understanding heat generation and temperature distribution during cutting operations. Periodic heating and cooling occurring during cutting and interruption, respectively, are modeled using a hybrid analytical and finite element (FE) transient thermal model. In addition to identifying a transition distance associated with initial period of chip formation (IPCF) from apparent coefficient of friction results using a sigmoid function, the transition temperature is also identified using the thermal model. The model is validated experimentally by measuring the tool–chip interface temperature using a two-color pyrometer at a specific cutting distance. Due to the cyclic behavior in interrupted cutting, where a steady-state condition may or may not be achieved, transient thermal modeling is required in this case. Input parameters required to identify the heat flux for the transient thermal model are obtained experimentally and the definitions of heat-flux-reducing factors along the cutting path are associated with interruptions and the repeating IPCF. The thermal model consists of two main parts: one is related to identifying the heat flux, and the other part involves the determination of the temperature field within the tool using a partial differential equation (PDE) solved numerically via a 2D finite element method. [ABSTRACT FROM AUTHOR]

Highlights Integrating fiber reinforcement plastics (FRP) materials into the industry plays a key role especially for pipes. However, due to the production methods of CFRP pipes, surface finishing processes are inevitable at the end of production. Despite the widespread use of CFRP materials, the predominant focus in the literature has been on their mechanical performance. This study aims to contribute to the limited research on the machinability of CFRP materials. In this context, the turning machining process for CFRP pipes was experimentally investigated in the present study. For this purpose, carbon fiber pipes with an inner diameter of 30 mm and an outer diameter of 60 mm were produced and subjected to computer numerical control (CNC) turning. First, a cylindrical aluminum pipe is used as a mold to manufacture CFRP pipes. Unidirectional (UD) carbon fabrics were wrapped on these aluminum molds. Shrink tape was used to enhance the surface smoothness and required pressure to prevent delaminating of the end product during the process. UD carbon fabrics are wrapped on to the mold at the selected angles (0°, 45°, and 90°) and the CFRP pipe specimens were manufactured using epoxy matrix. Pipes were processed on CNC lathe at 120, 160, 200 rev/min speeds and f 0.4 mm/rev feed rate. The surfaces of the machined specimens were measured with a microscope and a surface roughness device. On the other hand, wear on the tools was observed after the process. CFRP pipes were produced with different fibre orientations, i.e., 0°, 45°, and 90°. All CFRP pipes are made under equal conditions and cured at 80°. Turning of CFRP pipes was examined under dry environments. Performance criteria: surface roughness, temperature, tool wear, and chip formation were explored. 45° fibre angles CFRP pipes showed the best machinability performance. [ABSTRACT FROM AUTHOR]

Since its introduction in 2014, laser-induced graphene (LIG) from commercial polymers has been gaining interests in both academic and industrial sectors. This can be clearly seen from its mass adoption in various fields ranging from energy storage and sensing platforms to biomedical applications. LIG is a 3-dimensional, nanoporous graphene structure with highly tuneable electrical, physical, and chemical properties. LIG can be easily produced by single-step laser scribing at normal room temperature and pressure using easily accessible commercial level laser machines and materials. With the increasing demand for novel wearable devices for biomedical applications, LIG on flexible substrates can readily serve as a technological platform to be further developed for biomedical applications such as point-of-care (POC) testing and wearable devices for healthcare monitoring system. This review will provide a comprehensive grounding on LIG from its inception and fabrication mechanism to the characterization of its key functional properties. The exploration of biomedicals applications in the form of wearable and point-of-care devices will then be presented. Issue of health risk from accidental exposure to LIG will be covered. Then LIG-based wearable devices will be compared to devices of different materials. Finally, we discuss the implementation of Internet of Medical Things (IoMT) to wearable devices and explore and speculate on its potentials and challenges. [ABSTRACT FROM AUTHOR]

This paper presents the methodology of measuring chip temperature in the cutting zone in the rough milling of magnesium alloys. Infrared measurements are taken to determine the effect of variable cutting speed, feed per tooth, and depth of cut on the maximum temperature of chips. Thermal images of chip temperature for a generated collective frame and corresponding histograms are presented. Chip temperatures are presented in numerical terms as median and average values; maximum and minimum values; range; and standard deviation. Box plots are also shown for selected machining conditions. The problems arising during signal recording with a mean emissivity coefficient ε = 0.13, a value which is dedicated during machining magnesium alloys, are discussed in detail. Chip temperatures obtained in the tests do not exceed approx. 420 °C. Therefore, the dry rough milling process carried out with carbide tools with different blade geometries can be considered safe for a wide range of machining parameters. The proposed methodology of chip temperature measurement and result processing is a new and effective approach to safety assessment in the dry milling of magnesium alloys. [ABSTRACT FROM AUTHOR]

Cryogenic cooling has gathered significant attention in the manufacturing industry. There are inherent difficulties in machining materials that are difficult to machine because of high levels of hardness, abrasiveness, and heat conductivity. Increased tool wear, diminished surface finish, and reduced machining efficiency are the results of these problems, and traditional cooling solutions are insufficient to resolve them. The application of cryogenic cooling involves the use of extremely low temperatures, typically achieved by employing liquid nitrogen or other cryogenic fluids. This study reviews the current state of cryogenic cooling technology and its use in machining difficult-to-machine materials. In addition, this review encompasses a thorough examination of cryogenic cooling techniques, including their principles, mechanisms, and effects on machining performance. The recent literature was used to discuss difficult-to-machine materials and their machining properties. The role of cryogenic cooling in machining difficult materials was then discussed. Finally, the latest technologies and methods involved in cryogenic cooling condition were discussed in detail. The outcome demonstrated that the exploration of cryogenic cooling methods has gained prominence in the manufacturing industry due to their potential to address challenges associated with the machining of exotic alloys. [ABSTRACT FROM AUTHOR]

Modular hobs are tools with very complex geometry. Regardless of the material of the gear wheels, they determine the accuracy of the gears made in the hobbing machining process. Gears are made of various, often innovative materials depending on the requirements. Sometimes, the materials are characterised by very high hardness (over 65 HRC). The mathematical basis for describing the faces of a hob presented in the article allows for modifying the rack profile shaping the gear wheel's teeth. The model's universality makes it possible to perform numerical simulations of the influence of individual parameters of the hob creation process (geometry of the grinding wheels and their setting in the shaping process) on the profile of the rake and flank surfaces. The cutting edge (rack edge) is the locus of points belonging to both of these surfaces and thus directly impacts the accuracy of the gear wheel that is shaped in the hobbing process. The article summarises the authors' long-term cooperation with the industry, resulting in a series of articles devoted to hobs. The issues presented in the article are significant to the machinery industry and hob manufacturers. [ABSTRACT FROM AUTHOR]

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool. [ABSTRACT FROM AUTHOR]

Natural fiber composites are gaining interest in many industries, particularly the automotive and aerospace industries. One of the critical secondary activities required to produce a near‐perfectly shaped product is machining. The majority of product components had to be assembled before they could be used. This article reviews the various conventional and non‐conventional machining processes and its influential parameters investigated by various researchers. Non‐traditional machining is now proving to be a better choice than traditional machining. Non‐traditional machining has several advantages, including the absence of thrust force, chatter or vibration, minimal tool wear, and lower frictional heat. Delamination is the major issue occurring in the conventional machining processes like drilling. Researchers are more focused on the non‐conventional machining processes like abrasive water jet machining. Joining of natural fiber reinforced composites is another aspect discussed in this article. Adhesive joining is the most preferable joining method in the natural composites. The joining technique is also influenced by the intricacy of the materials to be bonded. Microwave joining and induction joining processes are the new methods of natural composites joining. This review also covers the various joining processes in the natural reinforced composite materials. [ABSTRACT FROM AUTHOR]

Polycrystalline diamond (PCD) is an excellent material for machining micro-tools due to its high hardness and wear resistance. However, the diameter of micro-milling tools is generally required to be less than 500 μm. When preparing micro-milling tools with a large aspect ratio, the processing load introduced by traditional grinding methods is easy to cause tool fracture. Therefore, a composite structure of carbide tool holder and diamond tool head is proposed in this paper. A technique of picosecond laser machining PCD micro-milling tool was studied, and the overall manufacturing process of tool blank preparation, nanosecond laser roughing and picosecond laser finishing was formed. A two-dimensional ablation mathematical model for tangential machining of PCD with a multi-pulse picosecond laser was established, and experiments verified the reliability of the mathematical model. In addition, the effects of picosecond laser pulse pitch, pulse energy, and track spacing on the surface quality of PCD materials with different particle sizes and cutting edge radius were studied by orthogonal experiments. The results showed that after picosecond laser processing, the surface roughness (Sa) of PCD with a particle size of 2 μm was the best, which can reach 0.33 μm, and the cutting edge radius of PCD micro-milling tool with a particle size of 25 μm was the smallest, which can get 1.67 μm. Using the optimized picosecond laser parameters, the high-sharpness single-edge micro-milling cutters with different particle sizes PCD with a diameter of 100 μm, aspect ratio greater than 3, and a cutting edge radius less than 3 μm were successfully fabricated. [ABSTRACT FROM AUTHOR]

This study aims to investigate the application and machining characteristics of brittle conductive ceramics using ultrasonic vibration-assisted powder-mixed electrical discharge machining (UV-PMEDM) technology. Titanium nitride (TiN) ceramics are widely used in aerospace, biomedicine, and electronics due to their excellent properties. However, the conventional mechanical machining methods of these materials result in severe tool wear, high costs, and low efficiency. Although electrical discharge machining (EDM) can overcome some limitations, it typically exhibits a low material removal rate and poor surface quality. This study utilizes the UV-PMEDM technology to process TiN ceramics. Single-factor experiments are conducted to analyze the effects of processing parameters, including ultrasonic amplitude, powder concentration, pulse-on time , and pulse-off time , on the material removal rate, surface roughness, and relative electrode wear rate, which indicate the EDM processing performance. With the assistance of ultrasonic vibration and mixed powders, the material removal rate of TiN ceramics using UV-PMEDM reaches 0.50 mm3/min, while maintaining a relative electrode wear rate below 1 % and surface roughness lower than 4.2 μm. The synergistic combination of ultrasonic vibration and mixed powders significantly enhances the processing efficiency and reduces surface roughness of TiN ceramics. These findings contribute to a better understanding of machining strategies for brittle conductive ceramics. [ABSTRACT FROM AUTHOR]

As a promising micro/nanofabrication method, electrical-assisted nanomachining has obtained substantial attention due to its high material removal rate and attainable superior surface quality. In this study, a rectangular wave electrical signal was applied for nanomachining by a customized tungsten tip. Owing to the coupling effect between the electric field and mechanical force, the cutting depth of the machined grooves can be expanded. In electrical-assisted groove processing, a depth of 270 nm and an aspect ratio of 0.6 on the copper sample can be achieved. The influence of operation parameters including applied voltage, frequency, duty ratio, normal force and cutting speed on the machining performance was investigated in terms of the groove depth, width, aspect ratio, and surface roughness. The potential machining mechanisms should be a combination of electric field force, nanoscale electric discharge, electric contact thermal effects, possible annealing behavior, and scraping and plowing actions induced by mechanical forces. Highlights: Customized tungsten probes are fabricated by electrochemical etching as the machining tools for electrical-assisted tip-based nanomanufacturing. Electrical-assisted machining is implemented by applying a rectangular wave electrical signal during scratching. The effects of the operation parameters including voltage, duty ratio, and frequency on the machining performance are studied in detail. Experiments reveal the potential mechanisms underlying electrical-assisted machining is a combination of the electric field force, nanoscale electric discharge, electric contact thermal effects, possible annealing behavior, and scraping and plowing actions induced by mechanical forces. [ABSTRACT FROM AUTHOR]

The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine's geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model's accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process. [ABSTRACT FROM AUTHOR]

Metal machining workshops generate a lot of metal chips, which pose both health and disposal challenges. Herein, for the first time, we demonstrate a novel process of reusing lathe-turning steel chips to manufacture high-compression strength Al alloy/steel composites. The process involves melting aluminium alloy onto the steel chips under vacuum and argon gas conditions. Clean and dried steel chips are packed inside a mould with a top layer of aluminium alloy pieces at three different quantity ratios of 80%Al/20% steel, 20%Al/80% steel and 50%Al/50% steel. The steel mould is then heated in a furnace at 650–700 °C until the aluminium metal melts. After a predetermined time of melting, argon gas is bubbled into the top layer (molten aluminium) of the mould while vacuum pumping is activated on the lower side of the mould (steel chips) for 5 min. The sample is allowed to cool within the mould. The composite containing 50%Al/50% steel has the highest mechanical properties with compression strength twice that of the other two samples. The microstructural observations revealed a good bonding between the steel chips and the Al alloy matrix. The study further presents the machinability of the samples, and it is seen that the composites behave like the aluminium matrix alloy during a turning operation and therefore do not require a special machining operation. It is shown that through this method, machining chips of steel can be recycled into high-compression strength metal–metal composites. These materials have the potential for high-compression strength applications such as machine supports and high-impact cushions. [ABSTRACT FROM AUTHOR]

In machining, the heat generated during the process deforms the components and the final shape might not meet specified tolerances. There is therefore a need for a compensation strategy which requires knowledge of the workpiece temperature field and the associated thermal distortions. In this work, a methodology is presented for the determination of the heat load for indexable insert drilling of AISI 4140. Compared to previous research, this work has introduced a varying heat load. The heat load is extracted from thermo-mechanical finite element simulations for different nominal chip thicknesses and cutting speeds using the coupled Eulerian-Lagrangian formulation of an orthogonal turning process. The heat load is then transferred to a simplified 2D axisymmetric heat transfer model where the in-process temperature field in the workpiece is predicted. To verify the methodology, the predicted temperatures are compared to the experimentally measured temperatures for various feed rates. It is found that the model is capable of predicting the workpiece temperatures reasonably well. However, the methodology needs to be further explored to validate its applicability. [ABSTRACT FROM AUTHOR]

Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation. [ABSTRACT FROM AUTHOR]

With the emergence of the Industrial Internet of Things and Industry 4.0, industrial automation has grown as an important vertical in recent years. Smart manufacturing techniques are now becoming essential to keep up with the global industrial competition. Decreasing machine's downtime and increasing tool life are crucial factors in reducing machining process costs. Therefore, introducing complete process automation utilizing an intelligent automation system can enhance the throughput of manufacturing processes. To achieve this, intelligent manufacturing systems can be designed to recognize materials they interact with and autonomously decide what actions to take whenever needed. This paper aims to present a generalized approach for fully automated machining processes to develop an intelligent manufacturing system. As an objective to accomplish this, the presence of workpiece material is automatically detected and identified in the proposed system using a convolutional neural network (CNN) based machine learning (ML) algorithm. Furthermore, the computer numerical control (CNC) lathe's machining toolpath is automatically generated based on workpiece images for a surface finishing operation. Machining process parameters (spindle speed and feed rate) are also autonomously controlled, thus enabling full machining process automation. The implemented system introduces cognitive abilities into a machining system, creating an intelligent manufacturing ecosystem. The improvised system is capable of identifying various materials and generating toolpaths based on the type of workpieces. The accuracy and robustness of the system are also validated with different experimental setups. The presented results demonstrate that the proposed approach can be applied in manufacturing systems without the need for significant modification. [ABSTRACT FROM AUTHOR]

This work aims to study the cutting behavior of biocomposites under different controlled hygrothermal conditions. This investigation choice is motivated by the fact that natural plant fibers such as flax are characterized by their hydrophilicity which makes them able to absorb water from a humid environment. This absorption ability is intensified by increasing the conditioning temperature. The moisture diffusion process affects considerably the mechanical properties of the resulting composite, which causes many issues during the machining operations. In this paper, moisture diffusion, chip form, cutting and thrust forces, and scanning electron microscope (SEM) observations are considered to explore the cutting behavior of flax fiber-reinforced polylactic acid (PLA) depending on the hygrothermal conditioning time. Results reveal that moisture content in the biocomposite is significantly influenced by the conditioning temperature and the fiber orientation. Moisture content and fiber orientation affect both the curling behavior of the removed chip as well as the tool/chip interaction in terms of friction. The machinability of flax fiber-reinforced PLA biocomposites depending on hygrothermal conditioning time is then investigated using SEM analysis in addition to analytical modeling. An analysis of variance is used finally to quantify the observed results. [ABSTRACT FROM AUTHOR]

This study explains the development of a manufacturing process that is machinable for a ceramic product. The initial process involves manufacturing a ceramic slurry, which is a mixture of high-weight percentage ceramic powders, and a polymeric binder. The ceramic slurry is then pressed to shape a solid body knwon as the green part, which can be structured using conventional machining tools. The structured green part undergoes a debinding process, the resulting body is referred to as the brown part. Finally, sintering is performed to merge the peripheral ceramic powders, and the white part is obtained. The white part exhibited approximately 18 % isotropic shrinkage in dimension and 9.4 % weight reduction compared with the initial green part. The durability of each part is determined via impact-resistant and microindentation tests. The fracture-resistant energy of the white part is determined as 0.673 J, which is an increase of approximately 500 % compared with the values of other parts. Furthermore, the elastic modulus of the white part is determined to be 14.7 GPa, representing an increase of approximately 270 %. In conclusion, we employed an innovative manufacturing process for ceramic products to address the challenges in conventional methods. [ABSTRACT FROM AUTHOR]

Increasing product diversity, rising performance and reliability demands, and industry competitiveness are some of the many reasons that increase the need of more complex product designs in almost all sectors. The complexity of parts increases with their geometrical features to be designed and manufactured. Researchers agreed that it can be qualitatively evaluated and expressed with terms like low, medium, high, and very high. However, it might be evaluated differently, depending on the designer's considerations, domain and experience. Quantitative evaluation of a design complexity is, therefore, indispensable and expedites the decision-making about the selection of the manufacturing process. However, having a well-defined and unambiguous metric for quantitative evaluation is challenging. Most of existing metrics are not objective and are only valid for their specific applications. This paper presents a novel, unambiguous, and generalized approach for shape complexity evaluation. The developed metric enables determining if the selected part should be produced by conventional methods such as machining, or by non-conventional methods such as additive manufacturing. In order to ensure its objectivity, only geometrical features have been considered. The metric was tested through 25 different part designs of varying complexity. The investigations showed an accordance between the qualitatively evaluated shape and the calculated complexity factor. Also, the comparison of the results with other metrics showed the weakness of the latter and the efficiency and reliability of our metric. The results have been also validated by 50 experts from 23 countries. Based on these results, a threshold between machining and additive manufacturing is fixed allowing an easier decision-making. [ABSTRACT FROM AUTHOR]

The generation of mechanical characteristics in workpiece subsurface layers as a result of the cutting process has a predominant influence on the performance properties of machined parts. The effect of the end milling process on the mechanical characteristics of the machined subsurface layers was evaluated using nondestructive methods: instrumented nanoindentation and sclerometry (scratching). In this paper, the influence of one of the common processes of materials processing by cutting—the process of end tool milling—on the generation of mechanical characteristics of workpiece machined subsurface layers is studied. The effect of the end milling process on the character of mechanical property formation was evaluated through the coincidence of the cutting process energy characteristics with the mechanical characteristics of the machined subsurface layers. The total cutting power and cutting work in the tertiary cutting zone area were used as energy characteristics of the end milling process. The modes of the end milling process are considered as the main parameters affecting these energy characteristics. The mechanical characteristics of the workpiece machined subsurface layers were the microhardness of the subsurface layers and the total work of indenter penetration, determined by instrumental nanoindentation, and the maximum depth of indenter penetration, determined by sclerometry. Titanium alloy Ti10V2Fe3Al (Ti-1023) was used as the machining material. Based on the evaluation of the coincidence of the cutting process energy characteristics with the specified mechanical characteristics of the machined subsurface layers, the milling mode effect of the studied titanium alloy, in particular the cutter feed and cutting speed, on the generated mechanical characteristics was established. [ABSTRACT FROM AUTHOR]

Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods. [ABSTRACT FROM AUTHOR]

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system. [ABSTRACT FROM AUTHOR]

Nanofabrication of two-dimensional materials through mechanical machining is normally influenced by not only process parameters such as load and velocity but also intrinsic properties such as strength and thickness. Herein, we examined the effects of graphene oxide (GO) film thickness on nanofabrication on the plane surfaces and at the step edges using scanning probe microscope lithography. The material removal of GO initiates at the load above a critical value, which strongly depends on film thickness and locations. With the increase in film thickness, the critical load decreases monotonically on the plane surfaces but increases gradually at the step edges. Further, the critical load for the GO monolayer at the step edges is at least 25 times lower than that on the plane surfaces, and the gap decreases to around 3 times when GO thickness increases to four layers. Then, mechanical nanofabrication initiating from the GO step edge allows producing various nanopatterns under extremely low loads around 1 nN. Finally, the GO nanostructures are deoxidized by annealing at 800 °C in high-purity argon to restore their highly functionalized conjugated structures, which are supported by X-ray diffraction and Raman characterizations. This work provides a novel approach to fabricating graphene-like nanostructures by deoxidizing GO after nanofabrication, which holds significant potential for applications in graphene-based devices. Highlights: The minimum load for GO removal initiation presents negative layer dependence on the plane surface but positive at the step edge. The minimum load for GO monolayer nanofabrication at the step edge is at least 25 times lower than that at the plane surface. GO nanopatterns can be fabricated under a low load and further deoxidized by high-temperature annealing. [ABSTRACT FROM AUTHOR]

The purpose of the article is to analyse the manufacturing factors that affect the cost of manufacturing a part. Nowadays the cost of equipment used for metal 3D printing, as well as metal powders, are still very expensive. The cost of additive manufacturing of metal parts is mainly dependent on the volume of printing, therefore, reducing the volume of printing can lead to a significant reduction in the cost of manufacturing the part. Manufacturing of a metal shell, the cavity of which is filled with a cheap metal polymer, can be an excellent alternative to an all-metal part. The article provides a method for calculating the cost of manufacturing a lever part using various technologies: subtractive, additive, and composite part manufacturing technology. According to the calculated data, the dependence of the cost of manufacturing a part on piece time has been built, which can be interpreted as the amount of machining. The constructed linear dependence can give an idea of the effectiveness of the application of a particular technology for obtaining a part. In addition, non-economic factors affecting the possibility of using various technological processes for manufacturing a part are described. [ABSTRACT FROM AUTHOR]

Nylon-CF is an important thermoplastic 3D printable material that can be used to develop high-quality functional parts with slight modifications in low-cost machines like material extrusion (MEX) technology. This paper investigates the tensile behavior of Nylon-CF samples produced by material extrusion. The influence of seven process parameters is studied on response characteristics. The Taguchi design is used to investigate the effect of process parameters on Young's modulus and yield strength of specimens fabricated as per ASTM D-638. A two-level fractional factorial design supplements the L27 design results. The results show that the layer height, infill pattern, density, and raster angle significantly affect the tensile behavior. The apparent Young's modulus is also significantly affected by the interaction between layer height, infill pattern, and infill density. [ABSTRACT FROM AUTHOR]

The axially-split phase spherical bearingless flywheel machine (ASP-SBFM) operates mostly in fast charging and discharging conditions. The unique topology design and high power density of the ASP-SBFM can lead to complex machine losses and result in high-temperature rise. This article employs magneto-thermal coupling to perform an accurate analysis of the temperature field distribution of the ASP-SBFM. First, as the heat source for machine temperature rise, the ASP-SBFM losses are analyzed in detail. And the theoretical calculation results agree well with the FEA results. Then, the magneto-thermal coupling method is conducted to obtain the accurate temperature distribution of ASP-SBFM, where both natural cooling and high vacuum conditions are considered. The temperature distribution results can provide a reference for machine heat dissipation design, topology optimization, and machine body material selection. [ABSTRACT FROM AUTHOR]