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Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples. [ABSTRACT FROM AUTHOR]

Wire electrical discharge machining (WEDM) has become the most important advanced production technology compared to other methods. The major goal of the current experiment is to use WEDM to ascertain the machining characteristics of the aluminum alloy EN AW-6082 T6, which has better corrosion resistance. The trials were planned using the design of experiments—response surface methodology. In total, 32 sWEDM experiments with molybdenum wire were conducted. The methods were used to reduce surface roughness (SR) and dimensional deviation (DD) by influencing machining parameters such as pulse on time (Ton), pulse off time (Toff), spark gap voltage (GV), peak current, and wire tension to help industrialists manufacture cost-effective industrial components. After machining, the significance of each parameter was determined using an analysis of variance (ANOVA), which revealed that for SR, Ton is the influencing factor and for DD, GV is the dominant factor. The measured output was optimized using desirability function analysis. The confirmatory tests were carried out, which indicate that the desired consistency with individual parameters was achieved, ensuring the validity of this investigation. [ABSTRACT FROM AUTHOR]

When machining high-speed steels (HSS) with micro-wire electrical discharge machining (micro-WEDM), high surface quality is achieved as standard. The value of the roughness parameter Ra is less than 0.2 μm. However, the problem is the performance of the electroerosion process (MRR), which is low. This problem is related to the mechanical and physical properties of the HSS in combination with the setting of the main technological parameters (MTP). The proposed solution to eliminate this problem relies on the selection of proper procedures for the determination of optimization criteria in relation to Ra and MTP, with the inclusion of properties of the machined material. The solution consisted in the identification of four significant physical (ρ, κ) and mechanical (Rm, HRC) indicators of HSS properties, on the basis of which a suitable combination of the process output parameters Ra and MRR can be determined through established mathematical regression models using simulation and optimization. In the next step, the proper values of the MTP output process parameter settings, which correspond to the optimized output parameters Ra and MRR during machining of HSS by micro-WEDM technology, were then obtained by the same approach. [ABSTRACT FROM AUTHOR]

The performance assessment of additive manufacturing (AM) printers is still a challenge since no dedicated standard exists. This paper proposes a systematic method for evaluating the dimensional and geometrical performance of such machines using the concept of machine performance. The method was applied to an Ultimaker 2+ printer producing parts with polylactic acid (PLA). The X and Y axes of the printer were the most performant and led to narrower potential and real tolerance intervals than the Z axis. The proposed systematic framework can be used to assess the performance of any material extrusion printer and its achievable tolerance intervals. [ABSTRACT FROM AUTHOR]

A coating can be a deciding factor for machining performance of a tool by improving its tool life and wear resistance. Additionally, a variety of elements, including coating thickness, composition ratio, layer sequencing in multilayer coatings, and deposition process, affect the coating's performance. In this study, the properties of single-layer AlTiCrN and AlTiN coatings deposited using PVD high-power impulse magnetron sputtering (HiPIMS) and scalable pulse power plasma (S3p) and multilayer TiN/TiAlN- and AlTiN/TiAlN-coated carbide tools deposited using PVD cathodic arc evaporation (CAE) were investigated. Initially, the coatings were characterized for physical properties. Moreover, the performance of these coatings for dry machining of DSS2205 steel are described with respect to tool wear and tool life. It was observed that the AlTiCrN coating deposited by the HiPIMS technique exhibited excellent properties, including low surface roughness 0.19 µm, coefficient of friction 0.35 µm, higher adhesion strength 110 N, and maximum operating temperature 1150 °C, followed by the S3p AlTiN coating with surface roughness 0.21 µm, adhesion strength 107 N, coefficient of friction 0.34 µm, and 850 °C maximum operating temperature. The TiN/TiAlN coating exhibited poor properties among all the tools used. Machining performance confirmed the excellent results obtained through characterization for the AlTICrN coating during dry machining of the DSS2205. [ABSTRACT FROM AUTHOR]

The paper provides an analysis of the impact of the values of cutting tool inclination strategies and angles measured in the parallel and perpendicular to feed direction, radial depth of cut and feedrate on the surface roughness. The workpiece was made of the AISI H13 steel, hardness 50 HRC, and was machined using a ball-nosed end mill with CBN edges. The research methodology involved experiments conducted based on the Taguchi orthogonal array, optimization of parameters with the use of Taguchi method and process modelling using neural networks. Thanks to the use of neural networks, the analyses were performed for various levels of machining efficiency, obtained as a result of different radial depths of cut and feedrates. In order to obtain mathematical models welldescribing strongly nonlinear impact of the cutting tool inclination strategies and angles, a separate neural network learned for each tool inclination strategy. The prediction of results was made using a set of neural networks. The analyses and experiments resulted in surfaces with very low Ra parameter of 0.16 mm and mathematical models with a good fit to the experimental data. Values of the cutting tool inclination angle that allow obtaining the surface of specific surface roughness were specified for various levels of machining performance. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced polymer (CFRP) composite has extensive application in several industries, including the automotive and aerospace industries. Fabrication of micro-features in CFRP composite is an important process that is frequently performed for various applications including for micro-products and micro-components. The fabrication of precise micro-features in CFRP composite is difficult to achieve by conventional means, even by additive manufacturing (AM). Thus, in this study, the machinability characteristics of the CFRP composite were experimentally investigated. CFRP composite was fabricated by using the AM technique. The different process parameters of 3D printing (infill density and layer sequence) and micro-EDM (voltage, capacitance, and tool rotation) were considered to investigate their effect on the hole quality and productivity. The optimization of the different parameters chosen for the purpose of the investigation was also carried out. The important process parameters that have an impact on the process were identified and analyzed. The better machining performance of CFRP composite was noticed at higher infill density and alternative fiber arrangement. The capacitance was the most significant parameter among all the EDM parameters to produce good quality micro-holes in CFRP composite. [ABSTRACT FROM AUTHOR]

The machining of Ti-6Al-4V alloys is challenging due to their high strength, poor thermal conductivity, and high chemical reactivity. When used in traditional machining, cryogenic coolants can reduce tool wear, thus extending tool life, improving surface finish, and requiring less power with reduced environmental effects. In this context, this study aimed to perform a machinability analysis of the surface roughness, power consumption, tool wear, and specific energy consumption of a Ti-6Al-4V titanium alloy and to comprehend the performance of dry and cryogenic machining in turning operations. A comprehensive analysis of tool wear and specific cutting energy (SCE) under dry and cryogenic machining was conducted. It was found that the machining time under a cryogenic environment was increased by 83% and 39% at 80 and 90 m/min compared to a cutting speed at 100 m/min. The higher cutting speed (100 m/min) in cryogenic environments produced an improved surface finish. Compared to dry machining, the cooling effect of liquid CO2 helped dissipate heat and reduce thermal damage, improving surface finish. The findings revealed that in dry conditions, approximately 5.55%, 26.45%, and 27.61% less power was consumed than in cryogenic conditions at 80, 90, and 100 m/min cutting speeds, respectively. Based on the outcomes of the work, the application of cryogenic cooling can be considered an alternative to dry and flood cooling for improving the machinability of Ti-6Al-4V alloys. [ABSTRACT FROM AUTHOR]

The contact parameters of joint interfaces have a great influence on the dynamic performance of the machine tool, and the dynamic performance determines the machining precision of the machine tool. Therefore, it is important to accurately identify the contact parameters of joint interfaces to improve the machining precision of the machine tool. In this paper, a method of identifying the joint interface stiffness parameters based on a joint simulation in Matlab and Ansys is first proposed, and then stiffness parameters of joint interfaces between the bed and column of the precision grinding machine are identified by applying the method. Finally, the feasibility and high efficiency of the proposed method are proved by comparing the natural frequencies of the simulation and the experiment. In order to further analyze the influence of joint contact parameters on the performance of the joint interfaces, the theoretical model of the normal stiffness of the joint interfaces based on fractal geometry is amended and verified by taking into account the asperity interaction and elastic–plastic deformation. Then, the modified model is extended to a three-dimensional model, which is more in line with the actual situation. Finally, the influencing factors of the joint interface stiffness are analyzed. The conclusions of this paper are of great significance in the dynamic performance analysis and structural design of machine tools. [ABSTRACT FROM AUTHOR]

Minimizing the negative effects of the manufacturing process on the environment, employees, and costs while maintaining machining accuracy has long been a pursuit of the manufacturing industry. Currently, the nanofluid minimum quantity lubrication (NMQL) used in cutting and grinding has been studied as a useful technique for enhancing machinability and empowering sustainability. Previous reviews have concluded the beneficial effects of NMQL on the machining process and the factors affecting them, including nanofluid volume fraction and nanoparticle species. Nevertheless, the summary of the machining mechanism and performance evaluation of NMQL in processing different materials is deficient, which limits preparation of process specifications and popularity in factories. To fill this gap, this paper concentrates on the comprehensive assessment of processability based on tribological, thermal, and machined surface quality aspects for nanofluids. The present work attempts to reveal the mechanism of nanofluids in processing different materials from the viewpoint of nanofluids' physicochemical properties and atomization performance. Firstly, the present study contrasts the distinctions in structure and functional mechanisms between different types of base fluids and nanoparticle molecules, providing a comprehensive and quantitative comparative assessment for the preparation of nanofluids. Secondly, this paper reviews the factors and theoretical models that affect the stability and various thermophysical properties of nanofluids, revealing that nanoparticles endow nanofluids with unique lubrication and heat transfer mechanisms. Finally, the mapping relationship between the parameters of nanofluids and material cutting performance has been analyzed, providing theoretical guidance and technical support for the industrial application and scientific research of nanofluids. [ABSTRACT FROM AUTHOR]

In this research, a comparison between two methods of grinding and WEDM and the chip formation of each form tool was studied through a set of designs of experiments. A multi-functional form tool with different cutting-edge shapes was designed to compare different production methods, and a grinding machine and a five-axis wire-cutting machine were made. The form tools by the wire cutting method were made with three different machining states, rough, semi-finish, and finish. The results of the experimental test showed that the chip formation of the finished surface of the wire cut tool was close to the ground tools. Additionally, the tool life in wear generation was assessed, revealing that the tool generated through the wire-cutting method with three passes exhibited superior performance compared to alternative approaches. Furthermore, employing the wire-cutting technique with high surface finishing yielded optimal outcomes for producing form-cutting tools featuring complex profiles. [ABSTRACT FROM AUTHOR]

In the context of satisfying sustainability requirements nowadays, dry machining is one of the ideal strategies to eliminate the environmental and human health burdens of machining processes. In addition, micro-textured cutting tools are used to improve the performance of dry machining processes. Micro-textures reduce the chip-tool contact length and thus reduce friction and heat, which results in fewer cutting forces and temperature. However, the action of micro-cutting of the bottom side of the chip, which is known as derivative cutting, cuts down the gains of using textured tools, where derivative cutting leads to higher cutting forces, heat, and tool wear. This study aimed to investigate the effects of significant texture design parameters (i.e., micro-groove width) when cutting AISI 1045 steel using different machining parameters (i.e., 75 m/min and 150 m/min of cutting velocity, 0.05 and 0.10 mm/rev of feed). Three different textured cutting tool designs were prepared using the laser texturing technique and then utilized in machining experiments. In addition, the measured machining outputs were forces, power consumption, flank wear, and surface roughness. There were no marks for the derivative cutting when using the textured cutting tool with the narrowest micro-grooves according to the obtained microscopical images after the machining tests. In addition, the textured cutting tool, which included the narrowest micro-grooves, showed better performance compared to the non-textured cutting tool and the other textured tool designs in terms of cutting and feed forces, power consumption, flank tool wear, and surface roughness at the used cutting conditions. This confirmed that the careful optimal design of the micro-textured tools can reduce or eliminate the severity of the derivative cutting, and thus improve the overall machining performance. [ABSTRACT FROM AUTHOR]

Thin-walled parts (TWPs) are widely used in aerospace, and their service performance is significantly affected by the machining accuracy. Multi-pass machining is used to machine the poor stiffness TWPs. However, it is difficult to accurately predict the final machining accuracy due to surface topography error propagation and accumulation in multi-pass machining. Therefore, this paper proposes a multi-pass machining accuracy prediction method for TWPs based on dynamic factors (cutting force and stiffness). First, a flexible cutting force prediction model, which considers the axial errors determined by the initial surface topography and part deflection, is proposed. Second, a position-pass-dependent stiffness (PPDS) model is established considering the position dependence of stiffness and multi-pass machining material removal. Finally, combining the two models above, a multi-pass machining accuracy prediction method based on a genetic algorithm–back propagation (GA-BP) neural network is proposed. Experiments under various conditions are carried out to validate the proposed method. The machining accuracy (flatness as an example) is as high as 90.8% using the method in this paper, while it is only 73.9% when the accumulative error is neglected. The proposed method can significantly improve the performance of machining accuracy prediction by revealing the error propagation mechanism and the effect of dynamic factors between multi-pass machining. Furthermore, this also provides a theoretical basis for process parameter optimization and machining accuracy improvement in TWP machining. [ABSTRACT FROM AUTHOR]

The study was performed to determine the optimum flushing condition for electrical discharge machining (EDM) of functional material titanium VT6 obtained by plasma cladding with a thermal cycle. Copper is used as an electrode tool (ET) to machine functional materials. The optimum flushing flows are analyzed theoretically by using ANSYS CFX 20.1 software which is also validated by an experimental study. It was observed that while machining the functional materials to adepth of 10 mm or more, the turbulence fluid flow dominates when nozzle angles are 45° and 75°, consequently drastically affecting the quality of flushing and the performance of the EDM. For the highest machining performance, the nozzles should be at an angle of 15° relative to the tool axis. The optimum flushing at deep hole EDM process minimizes the occurrence of debris deposition on tool electrodes, thus facilitating stable machining of functional materials. The adequacy of the obtained models was confirmed experimentally. It has been established that EDM of a hole with a depth of 15 mm, an intense accumulation of sludge, is observed in the processing zone. There arebuild-ups exceeding 3 mm in cross-section after EDM. This build-up leads to a short circuit and a reduction in surface quality and productivity. It has been proven that not correct flushing leads to intensive wear of the tool and a change in its geometric shape and, accordingly, to a decrease in the quality of EDM. [ABSTRACT FROM AUTHOR]

With the rapid growth of cloud computing and the creation of large-scale systems such as IoT environments, the failure of machines/devices and, by extension, the systems that rely on them is a major risk to their performance, usability, and the security systems that support them. The need to predict such anomalies in combination with the creation of fault-tolerant systems to manage them is a key factor for the development of safer and more stable systems. In this work, a model consisting of survival analysis, feature analysis/selection, and machine learning was created, in order to predict machine failure. The approach is based on the random survival forest model and an architecture that aims to filter the features that are of major importance to the cause of machine failure. The objectives of this paper are to (1) Create an efficient feature filtering mechanism, by combining different methods of feature importance ranking, that can remove the "noise" from the data and leave only the relevant information. The filtering mechanism uses the RadViz, COX, Rank2D, random survival forest feature ranking, and recursive feature elimination, with each of the methods used to achieve a different understanding of the data. (2) Predict the machine failure with a high degree of accuracy using the RSF model, which is trained with optimal features. The proposed method yields superior performance compared to other similar models, with an impressive C-index accuracy rate of approximately 97%. The consistency of the model's predictions makes it viable in large-scale systems, where it can be used to improve the performance and security of these systems while also lowering their overall cost and longevity. [ABSTRACT FROM AUTHOR]

The rigidity and natural frequency of machine tools considerably influence cutting and generate great forces when the tool is in contact with the workpiece. The poor static rigidity of these Vertical Machining Centre machines can cause deformations and destroy the workpiece. If the natural frequency of the machines is low or close to the commonly used cutting frequency, they vibrate considerably, resulting in poor workpiece surfaces and thus shortening the lifespan of the tool. The main objective of this study was to develop an experimental technique for measuring the effect of machine tool stiffness. The static rigidity of the X-axis was found to be 2.20 kg/μm, while the first-, second-, and third-order natural frequencies were 27.3, 34.4, and 48.3 Hz, respectively. When an external force of 1000 N was applied, the Y-axis motor load was found to be approximately 2740 N-mm. In this study, the finite element method was mainly used to analyze the structure, static force, modal, frequency spectrum, and transient state of machine tools. The results of the static analysis were verified and compared to the experimental results. The analysis model and conditions were modified to ensure that the analysis results were consistent with the experimental results. Multi-body dynamics analyses were conducted by examining the force of each component and casting of the machine tools and the load of the motor during the cutting stroke. Moreover, an external force was applied to simulate the load condition of the motor when the machine tool is cutting to confirm the feed. In this study, we used topology optimization for effective structural optimization designs. The optimal conditions for topology optimization included lightweight structures, which resulted in reduced structural deformation and increased natural frequency. [ABSTRACT FROM AUTHOR]

Titanium alloys are extensively used in various industries due to their excellent corrosion resistance and outstanding mechanical properties. However, titanium alloys are difficult to machine due to their low thermal conductivity and high chemical reactivity with tool materials. In recent years, there has been increasing interest in the use of titanium components produced by additive manufacturing (AM) for a range of high-value applications in aerospace, biomedical, and automotive industries. The machining of additively manufactured titanium alloys presents additional machining challenges as the alloys exhibit unique properties compared to their wrought counterparts, including increased anisotropy, strength, and hardness. The associated higher cutting forces, higher temperatures, accelerated tool wear, and decreased machinability lead to an expensive and unsustainable machining process. The challenges in machining additively manufactured titanium alloys are not comprehensively documented in the literature, and this paper aims to address this limitation. A review is presented on the machining characteristics of titanium alloys produced by different AM techniques, focusing on the effects of anisotropy, porosity, and post-processing treatment of additively manufactured Ti-6Al-4V, the most commonly used AM titanium alloy. The mechanisms resulting in different machining performance and quality are analysed, including the influence of a hybrid manufacturing approach combining AM with conventional methods. Based on the review of the latest developments, a future outlook for machining additively manufactured titanium alloys is presented. [ABSTRACT FROM AUTHOR]

After applying high-frequency vibration of micro-displacement to the tool of the common turning method, it has a significant positive effect on improving the machinability of the machined material, but the high-frequency vibration amplitude will make the tool produce a serious plowing effect and impact effect; it will not only shorten the tool life but also affect the processing force and surface roughness. As one of the main factors affecting the plowing effect of three-drive ultrasonic elliptical vibration-assisted turning, the contribution of tool geometry to the machining performance of three-drive ultrasonic elliptical vibration-assisted turning was investigated. It is considered that the tool nose radius, tool sharp angle, and clearance angle all have an influence on the processing force and surface roughness, and among them, the effect trend of the tool clearance angle on the two is obviously different from the tool nose radius and sharp angle. As one of the main factors affecting the impact effect of three-drive ultrasonic elliptical vibration-assisted turning, the effect trend of workpiece geometry (length-diameter ratio) on the processing force and surface roughness formed by three-drive ultrasonic elliptical vibration-assisted turning was determined. It is found that the processing force fluctuates greatly when the three-drive ultrasonic elliptical vibration-assisted turning is used to process workpieces with large length-diameter ratios, and the average processing force and surface roughness also tend to increase. The study also found that under the same cutting conditions, the machining performance and machining system stability of three-drive ultrasonic elliptical vibration-assisted turning are better than common turning. However, the influence of the workpiece geometry on the surface roughness and processing force of three-drive ultrasonic elliptical vibration-assisted turning is more significant than that of common turning. [ABSTRACT FROM AUTHOR]

The compliant mechanism (CM)-based fast-tool servo (FTS) is used in ultraprecision machining contexts to produce high value products for technically advanced applications. Far too often, the FTS' machined products are expected to be geometrically complex with minimal form tolerance and surface roughness. Since the FTS' enclosing CM is responsible for guiding the cutting motion, its design is of utmost importance in determining the quality of the machined product. The objective of this paper is therefore to review specifically the design and structural related aspects of CM-based FTS that affects its ultraprecision machining performance. After a brief introduction, the fundamentals for designing ultraprecision capable CMs such as flexure hinge modelling, actuator selection and isolation and CM designing are comprehensively explained. In the subsequent section, the various configurations of CM-based FTSs that exist so far and their functionalities are listed. The critical factors which impact the CM-based FTS' ultraprecision machining performance are identified and mitigating measures are provided wherever possible. Before concluding, the research questions that should be investigated for raising the state of the art of CM-based FTSs are presented as food for thought. With this review article, not only can practitioners have a clearer picture of how better to design their CMs for their FTSs, but they can also improve upon existing FTS designs from leading researchers so that products of higher quality than before can be made for the future. [ABSTRACT FROM AUTHOR]

Identification of key geometric errors is an essential prerequisite for improving the machining accuracy of five-axis machine tools. This paper presents a new sensitivity analysis (SA) method to extract key geometric errors, and then to improve the machining performance of machine tools by compensating key geometric error components. Development of geometric error prediction model is involved to obtain geometric error values at arbitrary positions at first. Based on the multi-body system theory and flank milling theory, the machining error model is developed, which considers 37 geometric errors. Then, a new SA method is introduced by taking the machining error model as sensitivity analysis model and taking the geometric errors as analytical factors. Meanwhile, a sensitivity index, which has the characteristics of simple expression and clear physical meaning, is proposed, i.e., the peak value of the machining error caused by each geometric error. Moreover, the simulations analysis is carried out to obtain the sensitivity coefficient of each geometric error and the key error components. Finally, the validity and correctness of the proposed method are demonstrated by the experiments. Furthermore, the SA method can be extended to multi-axis machine tools. [ABSTRACT FROM AUTHOR]

Mining/marine bushings, biomedical implants, and many more are among the several applications of lightweight elastomer components. The use of non-traditional machining to manufacture these good-quality components in small or batch-size units is required because the primary manufacturing method requires customised mould and follow-up machining. In this regard, the present work focuses on investigating the performance of machining a nitrile rubber (NR) using suspension-type abrasive water jet (AWJ) under both conventional (room temperature) and cryogenic (liquid nitrogen (LN2)) conditions at optimal values of process parameters: water jet pressure (WJP, bar), transverse rate (Vf, mm/min), and stand-off distance (SOD, mm). The experimental runs are designed using Taguchi analysis with respective performance parameters: Kerf taper ratio (KTR) and Material removal rate (MRR, mm³/min). The results show that Vf (Rank 1) is the highest influencing factor on the machining performance of NR under both conditions. The reasons are improved kinetic energy, less collision of garnet abrasive particles and lesser change in the average width of the kerf. The influence of LN2 showed that the optimal values of KTR reduced by 11.97% at 200 bar, 40 mm/min, and 1 mm, and MRR increased by 0.65% at 250 bar, 60 mm/min, and 2.0 mm, respectively. [ABSTRACT FROM AUTHOR]

Large-scale components usually have complex structures with high local stiffness, and the holes on them are required to be machined with high precision, which makes it important and challenging to study how to efficiently and precisely drill in the large-scale components. This article presents mobile hybrid machining equipment that consists of a five-axis parallel module, a 2-degree-of-freedom (DoF) robotic, arm and an automated guide vehicle (AGV) connected in series. With the ability of wide-range positioning and precise local processing, it has potential advantages in the drilling processing of large-scale components. Stiffness is one of the most important performances for machining equipment, and it's highly related to the its configuration. For the discussed equipment, the stiffness is determined by the two-stage-positioning hybrid machining robot, which comprises a five-axis parallel module and a two-DoF robotic arm. The redundant motion of the hybrid machining robot makes it possible to optimize its configuration by reasonably planning redundant motion. Therefore, a redundant motion-planning method based on stiffness performance is proposed. A kinematic analysis of the five-axis parallel module, the robotic arm, and the hybrid machining robot is carried out. A hybrid robot usually consists of several subsystems, and to take the compliance of each subsystem into consideration, the stiffness-modeling method for the hybrid robot with n subsystems connected in series is proposed. The stiffness model of the hybrid machining robot is established by using this method, and the variation of the stiffness magnitude has the same trend as that obtained by using FEA software. Stiffness magnitude and isotropy indices are proposed to evaluate the robot's stiffness performance along the axis of the spindle and in the plane perpendicular to the axis of the spindle. The redundant motion of the hybrid machining robot is planned by maximizing the stiffness magnitude along the spindle axis, with priority to the stiffness isotropy index. Finally, the drilling experiment is carried out, and the results show that the relative error of the hole diameter obtained under the optimal configuration of the hybrid machining robot is 1.63%, which is smaller than those obtained under the other two configurations for comparison with relative errors of 3.75% and 3.50%, respectively. It proves the validity of the redundant motion-planning method. The proposed stiffness-modeling method and the stiffness-performance indices are also applicable to other hybrid machining robots. [ABSTRACT FROM AUTHOR]

The static and dynamic performances of a machine tool structure are considered to constitute the primary factors affecting the load-carrying capacity, geometric accuracy and surface precision of the workpiece. The machining performance of a large machine tool under stable conditions is effectively determined by its dynamic response to the cutting force at low-frequency excitation. This study, therefore, investigated the static and dynamic characteristics of a large heavy-duty lathe machine tool in which the headstock and tailstock comprised critical component modules supporting a large workpiece during low-speed machining. Using a finite element model, the influences of the structural modules on the static and dynamic characteristics of the lathe were analyzed, considering the effects of the workpiece load. The results indicated that the fundamental vibration modes of the lathe were primarily dominated by headstock, tailstock, and workpiece behaviors. The maximum compliances of the lathe under the rated load were found to occur at relatively low frequencies (22, 40.7, and 82.7 Hz) and increase with the reduction in workpiece weight. Notably, these modal frequencies were significantly higher than the maximum rotational speed of the spindle (450 rpm). In addition, the dynamic rigidity corresponding to the rated speed was higher than that induced at the natural frequency. These results indicate that the subject lathe possesses sufficient capacity to sustain the cutting load during stable turning machining. This study can, therefore, help designers improve the performance of machine tools for future fabrication. [ABSTRACT FROM AUTHOR]

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements. [ABSTRACT FROM AUTHOR]

Machinability investigation of new material is one of the mandatory investigations to complete the purpose of creation of it. Electrical discharge machining (EDM) is one of the promising unconventional machining processes for highly accurate machining performance in difficult-to-cut material even machining complicated profiles. The powder-mixed EDM and nanopowder-mixed EDM are the improved versions of the EDM. The Al–Zn–Mg composite is reinforced with Si3N4 (9 wt. %) for meeting automotive and marine applications. The aluminium nanoparticles enhanced deionised water was used in NPMEDM. The nickel-coated brass and uncoated brass tube electrode were considered for the investigation. Pulse on time (µs), voltage (V), input current (A), and capacitance (nF) were independent variables and varied at 3 levels. The microhole machining performance with a coated and uncoated electrode was investigated. The L18 orthogonal array involved in the experimental design, material removal rate, and electrode wear rate were analysed. The SEM analysis was employed in the surface morphological study of electrodes before and after machining. The input parameters were optimised for the coated electrode for the responses of MRR and EWR. [ABSTRACT FROM AUTHOR]

Electro-discharge machining (EDM) is a useful non-conventional machining operation frequently applied to make different complex geometries in any conducting material. The objectives of the present paper are to study the effect of a variation of thermo-physical properties (TPP) of three different tool materials on EDM performances. The different performances compared in this paper are: material removal rate (MRR), tool-wear rate (TWR), surface roughness (SR), radial overcut (ROC), surface-crack density (SCD) and surface hardness. Two of the most widely used work piece materials, such as corrosion-resistant austenitic stainless steel (SS316) and high strength corrosion-resistance titanium alloy (Ti-6Al-4V), are machined with the help of three different tools by varying input current and maintaining constant pulse-on time, pulse-off time and flushing pressure. Microstructural studies of the tool tip surface after machining have also been carried out. It is found that among these three tool materials, the copper tool showed the best machining performance with respect to material removal rate, radial overcut, surface finish and surface-crack density. This work will help industry personnel to choose a suitable tool for a specific work piece material. [ABSTRACT FROM AUTHOR]

Belt transmission is one of the most common forms of transmission on CNC lathes. To assess the impact of belt performance on monitoring machining conditions using motor current signal analysis, we developed a dynamic model for belt-driven electromechanical spindle systems. This model enables the coupling of multi-physics variables including mechanical dynamics, particularly the dynamic axil and belt bending motions of belt transmissions and electromagnetic effects. Thus, various changes in belt transmission, including different degrees of belt tension and defects with inherent motor and spindle errors, can be investigated. The modulation characteristics of the current and speed signals revealed that belt transmission can cause a high degree of modulation, reflected by the rich harmonic sidebands at the belt-passing frequency in the motor current spectrum. The rate and extent of belt wear worsened with excessive wear and inadequate tension. It was confirmed that the sideband at the spindle rotating frequency was slightly impacted by changes in the belt conditions. This model was verified based on a CNC lathe induced with different degrees of belt looseness. These conclusions drawn from the numerical analyses can be used for motor current signal analysis, providing reliable and accurate results for machining monitoring. [ABSTRACT FROM AUTHOR]

The base fluids of eco-friendly cutting fluids should contain some vegetable oil. Due to their eco-friendly, thermal, and tribological qualities, these types of machining fluids are becoming increasingly popular among researchers throughout the world. The authors summarized published research studies on the use of eco-friendly machining fluids (including nanofluids) in various metal removal processes such as turning, milling, grinding, and drilling in this review paper. The impact of several biodegradable oil-based machining fluids on performance metrics such as surface irregularity, machining force, tool wear, power demand, and temperature-induced during the machining process is also explored in this paper. According to the previous and current literature, using an eco-friendly nano machining fluid results in better surface features, less cutting tool wear, lower cutting force, less power required, and lower machining zone temperature during the machining operation due to superior cooling and lubrication properties. [ABSTRACT FROM AUTHOR]

This study assessed the machining properties of 11-year-old Eucalyptus urophylla × E. grandis, known as E. urograndis wood, that was infected by stem canker disease, Chrysoporthe deuterocubensis. Instead of being discarded directly, the study aimed to explore the possibility of infected trees being used in other applications such as furniture. Sawing, planing, and boring properties as well as the surface roughness of the healthy and infected trees were evaluated. The samples were collected from infected and healthy trees and classified according to the severity of the attack: healthy (class 1), moderately infected (class 2), severely infected (class 3), and very severely infected (class 4). Prior to sawing, planing, and boring, the samples were prepared according to ASTM D 1666-11 Standard Test Methods for Conducting Machining Tests of Wood and Wood-Base Materials. All samples were sawn, planed, and bored and were evaluated for their respective machining quality. The surface roughness of the machined samples was also assessed. Overall, E. urograndis of different infection severity has very good machining properties ranging from Grade I to III. Fuzzy grain, chip grain, chip mark, and tear out are the most commonly seen physical defects. As for surface roughness, healthy trees have lower surface roughness compared to that of infected trees, which indicates a better surface quality. The findings of this study suggested that infected E. urograndis can still be used in many applications. The results of this study will provide us with better knowledge about the machining performance of disease-infected E. urograndis wood and its possibilities to be used as raw material for the wood products industry. [ABSTRACT FROM AUTHOR]

Multi-axis electrical discharging machining (EDM) is the most commonly used manufacturing method for shrouded blisks. However, debris is difficult to be evacuated from a discharge gap, severely affecting the machining efficiency. Spatial planetary movements of a tool electrode can generate a non-uniform flow field in a discharge gap, promoting the evacuation of debris. This paper proposes a spatial planetary movement method for EDM of inclined holes. Screw theory is used to derive the kinematics of a spatial planetary movement, and the coordinate increments of each axis in an interpolation period can be calculated. The trajectories of the spatial planetary movements are simulated. The reliability of the method is confirmed by the simulation. Then, a multi-axis EDM machine can be controlled to drive a tool electrode to realize the spatial planetary movement. Machining experiments are carried out to explore the influences of the spatial planetary movement parameters on the machining performance of multi-axis EDM. Experiment results show that the increase in the amplitude of spatial planetary movements can improve discharge rate and gap status. The machining depth and machining efficiency of multi-axis EDM can be increased ulteriorly. And the speed of spatial planetary movements has no significant effect on the performance of multi-axis EDM. [ABSTRACT FROM AUTHOR]

In this paper, a finite element analysis (FEA) based fast optimization method to optimize a lightweight in-wheel switched reluctance machine is presented. This method speeds up the switched reluctance machine optimization procedure by running the FEA simulations with single-phase constant current excitations for half electrical cycle and estimating the machine performance metrics using the gathered FEA data. Hence, the machine's dynamic performance estimation process takes shorter for each design candidate. The optimization algorithm employs designs of experiments (DOE), response surface (RS) analysis method, and differential evolution algorithm (DE). Here, the DOE method is used to reduce the search space by narrowing down the upper and lower boundaries of each design variable based on the RS analysis. Although this process does not guarantee getting the Pareto front, it places the search space close to the actual one. Hence, the multi-objective DE optimization finds the Pareto optimal solution set without requiring a large number of iterations as well as a large number of candidate designs for each iteration. The method is applied to a 24/16 SRM that is intended to be used in a lightweight race car as a hub motor. Six dimensionless geometric variables are optimized to satisfy three objective functions, namely torque ripple, motor mass, and copper loss. While the conventional DE takes at least 3000 candidate designs, the proposed method considers only 559 designs to reach a similar Pareto front. It is observed that the proposed method takes about 6 h 30 min compared to the conventional method that takes 32 h 50 min using the same computer. Therefore, the computation time is reduced almost five times with the proposed method. [ABSTRACT FROM AUTHOR]

The support point layout has an important influence on the working performance of the machine tool, when the material, manufacturing process, and internal structure of machine bed are given. In order to ensure the performance, stability, and anti-interference of precise machine tool, this paper presents an optimized design method of 3-point support for T-shaped bed of precision horizontal machining center. This article first establishes the statics model of the T-shaped bed and analyzes grillage beam model used to characterize the main static deformation trend of the bed based on the singular function method. After verifying the rationality of the model through simulation, the optimized 3-point support position can be obtained. Then this paper measured the deformation of the upper surface of a simple bed due to gravity. The deviation between the experimental results and the simulation results is less than 20%, which verifies the reliability of the simulation and theoretical results. Based on the ISIGHT multidisciplinary optimization platform, this paper completes the multi-objective optimization of the support point layout of the bed, and the optimization results prove the accuracy of the theoretical model. This paper takes the bed of M800H precision horizontal machining center as an example to illustrate the application process of the proposed method. Then, the optimization effect of the bed and the static characteristics of the whole machine by simulation are compared. The maximum deformation of the bed has reduced by 27.1%. In the whole machine status, the deformation of the spindle end has reduced by 50.8%, and the maximum deformation of the workpiece end has reduced by 50.0%. Finally, a verification experiment of the relative position deviation between the end of the spindle and the worktable on the complete machine was carried out. The error of the simulation and experiment results was about 22%, which proves the effect of the optimization of the support position. [ABSTRACT FROM AUTHOR]

Featured Application: Aiming at reducing the machining deformation of aero-engine casing, this paper proposes a method to monitor the deformation of the part by use of the variation of clamping force, and designs an intelligent fixture according to the machining characteristics. To facilitate deformation monitoring, a clamping force decision method based on a probability model and Kalman filter is proposed, which can improve the monitoring success rate. This paper belongs to the research category of intelligent manufacturing and high-precision machining. Aero-engine casing is a kind of thin-walled rotary part for which serious deformation often occurs during its machining process. As deformation force is an important physical quantity associated with deformation, the utilization of deformation force to control the deformation has been suggested. However, due to the complex machining characteristics of an aero-engine casing, obtaining a stable and reliable deformation force can be quite difficult. To address this issue, this paper proposes a deformation force monitoring method via a pre-support force probabilistic decision model based on deep autoregressive neural network and Kalman filter, for which a set of sophisticated clamping devices with force sensors are specifically developed. In the proposed method, the pre-support force is determined by the predicted value of the deformation force and the equivalent flexibility of the part, while the measurement errors and the reality gaps are reduced by Kalman filter via fusing the predicted and measured data. Both computer simulation and physical machining experiments are carried out and their results give a positive confirmation on the effectiveness of the proposed method. The results are as follows. In the simulation experiments, when the confidence is 84.1%, the success rate of deformation force monitoring is increased by about 30% compared with the traditional approach, and the final impact of clamping deformation of the proposed method is less than 0.003 mm. In the real machining experiments, the results show that the calculation error of deformation by the proposed method based on monitoring the deformation force is less than 0.008 mm. [ABSTRACT FROM AUTHOR]

Planning tool paths on free-form surfaces is a widely discussed issue. However, satisfying all the requirements of blade machining using traditional path-planning methods remains a challenge. Herein, a new iso-parametric path-planning strategy, based on a novel parameterization method and combined with conformal transformation theory, is proposed. The presented strategy adapts to the curvature of blade surfaces, improving the kinematic performance of the machining process, reducing the complexity of multi-axis coordinated motion control, and improving the machining quality. A comparison between the proposed and three traditional methods is also discussed. The influence of the tool path on the kinematic performance of the machine tool is quantitatively examined based on the kinematic models of two different machines. Finally, the results of a deep-cutting milling experiment conducted to verify the improvement of the machining quality due to kinematic optimization are described. The proposed method provides a more reasonable path-planning approach for blade machining on a five-axis machine tool, which can significantly reduce the cost of blade machining, minimize the risks of blade failure, and enhance the large-scale automated production of blades. [ABSTRACT FROM AUTHOR]

Machining optimization has been studied for low machining cost, high production rate, and energy saving. Nevertheless, most of the studies did not consider the part geometry. The complex part geometry results in varying contact conditions between the cutting tool and the workpiece. Therefore, the conventional tool life equation may not be applicable, and it is impossible to expect the same performance of machining different parts even with the same cutting parameters. In addition, the amount of material removal is different from part to part. Either the underuse or overuse of the tool is considered production loss in machining process. As a result, it is better for the cutting tool to be utilized until it reaches the end of its life when the operation is completed. Taking the conditions above into considerations, an objective function including the machining cost and the loss of tool usage is proposed in this study. Three turning operations, one straight turning and two profiling, were investigated to show the effect of part geometry on the machining optimization and tool wear. The results showed the different optimization processes and tool wear patterns although the initial cutting parameters and the amount of material removal were identical. The optimization processes were converged in 5 iterations. The cost of three cases reduced 34%, 33%, and 19%, respectively, with different optimized cutting parameters, and the tool approached the end of its life when the operation was done. [ABSTRACT FROM AUTHOR]

Ultrasonic vibration–assisted grinding (UVAG) shows excellent performance in the machining of difficult-to-cut materials. Grinding force is one of the key factors affecting the quality of the machined surface, which has been investigated both experimentally and theoretically. However, the research on the modeling of the grinding force of the separated ultrasonic vibration–assisted grinding of metal difficult to cut materials is not in-depth for this process. It is concluded through experimental analysis that 304 stainless steel is removed by plastic removal under the action of tangential ultrasonic vibration–assisted grinding (TUVAG), and it is removed by brittle removal under the action of radial ultrasonic vibration–assisted grinding (RUVAG). On this basis, two kinds of prediction models of grinding force for ultrasonic vibration–assisted grinding are established. In addition, the relationship between grinding force and experimental parameters (spindle speed, feed rate, grinding depth, ultrasonic amplitude) was also revealed through models. Finally, the theoretical models are verified by experiments. The experimental results are consistent well with the models predictions. Therefore, these two theoretical models can be used to predict the grinding force well. [ABSTRACT FROM AUTHOR]

The industry anticipates technological advancements for productivity improvement, and this can be accomplished by improving the machining performance in the areas where machinability challenges exist. The machinability and productivity of wire electric discharge machining (WEDM), which is a popular non-conventional cutting process, can be improved while having comparable surface integrity. Previously the non-conventional machining literature is focused on the machinability investigations of various industrial materials and process optimization. However, no extensive research on the study of flushing parameters optimization to improve machinability in non-conventional machining is known. In this research, WEDM of M42 HSS using controlled flushing is performed resulting in improved machinability in terms of material removal rate (MRR), surface roughness (Ra), and kerf width (KW). The findings indicate that the nozzle diameter (76.85%) has a substantial influence on total machining performance. Nozzle diameter, nozzle-workpiece distance, servo voltage, and flushing pressure all had a substantial influence on MRR, with percentage contributions of 34.50%, 26.02%, 22.94%, and 14.21%, respectively. Furthermore, multi-response optimization is also performed and depicts the possibility of achieving optimized values of MRR, Ra, and KW simultaneously. The flush-controlled machining improved work quality in terms of accurate production, increased productivity by lowering process time, and improved surface integrity of the machined piece thus can be a possible process advancement in the aerospace and automotive industries. [ABSTRACT FROM AUTHOR]

In order to quickly and accurately predict the spatial geometric error field of the five-axis machine tool processing, a method for predicting the comprehensive error field of the five-axis machine tool processing space based on the "S"-shaped specimen family is studied. Firstly, for the five-axis CNC machine tool in the form of A-C dual turntable, the geometric error model of the rotating axis is established based on the multi-body dynamics theory; the error mapping relationship between the processing technology system and the workpiece is analyzed based on the "S"-shaped specimen family, and the identification of 12 geometric errors of the two rotating shafts. Then, the error value of the sampling point is measured based on the "S"-shaped test piece in machine contact, and the double-circle center coordinate value is determined according to the curvature of the measured wire of the test piece, in order to identify the geometric errors of the two rotation axes of the five-axis machine tool. Finally, based on the prediction method, the comprehensive error field of the five-axis CNC machine tool processing space is analyzed. Compared with other geometric error identification methods, the measurement accuracy of this method meets the processing requirements and can further evaluate the comprehensive performance of the machine tool. [ABSTRACT FROM AUTHOR]

The high-purity G5 graphite material is widely used for glass moulding and provides high hardness and brittleness because it is sintered to fine particles unlike other graphite materials. Hence, tool cutting of a G5 workpiece is performed by local fracture instead of plastic deformation of the machined surface. Although a diamond-coated tool with outstanding hardness is used to machine very hard graphite, the tool shows variability regarding the service life and machining performance depending on the grain size, even in the same machining environment. We investigated the wear and change trend of machined surface roughness considering microcrystalline diamond (MCD) and nanocrystalline diamond (NCD)-coated tools, which are generally used to machine graphite materials, and analysed their relation with coating. For rough machining, the MCD-coated tool, for which the delamination of coating occurred later, showed less wear and improved machined surface roughness. For precision machining, the NCD tool showed less tool wear rate relative to the cutting length, leading to a small difference in the machined surface roughness between the two tools. We conclude that if rough and precision machining processes are performed using the same cutting tool, the MCD-coated tool is advantageous in terms of service life, while the difference in roughness of the final machined surface between the tools is negligible. [ABSTRACT FROM AUTHOR]

For improving energy efficiency of machining process, extensive studies focused on how to establish energy consumption model and optimize cutting parameters. However, the existing methods lack a systematic method to promote the widespread use of energy efficiency methods in industry. This paper proposes a systematic method integrating energy model, experiment design, and multi-objective optimization model. Firstly, the energy model is established considering cutting energy and non-cutting energy. Then, the orthogonal experiment is designed with the three levels of four factors of spindle speed, feed speed, cutting depth, and cutting width in the X and Y cutting directions. The data of energy consumption, surface quality, and machining time are obtained to study the effects of different cutting elements and cutting directions. Meanwhile, the standby, spindle idling, feed, SEC (specific energy consumption), material cutting, and idling feed models of the CNC machine tools are established based on the experimental data. Additionally, five sets of experimental data are tested for verifying the accuracy of the established energy consumption model, which can reach 99.4%. Finally, a multi-objective optimization model for high efficiency and energy saving of processing process is established to optimize the cutting parameters considering energy consumption, processing time, and surface quality simultaneously. Combining the case of milling with constraints including machine tool performance, tool life, processing procedures, and processing requirements, the Pareto solution set is used to solve the Pareto of the target model. Through drawing a three-dimensional needle graph and two-dimensional histogram, the optimal cutting parameter combination for rough machining and semi-finish machining is provided assisting in promoting the application of the sustainable techniques in the industry. [ABSTRACT FROM AUTHOR]

The additional tool orientation freedom of multi-axis machining introduces technical and computational challenges, including tool-path instability (jaggedness) and local/global cutter interference. To address the two challenges, most research has focused on the following three aspects of tool-axis control: (1) smoothness, (2) preferred direction, and (3) gouge avoidance. The previously proposed covariant-field multi-objective tool-axis optimization (CMTO) method addressed all three requirements collectively in a common framework. However, it involved numerical optimization with a vast number of variables and the resulting computational performance is less competitive compared to leading commercial CAM. In addition, the CMTO method uses the contact-driven tool position computation method and a corresponding gouge-avoidance scheme that does not optimize cut width. To address the computational performance issue and contact-driven limitations (especially non-optimal gouge avoidance), this paper presents an enhanced CMTO that incorporates a projection-based, improved/optimized gouge-avoidance mechanism and a pseudo-multi-grid (PMG) method, replacing SQP, as the numerical solver. The use of a more robust projection-based gouge-avoidance method ensures the least possible cutter forward leaning to maximize cut width such that the solution is generalized for finishing applications. The PMG method divides the tool path into sub-regions and solves the optimization in steps, achieving faster convergence and hence computational performance upgrade. The enhanced CMTO was applied to five-axis blade finishing tool-path computation. The experimental results show that the proposed method delivers better computation performance and machining quality and is suitable for industrial application for five-axis blade finishing. [ABSTRACT FROM AUTHOR]

Despite various vibration-assisted cutting techniques have been utilized to increase the machining performances of brittle materials, the highly dynamic variations of cutting process quantities lead to the complicated brittle-ductile transition (BDT) mechanism and the formation of undesired vibration marks on the machined surfaces. In this paper, a novel ultra-precision vibration-assisted cutting process, named trapezoidal modulation diamond cutting (TMDC), is firstly proposed for ductile-regime machining of brittle materials with significantly increased BDT cutting depth. By imposing a dedicate trapezoidal locus to the diamond tool, the unique invariable uncut chip thickness and cutting states were achieved for realizing stable vibration-assisted cutting without the formations of vibration marks. Systematic cutting experiments of KDP crystals were carried out to comprehensively investigate the influences of different process parameters on the machining performances of TMDC process. In addition, the underlying mechanisms of machining performance improvements have been discussed under the different combinations of process parameters, based on which the guidelines for optimal process parameter selection are given for increasing the BDT cutting depths. The outcomes of this study contribute to not only improving the ductile machining efficiency and machining quality of KDP crystals, but also help to deepen the understandings of BDT mechanism during vibration-assisted diamond cutting of common brittle materials. [Display omitted] • A novel trapezoidal modulation diamond cutting is firstly proposed for brittle materials. • The BDT cutting depth of KDP crystal has been remarkably increased. • The vibration marks in traditional vibration-assisted cutting are avoided. • Systematic experiment studies are carried out for process parameter sensitivity analysis. • Optimal process parameter selection is given for increasing the BDT cutting depths. [ABSTRACT FROM AUTHOR]

The growing public concern about sustainable development has increased interest in exploring sustainable machining alternatives. However, the absence of a widely accepted method for evaluating the environmental impact of machining processes and a thorough understanding of the requirements for achieving sustainable development has created a pressing need for integrated assessment models. This study addresses this need by proposing a novel sustainable performance model for evaluating machining processes based on a thorough understanding of economic and environmental sustainability and employing text mining technology and grey correlation analysis. By weighting keyword frequencies in a text mining approach, the model identifies the key factors influencing the development of machining processes toward economic and environmental sustainability, improving the objectivity of sustainable development assessment indexes. To validate the proposed model feasibility, a case study is conducted using existing data from a typical machining process, confirming the effectiveness of the assessment model. The model presented in this study provides a scientific framework for assessing sustainable development in the machining field and a valuable foundation for decision-making that considers both economic and environmental sustainability in machining practices. [Display omitted] • Creating a sustainability assessment model to evaluate the machining process. • Two-step sustainable assessment model with text mining and grey relation analysis. • Case studies validation for the proposed sustainable indexes. • Proposed model optimizes machining conditions and environmental impacts. [ABSTRACT FROM AUTHOR]

Cemented carbide inserts coated with CVD α-alumina, particularly those exhibiting a (0001) texture, have proven highly effective in steel turning. Despite the established superior performance of (0001) textured alumina coatings, the underlying reasons remain unclear. This study explores the influence of the crystallographic texture of alumina on wear mechanisms in various chip-tool contact zones on the insert rake face. The objective is to establish a fundamental understanding of the active degradation mechanisms and machining performance by relating coating texture to the orientation and deformation of individual Al 2 O 3 grains. Two multilayered coatings, Al 2 O 3 on Ti(C,N), featuring (0001)- and (11 2 ‾ 0) -textured CVD α-alumina, were assessed in dry turning of a bearing steel. The wear rate of the (11 2 ‾ 0) coating was double that of the (0001) coating. Worn coatings exhibit nano-terrace formation at the insert edge, likely due to chemical etching. In the sticking zone, plastic deformation leads to larger facets for grains oriented with the chip flow direction, while rounded surfaces result if this condition is not met. In the transition zone, both (0001) and (11 2 ‾ 0) textured coatings undergo increased plastic deformation accompanied by sub-surface dislocations. (0001) texture deforms more by basal slip creating a wavy coating pattern with steps present at larger misalignments of the lattice planes in neighboring grains while (11 2 ‾ 0) texture deforms by several slip systems creating elongated ridges and ruptured-like areas resulting in rougher surface. This difference in surface morphology is then inherited by the abrasion of submicron coating fragments embedded in the chip (more in (11 2 ‾ 0) texture) in the sliding zone resulting in an even rougher surface. Chemical reaction with the hot chip may also contribute to wear acting as an additional mechanism. This fundamental understanding contributes to the potential enhancement of steel machining using texture-controlled CVD alumina coatings, ultimately improving coated cutting tool performance. [Display omitted] • The wear rate of (0001)-textured α-alumina coating is half of that of the (11 2 ‾ 0) -textured coating. • Generation of a flat tool/chip contact surface gives less coating plastic deformation. • Slip systems parallel to the wear surface generate a microscopically flat surface. • Steps are created at larger misalignment of the lattice planes in neighboring grains forming a wavy pattern. • Wear surface morphologies are coupled to the number and direction of activated slip systems. [ABSTRACT FROM AUTHOR]

The manufacturing sector accounts for 40% and 25% of global energy and resources consumption respectively. Machining is one of the most power-intense operations in the manufacturing sector making a significant contribution to the environmental footprint. The purpose of this study is to identify the key parameters that contribute towards the environmental impacts of conventional turning. A set of experiments was designed using the Taguchi L9 method. Experiments were performed to analyse the electrical energy consumption, metalworking fluid (MWF) consumption, surface roughness, and material removal rate during turning of AISI P20 with both wet and dry machining. A life cycle assessment (LCA) was conducted to assess the environmental performance of turning. A multi-response optimisation was performed to identify the optimum operating conditions. The results show that turning with wet machining yields better machining and environmental performance compared to dry machining. The LCA results reveal that electrical energy is the highest contributor under most of the impact categories, while the effect of MWF is negligible. The use of workpiece material and cutting insert material contribute significantly to the impacts under aquatic ecosystem and resource depletion damage categories. Further, optimum parameters were proposed considering both machining performance and environmental impact. [ABSTRACT FROM AUTHOR]

The use of metal foams for damping vibrations of mechanical structures has found interesting applications in machine tools and its components. Indeed, undesired vibration is one of the most detrimental causes that limit the machine performance in terms of the maximum achievable material removal rate MRR. Although positive results were presented in some research works, a methodology for predicting the damping properties of such materials in the machine tool design phase using finite element codes is still missing. In order to bridge this gap, in this paper, an experimental procedure for identifying the damping contribution of the aluminum metal foam to the hosting structure is proposed. The experimental data are even used to develop a model for predicting the damping. The procedure is further validated on a dummy structure. [ABSTRACT FROM AUTHOR]

In the present investigation, machining performance of Ti6Al4V is studied under dry environment, pressurized air supply and distilled water based Minimum Quantity Lubrication (MQL). Machining (turning) performance is assessed in purview of cutting force (tangential component) magnitude, tool-tip temperature, width of tool flank wear progression, morphology of evolved chips and severity of vibrations at varied cutting speeds as well as cooling media. Characteristic features of spatial temperature distribution profile (at the vicinity of tool-tip) as influenced by varied cooling media are studied with the help of thermographs of the cutting zone. Mechanisms of cutting tool wear are studied as well. It is experienced that amongst three cutting environments tested, application of water-MQL is beneficial for machining of Ti6Al4V at low cutting speed. Severity of vibrations gets 50.39% reduced during machining under water-MQL than dry condition. Consequently, as compared to dry machining, water-MQL causes 79% reduced tool flank wear and 82% reduced crater wear at low cutting speed. Under water-MQL, reduced tool-tip temperature (66% reduced than dry condition) suppresses severity of tool wear. In comparison with high cutting speed, performance of water-MQL is found much better at low cutting speed. 'Unaffected zones' are identified at the worn-out tool rake face under water-MQL. High amplitude of vibration (maximum absolute mean value) causes low chip-segmentation ratio. On the contrary, chip reduction coefficient gets truncated with decrement in vibration amplitude. Vibration amplitude has positive influence on degree of chip-curl. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a general precision allocation method to improve machining performance of CNC machine tools based on certain design requirements. A comprehensive error model of machine tools is established by using the differential motion relation of coordinate frames. Based on the comprehensive error model, a reliability model is established by updating the primary reliability with an advanced importance sampling method, which is used to predict the machining accuracy reliability of machine tools. Besides, to identify and optimize geometric error parameters which have a great influence on machining accuracy reliability of machine tools, the sensitivity analysis of machining accuracy is carried out by improved first-order second-moment method. Taking a large CNC gantry guide rail grinder as an example, the optimization results show that the method is effective and can realize reliability optimization of machining accuracy. [ABSTRACT FROM AUTHOR]

The motion smoothness of 3C locking robot directly affects the machining performance. Improving the motion smoothness can optimize the motion trajectory and reduce the processing time. In this paper, a novel machining path optimization model including motion smoothness is built by employing the coordinate boundary of velocity and acceleration after evaluating the machining motion smoothness of the 3C locking robot. Secondly, based on the creation of the ant colony of adaptive function algorithm, the optimization model of the 3C locking robot in the situation of fixed bolt hole position and floating bolt hole position is resolved. Lastly, the proposed approach collects and analyses a huge amount of data to enable robots to make on-the-fly decisions in the middle of production, even when faced with unexpected circumstances. In the Spark distributed environment, we use the conventional K clustering technique to improve the final output utilizing clustering means. The results show that the machining path optimization of fixed hole considering the motion smoothness improves the smoothness but extends the machining path; the cooperative machining path optimization of multiregion floating bolt holes can significantly improve the motion smoothness and effectively reduce the length of the path. The research results provide theoretical support and design guidance for designers. [ABSTRACT FROM AUTHOR]