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Nanofluids are efficient heat transfer media that have been developed over the past 27 years and have been widely used in the electronic microchannel, engine, spacecraft, nuclear, and solar energy fields. With the high demand for efficient lubricants in manufacturing, the application of nanofluids in machining has become a hot topic in academia and industry. However, in the context of the huge amount of literature in the past decade, existing review cannot be used as a technical manual for industrial applications. There are many technical difficulties in establishing a mature production system, which hinder the large-scale application of nanofluids in industrial production. The physicochemical mechanism underlying the application of nanofluids in machining remains unclear. This paper is a complete review of the process, device, and mechanism, especially the unique mechanism of nanofluid minimum quantity lubrication under different processing modes. In this paper, the preparation, fluid, thermal, and tribological properties of nanofluids are reviewed. The performance of nanofluids in machining is clarified. Typically, in friction and wear tests, the coefficient of friction of jatropha oil-based alumina nanofluids is reduced by 85% compared with dry conditions. The cutting fluid based on alumina nanoparticles improves the tool life by 177–230% in hard milling. The addition of carbon nanotube nanoparticles increases the convective heat transfer coefficient of normal saline by 145.06%. Furthermore, the innovative equipment used in the supply of nanofluids is reviewed, and the atomization mechanisms under different boundary conditions are analyzed. The technical problem of parameterized controllable supply system is solved. In addition, the performance of nanofluids in turning, milling, and grinding is discussed. The mapping relationship between the nanofluid parameters and the machining performance is clarified. The flow field distribution and lubricant wetting behavior under different tool-workpiece boundaries are investigated. Finally, the application prospects of nanofluids in machining are discussed. This review includes a report on recent progress in academia and industry as well as a roadmap for future development. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools' health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method. [ABSTRACT FROM AUTHOR]

This paper presents the development of an innovative augmented reality application for evaluating clamping concepts through visualizing the finite element analysis. The focus is on transforming the traditional simulation results into immersive, holographic displays, enabling users to experience and assess finite element analysis in three dimensions. The application development process involves data processing by MATLAB, visualization in the software Unity, and displaying holograms through Microsoft's Hololens2. The most significant advancement introduces a new algorithm for rendering different finite elements in Unity. The application targets not only university engineering students but also vocational students with limited background in finite element analysis and machining, aiming to make the learning process more interactive and engaging. It was tested in a real machining environment, demonstrating its technical feasibility and potential in engineering education. [ABSTRACT FROM AUTHOR]

Despite numerous studies regarding tool life modeling, it is usually difficult to apply conventional models to industrial fields because of factors such as impractical data acquisition systems, the complexity of model construction, and low adaptability to varying cutting conditions and volumes. This paper introduces a novel tool life model based on the power consumption of a machine tool with respect to varying process parameters; it was hypothesized that the proportion of the power consumed in tool wear (i.e., dissipating the cutting edge) to total power remains constant, regardless of the cutting conditions. Cutting power consumption was measured and integrated with the cutting length in the milling of titanium alloy. The experimental results confirmed the validity of the hypothesis; tool life was successfully predicted based on the accumulated cutting power consumption values, and the effects of variations in cutting volumes were compensated for based on cutting power consumption. The developed model was validated in a case with various feeds and cutting volumes mimicking a field case. Furthermore, the model was able to cope with unexpected incidents, such as a non-continuous coolant supply. Thus, the results demonstrate the applicability and practicality of using the integrated cutting power consumption value to predict tool life. Even in new machining environments such as with new tools and work materials, the developed model can easily be constructed with a small number of experiments to justify the model coefficients. Power consumption is expected to be more widely utilized for process monitoring in industry and as a health indicator based on its excellent performance and practicality. [ABSTRACT FROM AUTHOR]

The traditional method of manufacturing propellant tanks for rockets and spaceships involves significant amounts of forging, and machining, making it expensive and environmentally unfriendly. A novel approach for manufacturing propellant tanks that reduces the need for machining and friction stir welding processes has been presented in this paper. This approach involves manufacturing a tank half starting from a single metal plate, using innovative and advanced metal forming processes such as hot stretch forming, magnetic pulse forming, hub forming, and integrated stiffened cylinder (ISC) flow forming followed by orbital welding of two tank halves. A life cycle assessment (LCA) study was conducted in accordance with ISO 14044:2006 standard using the ReCiPe 2016 Midpoint (H) method to compare the environmental impacts of the traditional and newly developed approaches for manufacturing propellant tanks. The results of the LCA study showed that the new approach based on advanced forming technologies reduced carbon footprint by 40%, cumulative energy demand by 35%, water footprint by 17%, and materials waste by 4% compared to traditional manufacturing. The lower environmental impact of the new approach was attributed to a decrease in friction stir welding requirements due to the implementation of advanced forming techniques that enable integrated tank production. This lowered the overall energy consumption in the novel approach by a factor of 1.5 and in turn resulted in lower environmental impact compared to traditional forging and machining-based method. Furthermore, a futuristic scenario that involves in-house tank production using the novel approach with minimal transportation of inventories was also simulated. Based on the LCA results, it was seen that the newly developed approach for manufacturing propellant tanks was more environmentally friendly than the traditional approach and its environmental footprint could be further reduced by implementing the futuristic scenario with minimal transportation. This novel approach is also expected to reduce the lead time and production cost of manufacturing a propellant tank. Hence, future efforts in cost assessment and further optimization of raw material and energy usage are recommended. [ABSTRACT FROM AUTHOR]

The physics of Ti-6Al-4 V titanium alloy chip formation during orthogonal cutting is still a prominent research field while considering its modelling. The accuracy of the numerical machining simulations primarily depends on the choice of adapted constitutive material behaviour model and thermal properties. This paper allows studying the material parameters associated with a modified Johnson–Cook (MJC) material model and thermal properties based on chip segmentation analysis. Later, the model's ability to correctly simulate the machining is evaluated by comparing it to experimental cutting on a Ti-6Al-4 V alloy specimen. The experiments are performed on an instrumented orthogonal cutting bench, which allowed cutting force measurement and chip geometry analysis during the orthogonal cutting process within a wide range of cutting speed and feed configurations. [ABSTRACT FROM AUTHOR]

Micro-cutting is characterised by undeformed chip thicknesses in the micron range. This range leaves little choice of in situ observation techniques. One solution is therefore to observe these zones a posteriori after the cutting has been abruptly interrupted. This technique has proven to be very useful for conventional machining. This paper deals with the development of a quick-stop device dedicated to micro-cutting. The aim of this device is to observe cutting mechanisms and chip formation at this scale in order to enhance micro-cutting models. [ABSTRACT FROM AUTHOR]

This article discusses the effect of machining assistance on the machinability and surface integrity of titanium and nickel-based alloys for turning operations. The presented results are based on a literature review of the principal experimental work published by the French research laboratories within the framework of the Manufacturing 21 group. The work presented focuses on the assistances the most studied by the research group: cryogenic assistance and high-pressure assistance. This paper specifically addresses the experimental approaches used to determine the effect of the assistant on the cutting force, tool wear, chip formation, microstructural changes, and residual stresses generated on the workpiece machined surface. This literature review is completed by new results comparing machining tests on the Ti6Al4V titanium alloy using high pressure and cryogenic assistances. The results show that the assistances increase the tool life, as well as improve the surface integrity of the parts. However, these gains are not identical from one material to another. In particular, titanium alloys and nickel alloys show how a very different gains for a fixed assistance. The conclusions presented highlight current trends and research needs. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology is highly regarded due to its advantages, such as high precision and the ability to address complex geometric challenges. However, the development of additive manufacturing process is constrained by issues like unclear fundamental principles, complex experimental cycles, and high costs. Machine learning, as a novel artificial intelligence technology, has the potential to deeply engage in the development of additive manufacturing process, assisting engineers in learning and developing new techniques. This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing, particularly in model design and process development. Firstly, it introduces the background and significance of machine learning-assisted design in additive manufacturing process. It then further delves into the application of machine learning in additive manufacturing, focusing on model design and process guidance. Finally, it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing. [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]

Cutting toolholders, with designed in-built compliant mechanisms, are frequently used for machining microfeatures. In this paper, compliant structures are designed using trusses as they exhibit a superior strength-to-weight ratio. Systematically stacked two-stage truss structures along with flexure hinges are designed to yield the desired precise cutting motion. The materially reduced efficient design amplifies the output force and greatly assists in overcoming cutting resistance during machining. The characteristics of the designed compliant tool holder are analysed by considering the output force and displacement for a given input. Both mathematical modelling to determine the force and finite element analysis to identify the critical stress regions when subjected to a prescribed input displacement are presented. As part of dynamic analysis, modal analysis and harmonic response studies were performed to determine how the trussed compliant structure deforms when subjected to dynamic loadings. The designed tool holder was additively manufactured using functionally rigid polyamide-12-glass-bead (PA12-GB) material. The performance of the developed cutting tool holder is then verified by machining radially graded surfaces following which, the machining results are presented in this paper. [ABSTRACT FROM AUTHOR]

Hybrid components, made of multiple materials, can meet the increasing demands for lightweight construction and functional integration in the automotive and aircraft industry. Hybrid semi-finished components are produced by applying a high-alloy cladding to a low-alloy base material before hot-forming and machining the workpiece. Throughout this process chain, workpiece deviations in the form of material distribution and material properties can occur that influence the component's lifetime. This paper investigates whether such workpiece deviations can be detected within the process chain by analyzing process signals obtained from subsequent process steps. For this purpose, artificial workpiece deviations were introduced to hybrid semi-finished workpieces made of C22.8/X45CrSi9-3. Then, process signals during forming and machining were analyzed to determine their sensitivity to the artificial deviations. The results revealed that deviations in cladding size can be effectively monitored using signals from both forming and machining. Cladding position deviations can only be detected during machining, while forming signals are more responsive to detecting the introduced hardness deviations of approx. 100 HV0.1. [ABSTRACT FROM AUTHOR]

Wire arc additive manufacturing (WAAM) has received increasing use in 3D printing because of its high deposition rates suitable for components with large and complex geometries. However, the lower forming accuracy of WAAM than other metal additive manufacturing methods has imposed limitations on manufacturing components with high precision. To resolve this issue, we herein implemented the hybrid manufacturing (HM) technique, which integrated WAAM and subtractive manufacturing (via a milling process), to attain high forming accuracy while taking advantage of both WAAM and the milling process. We describe in this paper the design of a robot-based HM platform in which the WAAM and CNC milling are integrated using two robotic arms: one for WAAM and the other for milling immediately following WAAM. The HM was demonstrated with a thin-walled aluminum 5356 component, which was inspected by X-ray micro-computed tomography (μCT) for porosity visualization. The temperature and cutting forces in the component under milling were acquired for analysis. The surface roughness of the aluminum component was measured to assess the surface quality. In addition, tensile specimens were cut from the components using wire electrical discharge machining (WEDM) for mechanical testing. Both machining quality and mechanical properties were found satisfactory; thus the robot-based HM platform was shown to be suitable for manufacturing high-quality aluminum parts. [ABSTRACT FROM AUTHOR]

The machining of thin-wall components made of titanium alloys is challenging because the poor machinability of the material leads to severe problems such as accelerated tool wear and poor surface quality, while the weak rigidity of the thin-wall structure results in unavoidable vibration and surface form errors. To address these issues, this paper investigated the mechanisms and performance of cooling minimum quantity lubrication (CMQL) in milling titanium thin-wall parts. To verify the efficiency of CMQL, different cooling/lubrication strategies, including conventional flood cooling, minimum quantity lubrication (MQL) and CMQL with different temperature levels, were investigated. The cutting force, tool wear state, chip formation, surface integrity, and surface form errors were compared and analysed in detail. The experiment results show that MQL is inadequate at higher spindle speeds due to its ineffective cooling capacity and weakened lubrication ability. In contrast, CMQL has demonstrated its feasibility and superiority in milling titanium thin-wall parts under all conditions. The outcomes indicate that a lower temperature level of CMQL is advantageous to producing better wear resistance and lower thermomechanical loads, and the CMQL (− 15 ºC) machining environment can remarkably improve the overall machining performance and control the surface form errors of the machined thin-wall parts. At the spindle speed of 3000 rpm, the surface roughness measured under CMQL (− 15 °C) condition is reduced by 16.53% and 23.46%, the deflection value is decreased by 54.74% and 36.99%, while the maximum thickness error is about 53.51% and 20.56% smaller in comparison to flood cooling and MQL machining. In addition, CMQL is an economical and sustainable cooling/lubrication strategy; the outcomes of this work can provide the industry with useful guidance for high-quality machining of thin-wall components. [ABSTRACT FROM AUTHOR]

The continuous short line segment processing method in CNC machining has the defect of frequent fluctuation of feedrate, which affects the quality and efficiency. For this problem, the local and global methods are common solutions. The local method is to insert transition curves at the corners to obtain a smooth toolpath, but this method does not essentially reduce the number of accelerations and decelerations, and it is difficult to insert transition curves between tiny straight sections. Although the global method can obtain smooth tool trajectories, the parametric interpolation method is not suitable for middle- and low-end CNC machine tools. The article proposes an adaptive tool path generation method with the original part contour as the research object and discretizes the NURBS curves into smooth trajectories consisting of large segments of straight lines and circular arcs. The article mimics the crawling characteristics of snakes and designs a double-headed snake algorithm based on the least squares method to implement this process. First, given the start and end points of the curve, two snakeheads search for the maximum linear and circular segments that satisfy the error constraint in the curve direction. The winner will then be selected as the candidate track segment through a competition mechanism. Finally, all trajectory segments are connected to obtain a smooth tool path. Experiments show that the toolpath data of the method in this paper are reduced by 75.56% compared with commercial CAM software when the contour error threshold is the same, and the contour error is reduced by more than one order of magnitude when the number of toolpath segments is equal. In addition, the method in this paper can obtain a smoother machining surface and more stable cutting force, thus achieving a win‒win situation of quality and efficiency. [ABSTRACT FROM AUTHOR]

Large-mechanical-part machining is a very important trend for modern industry to develop, and it has attracted a lot of attention from advanced industries. As an important element of the research, the manufacturing system for large parts has been widely studied. In order to get a comprehensive understanding of this kind of system, the state of the art in several aspects including the classification of the system, major challenges facing each kind of system, structure and optimizes design of the system are summarized in this paper. The manufacturing system is divided into two categories: large workshop machine tools and light and agile machine systems. The design and optimization methods for large workshop machine tool structural parts are summarized. Common techniques for error compensation are also analyzed. Development of the light and agile machine system is stated, and its further classification is carried out. Significantly, features as well as advantages and disadvantages of different systems are analyzed. Finally, this paper gives out further research on the manufacturing systems for large parts. [ABSTRACT FROM AUTHOR]

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving. [ABSTRACT FROM AUTHOR]

This work presents a measurement for the identification of translational quasi-static stiffness of machine tool using Stiffness Workspace System (SWS). The novelty of this work is a significant modification of the SWS. The changes in measurement procedure and data analysis as well as new technical solutions are described in detail. A methodology for generalised translational static stiffness determination is presented. The major purpose of this paper is to provide the information about quasi-static stiffness values that characterise a typical machine tool. The measurement procedure is implemented in a case study on a 5-axis machining centre. Measurement results were used to determine the generalised translational stiffness indicators of points in the workspace. Then, the static stiffness distribution on the XY plane over machining space was estimated. The obtained results confirm the importance of analyses of static stiffness distribution as they point out areas where some possible changes occur. In the conclusion of the paper the utility value of the information of translational static stiffness is emphasised. [ABSTRACT FROM AUTHOR]

Realizing good quality of machined parts at reduced production time and cost is an important task. Cutting parameters have a major influence on the cost and material removal rate (MRR). Chatter, a dominant phenomenon that occurs during machining, affects the selection of cutting parameters. It is present in almost all machining operations and is a major obstacle in achieving accuracy and productivity. Regenerative chatter, in particular, is the most detrimental to any process and it creates excessive vibration between the tool and the workpiece, preventing possible higher MRR. In addition, it leads to poor surface finish and accelerated tool wear. Either best machining parameters for an existing condition or good machine tool dynamics through minor modification improves MRR. This paper opts for the latter method by modifying the existing tool-post in a conventional lathe with a semi-active biodegradable MR fluid-filled tool-post. Stability lobe diagrams (SLD) prepared through frequency response functions (FRF) for the existing and modified tool-posts revealed that there is an increase of 23% in damping ratio, and furthermore, the chatter-free depth of cut in turning increased from 0.831 to 1.355 mm, i.e., an increase of 63%, irrespective of the spindle speed, along with improved surface finish. [ABSTRACT FROM AUTHOR]

In the state of machining service, to evaluate the machining accuracy reliability of multi-axis CNC machine tools, analyze the accuracy failure mode and the reliability sensitivity under the failure mode, this paper proposes a research method by the cross-correlation studies of geometric error parameters to improve and promote the accuracy reliability of CNC machine tools. Firstly, by multi-body system theory, the homogeneous coordinate transformation matrix between each body of the machine tool is established, and the spatial machining accuracy model is constructed. At the same time, considering the time series problem in the measurement process of geometric error parameters, this paper studies the correlation between the error parameters for reliability analysis and proposes a novel reliability analysis index for each measurement point in the machine tool workspace. To validation of the method, the analysis results of this paper are compared with those by Monte Carlo simulation. In addition, the accuracy failure conditions of the machine tool are studied, the failure state function of machine tool accuracy is established, and the accuracy failure modes that may occur are analyzed from this, to carry out the accuracy failure sensitivity analysis under the failure mode, and to identify key geometric errors which have a great impact on the machining accuracy reliability of machine tool in the failure mode. Finally, taking a 3-axis machine tool as an example, according to the analysis results, this paper puts forward measures to improve the accuracy reliability and verifies the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In view of the difficult machining characteristics of high volume fraction SiCp/Al composites, this paper researches the grinding force variation of grinding SiCp/Al composites with grinding rod. A diamond grinding rod with a diameter of 3mm is used to grind the SiCp/Al2024 composite with 60% volume fraction by the method of end face grinding. By measuring the tangential grinding forces and normal grinding forces after grinding, the theoretical model of unit grinding force is deduced. According to the experimental parameters of spindle speed, feed rate, and grinding depth, this paper derives the theoretical model of grinding force based on SiCp/Al2024 composites. And it clarifies the influence mechanism of grinding depth and feed rate on grinding force and explores the variation of grinding parameters on grinding force under dry grinding condition. Then the variation rule of grinding component force ratio is obtained. The related research and theoretical model have theoretical guiding significance for exploring the grinding properties of hard-to-machine materials. [ABSTRACT FROM AUTHOR]

STEP-NC is a smart standard, developed by the International Organization for Standardization (ISO) as a substitute for the ISO 6983 G-code, because the language of the G-Code, normally used for computer numerical control (CNC), is qualified as being unable to link the CAD/CAM/CNC digital chain and meet the needs of modern intelligent manufacturing in terms of tractability, interoperability, flexibility, adaptability, and extensibility. The purpose of this work is to implement the new STEP-NC standard in a computer-aided process planning (CAPP) turning process to overcome the shortcomings of ISO 6893 G-code and enable the process to meet the demands of modern manufacturing. Therefore, the first objective of this paper is to design and implement a CAPP for the turning process, designated as CAPP-Turn, to ensure machining of rotational parts within this modern vision. However, to achieve the CAPP-Turn system, it is necessary to build a robust automatic manufacturing feature recognition (AMFR) module to establish full communication between the first two links of the digital chain, the design CAD and manufacturing CAM, by using a hybrid graph-rules method. The second objective of this work is to elaborate a new consistent-fast algorithm that allows one to extract the machining turning entities for parts with the most efficiency and complex geometry. It then becomes necessary to introduce the main machining entities defined within the framework of this standard and to explain the different parameters that are necessary for the unambiguous definition of these entities whether of a geometric, topological, or other nature. In fact, most of the AMFR systems presented in the literature are restricted to the external turning process and cannot handle parts with complex geometry and interacting features. Moreover, the frontal turning features are largely neglected in most of these systems, despite their importance for fulfilling certain functions in mechanical systems. This article first details the global architecture of the CAPP-Turn and clearly describes the interaction between the CAD part and STEP-NC output file. It then explains the model of the AMFR system, which encompasses (i) a parser module that translates geometric and topological data from a STEP AP203 CAD file into Python entity class objects; (ii) an AMFR that analyzes the objects created and applies predefined rules to construct all possible turning machining; and (iii) a module capable of distinguishing external features from internal, frontal features from axial, and handling interacting features from the simple features. After these steps, the AMFR provides all suitable sequencings for part machining. Finally, to demonstrate the potential advantages and power of the proposed AMFR, a selected part is chosen for testing. The result shows that the AMFR performs well in recognizing all types of features regardless of their type: internal or external, axial or frontal, simple or interacting. [ABSTRACT FROM AUTHOR]

Iso-scallop tool path has uniform scallop height (maximum allowed value) between cutter location (CL) points on the adjacent line, which can maximize interval values of tool path and minimize total lengths of tool path. However, iso-scallop tool path generation process is more complicated than common iso-parameter and iso-planar tool path. In order to avoid exceptions happened in offsetting surface or transforming surface to mesh, this paper generates iso-scallop tool path directly on surface. To improve computational efficiency, based on geometric principle of scallop height and iso-scallop CL points, scallop points are iteratively calculated based on a discrete feature point set including the golden section points. An initial CL location is calculated to be closer to the wanted theoretical iso-scallop point. And an adaptive discretization method is proposed to obtain discrete feature points on surface. The minimum distance from feature points to the CL point is calculated iteratively for the wanted iso-scallop CL point. Both scallop points and iso-scallop CL points are calculated iteratively by a small amount of feature points for efficiency improvement. Two examples of typical freeform surface are used to test the presented method. The results indicate that the scallop height of iso-scallop tool path is uniform, and total lengths are shorter than lengths of iso-planar tool path. [ABSTRACT FROM AUTHOR]

CNC machining center linear axis thermal positioning errors, seen as the synthetic consequences of geometric and thermal errors, respectively generated due to the manufacturing and assembling inaccuracies and the asymmetric thermal deformation of the machining center structure, are significantly affected by varying position of the cutting point and shifting state of temperature field. Hence, developing a practical approach to reduce or even to eliminate thermal positioning errors is crucial. This paper proposes an approach to decouple and separate machining center linear axis thermal positioning errors, based on which a highly accurate prediction model of the thermal positioning error is formulated. A sensitivity analysis-based thermal critical point optimization method is presented where grey theory is borrowed to characterize the mapping between thermal positioning error and varying temperature fields, according to which the highly related temperature sensors are derived. The thermal positioning errors are then decoupled and separated into geometric and thermal errors by adopting multiple regression algorithm and linear fitting approach, respectively. Accordingly, the comprehensive thermal positioning error prediction model is constrcuted, based on which the compensation approach is also proposed. Next, the corresponding compensation module is developed within the SIEMENS 840D CNC system to realize the online compensation strategy, providing the engineering applications. Experimental validations are performed on a commercial machining center, where the thermal positioning errors of the Z-axis are measured with the help of a laser interferometer testing kit and a thermal inspection instrument. The data comparisons indicate that the maximum thermal positioning errors of the Z-axis in the cold and warm state are respectively decreased for 87.09 % and 49.87 % after activating the compensation module, which also suggests that the proposed approach is adequate and accurate to decouple and separate the thermal positioning errors. [ABSTRACT FROM AUTHOR]

This paper presents a new and highly efficient voxel modeling method to determine cutter-workpiece engagement (CWE) in three-axis milling. The method voxelizes both the workpiece and milling cutter in a voxel-based machining simulation space. This allows workpiece model update during machining to be done without the intensive intersection calculations between the cutter and workpiece. In addition, unlike the existing methods that update workpiece and compute CWE individually, the updated workpiece model and CWE are computed in an integrated fashion in the present work. This further reduces the involved computational load. At each cutter location, a voxel model of the cutter surface is created first by slicing the cutter surface by the voxel layer boundary planes to obtain a sequence of circles along the cutter axis. A main contribution of this work is the development of a unique fast increment and decrement scheme to voxelize each circle. Workpiece update and CWE determination can then be done simultaneously from the created voxel model of the cutter surface. A series of case studies have been performed to demonstrate the effectiveness of the proposed method. It can be seen that the proposed method is able to compute CWE very efficiently while maintaining accuracy comparable to the specified voxel size. [ABSTRACT FROM AUTHOR]

In this study, analysis and optimization of the machining parameters on the surface roughness in the ball burnishing process of AISI O2 hardened steel have been performed. The initial surface roughness value, ball diameter, burnishing force, burnishing speed, and burnishing feed were considered control factors, and Taguchi's L36 orthogonal array was employed to reduce the number of experiments. The response surface methodology (RSM) was used to develop a mathematical prediction model of the surface roughness in terms of the above parameters and to analyze interactions among the control factors as well. Additionally, analysis of variance (ANOVA) was applied to determine the significance of each burnishing parameter. The gray wolf optimization (GWO) algorithm, a relatively new bio-inspired algorithm, was introduced in the second part of the paper to obtain the optimum control factors of the ball burnishing process. Confirmation experiments were performed to verify identified optimal level of the burnishing parameters and to demonstrate the effectiveness of the GWO algorithm for the optimization of machining problems. [ABSTRACT FROM AUTHOR]

Carbon fiber-reinforced plastic/polymer (CFRP) is a composite material widely used in industries such as aerospace, medical equipment, and sport products. The angle between the cutting and fiber orientations significantly impacts surface quality during the machining process due to CFRP's pronounced anisotropy. An acute angle results in a superior surface finish, whereas an obtuse angle can lead to defects. Milling of CFRP curved surfaces necessitates accurate tool position control, given the fiber orientation changes. This paper introduces a novel tool positioning method for machining CFRP curved surfaces, which considers fiber orientation. This strategy ensures an acute angle between the cutting and fiber orientations at any position, thereby enhancing machining quality. Furthermore, a tool position smoothing method is implemented to eliminate machine traces. Simulations and CFRP machining experiments are conducted to demonstrate the strategy's effectiveness. Microscopic observation and surface roughness tests reveal a 52% reduction in surface roughness of CFRP specimens, with most machining-induced cracks eliminated. [ABSTRACT FROM AUTHOR]

Laser-assisted milling (LAM) is generally regarded as a promising process for machining difficult-to-cut materials. Accurate prediction of workpiece temperature is an essential prerequisite for the configuration of process parameters in LAM. An analytical thermal model concerning the combined effect of multi-heat sources is presented in this paper to predict the workpiece temperature in LAM. To deal with the complex geometry and kinematics problems in the LAM process, the method of heat source discretization and temperature superposition is used in the thermal model. In addition, the influence of material softening caused by laser heating on cutting heat source is considered. A series of LAM experiments are conducted to validate the thermal model. Good agreement between the predicted and measured results indicates the proposed model is effective. Further, the effect of spindle speed and feed per tooth on workpiece temperature is discussed. This work can be applied to optimize process parameters in LAM for reasonable machined surface integrity. [ABSTRACT FROM AUTHOR]

A multi-tolerance constraint-based registration algorithm for near-net-shape blades is proposed for problems of small machining allowance and positioning difficulties of near-net-shape blades. A global registration model based on the tolerance constraint of contour degree and a local registration model based on the constraints of position degree and torsion degree are established under multi-tolerance constraints. The registration algorithm proposed in this paper is verified using actual measurement data of a near-net-shape blade and its nominal model, and the solution process of the registration model is given. The analysis shows that the registration algorithm based on multi-tolerance constraints can effectively improve the registration accuracy of the near-net-shape blade. Registration parameters are optimized to make the out-of-tolerance value to be zero. Finally, based on the registration results, machining experiments are conducted on a near-net-shape blade. The results show that deviations of leading and trailing edge profiles are machined within the required limits. The proposed registration method is effective to improve the machining accuracy. [ABSTRACT FROM AUTHOR]

The geometric errors (GEs) of the spiral bevel gear milling machine will seriously affect the machining accuracy of the tooth surface and need to be compensated. In this paper, an innovative method for compensating the geometric error of CNC gear milling machine is proposed. This method describes the explicit relationship between the motion axis of the machine tool and the geometric errors, realizes the dynamic compensation by tooth, and improves the machining accuracy and machining efficiency of the tooth surface. Firstly, the actual forward kinematics model (FKM) is constructed based on the geometric errors module, and the error tooth surface with GEs is established. Secondly, the corresponding relationship between the machine setting parameters of the spiral bevel gear universal generation machine (UGM) and the CNC machine tool motion axis is given, and then the functional expression between the motion axis with geometric errors and the machine setting parameters is established, which is the inverse kinematics model (IKM). Then, the tooth surface error correction model is established according to the relationship between the machine setting parameters and the tooth surface errors. The compensation amount of the machine setting parameters obtained by the model is introduced into the IKM to obtain the GEs compensation model. Finally, the effectiveness of the geometric error compensation technique is verified by numerical analysis and experiments. The results show that the tooth surface errors, contact stress, and loaded transmission error after geometric errors compensation are significantly reduced, and the contact pattern meets the design requirements, which verifies the feasibility and effectiveness of the geometric errors compensation technology. [ABSTRACT FROM AUTHOR]

There have been high demands of high-quality, highly efficient processing methodologies on "difficult-to-cut" titanium alloys. The current methods for dealing with this kind of materials are mainly mechanical cutting ones. However, because of high processing costs, poor surface qualities, and restrictive machining operations, the costs of mechanical cutting methods are high. Electrical discharge machining (EDM), because of its flexibility, was considered as a supplement. However, serious difficulties arose while machining titanium alloys by EDM. Because of low thermo-conductivity of titanium alloys, the liquid temperature in gap between electrode and workpiece rose quickly after a series of pulse discharges. The high temperature of gap liquid usually led to gap liquid breakdown strength to decline. The consequence was discharging pulses tended out to be stable arc pulses or short pulses, burning workpiece surface and wearing electrode. The machining process became unstable. The low thermal conductivity of titanium alloys was the inherent property which could be hardly changed, and at present, the only way to settle the hard-to-cut problem of machining titanium alloys by EDM was to seek a way to keep gap liquid breakdown strength not go down so fast but still be suitable for effective pulse discharges. To solve this problem, this paper first listed three conditions to be met and analyzed the reasons to affect gap liquid breakdown strength in detail and concluded with three factors, gap distance, amount of chips left in gap, and gap liquid deionization after pulse discharges and then came up with a proposition to the problem. Technically, the proposition was accomplished by constructing a multiple-variable adaptive control system in which gap servo-voltage proportional to gap distance was in charge of discharging extent of pulses, electrode-discharging time decided the amount of chips produced in an electrode discharging cycle, and pulse-off time decided gap liquid deionization after discharges. These variables were timely regulated to keep the liquid breakdown strength suitable for discharging and meanwhile avoiding arcing in machining. The verification test demonstrated that the multivariable control system really helped electrical discharge machining titanium alloys in severe machining situations and proved its usefulness in applications. [ABSTRACT FROM AUTHOR]

Servo matching (SM) controls the contour error (CE) by coordinating the tracking error (TE) of each axis, which is a basic strategy of CE control. However, the five-axis SM approach has not been widely used in engineering because of the long time to compute the modification of the position loop gain (PLG). This paper establishes the function relationship between the five-axis PLG and parts CE based on the Jacobian matrix. An offline PLG fast modification approach for parts CE in five-axis machining was proposed based on the above criteria. Using this approach, five-axis machine tools could modify the PLG of each axis for specific parts to improve contour accuracy. Compared to the traditional approach, the servo control model prediction, forward kinematics transformation (FKT), and tool contour pose search are omitted in the proposed approach. Therefore, the computational load is reduced significantly, computing time is reduced by more than 98%, and average contour accuracy is improved by more than 57%. [ABSTRACT FROM AUTHOR]

Thin-walled parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, residual stress, and other factors. This paper studied the minimization of the failure probability of machining distortion by controlling the uncertainties of inputs. For this, a fuzzy inference model for the machining system was proposed to determine the effects of uncertain factors on the machining distortion errors, which was composed of rule frame and result frame. In the rule frame, machining parameters, outline size, and wall thickness were used as inputs. In the result frame, linear stiffness, cutter path, as well as cutting force were taken as the input parameters. The values of machining distortion were the output, taken into a threshold function. Comprehensive matching was defined to measure the importance of uncertain inputs to outputs. Machining distortion will exceed the specification (failure) with the increase in comprehensive matching. Therefore, the comprehensive matching index evaluates the effects of the uncertainties on the machining distortion and quantifies the effects of given uncertain parameters. Two engineering examples were employed to illustrate the accuracy and efficiency of the proposed approach. It revealed that the comprehensive matching of cutting force to the failure probability of machining distortion was the maximum, 0.040, which was 12 to 13 times greater than that of linear stiffness or cutter path. [ABSTRACT FROM AUTHOR]

Efficient and productive manufacturing of freeform shapes requires a suitable three-dimensional CAD model at the entrance to the CAM system. The paper deals with the impact of NURBS or B-spline CAD model geometric continuity on the accuracy and productivity of 5-axis ball-end milling of freeform surfaces. The relationship between a different order of CAD model geometric continuity and the quality of the toolpath generated in CAM system is analysed and demonstrated on an example of a Blisk blade profile. In order to reveal the effect of CAD geometry on the quality of the machined surface, linear interpolation of cutter location points, i.e. piecewise linear discrete toolpath, is considered. Also, no further smoothing of the toolpath is applied. The distance of the cutter location points is commonly used as the indicator of toolpath quality. In addition, the discrete curvature of a linear discrete toolpath is introduced here, and its dependence on the curvature and continuity of the underlying CAD model is demonstrated. In this paper, it is shown that increasing the order of CAD model geometric continuity significantly eliminates sharp changes in the distance of cutter location points, and smoothes the discrete curvature of the toolpath. Finally, it is experimentally verified that increasing the continuity of the CAD model from G0 to G3, while maintaining the same cutting conditions, leads to an increase in workpiece accuracy and a reduction in machining time, without the need to smooth the toolpath generated in the CAM system. [ABSTRACT FROM AUTHOR]

With the continuous development of the manufacturing industry, the proportion of deep hole machining in mechanical processing is increasing. In 304 stainless steel, as a kind of difficult machining material, its deep hole machining demand is also increasing year by year. Due to the physical characteristics of 304 stainless steel and the uncontrollability of gun drilling, the quality of deep hole processing is relatively poor. In this paper, some experimental studies are carried out to solve this engineering problem. Firstly, by analyzing the structure of the gun head, this paper establishes the stress model of the gun head in the processing process. Then, the accuracy of the theory is verified by experiments. Secondly, this research carried out multiple single-factor drilling experiments to change the front angle of the internal and external edges of the gun drill to control the cutting force in the machining process. Based on the analysis of the test results, the influence of the change of the gun drill's internal and external edge angle on the surface quality of hole wall, chip forming, and hole straightness offset is explored. The results show that the increase in the internal angle of a gun drill can promote the surface roughness of the hole wall and chip forming. And properly increasing the external angle can slow down the straightness offset of the hole. Finally, for the deep hole processing of 304 stainless steel, this paper gives the best edge angle selection of gun drill. [ABSTRACT FROM AUTHOR]

The micro-milling process is one of the most suitable methods for the accurate machining of devices in the micro-domain and is implemented for a wide range of materials. Fine-grained graphite is extensively used as electrode material for micro-electrical discharge machining because of its excellent thermal, chemical, and electrical properties. Dimensional accuracy and form accuracy are among main goals in the micro-milling process of the 3D micro-form electrodes. Among the numerous factors that affect the quality of the machined micro-components, tool deflection is the most significant. This paper investigates the influence of fine-grained graphite type, depth of cut, tool wear, cutting edge radius size, workpiece inclination angle, and machining strategy as control factors on tool deflection. The experimental results presented in this paper provide important insight into tool deflection issues for improving machining accuracy in micro-milling process of graphite electrodes with complex 3D geometry and high aspect ratio. [ABSTRACT FROM AUTHOR]

Machining precision components involves challenging distortion issues that entail high costs and material and energy waste to the industry. In parallel, advanced control of production processes is a rapidly growing field because of its unique capabilities to solve multi-agent nonlinear problems and develop control actions based on knowledge and experience. Despite the several studies carried out on the subject, research keeps fragmenting distortion issues in different niches of components, and comprehensive reviews considering distortion as a cross-cutting technical hitch have never been reported. In this paper, a study compiling recent advances in machining distortion control from a holistic perspective is presented. For the first time, distortion understanding is unified, offering a new perspective, more practical and comprehensive, which includes intelligent systems. This novel way of attaining the research on distortion distinguishes three interconnected pillars: distortion source identification and quantification, distortion simulation model development, and control strategies drafting and application. The paper guides the reader through several distortion investigations of different kinds and provides classifications never addressed in the field with which a profound understanding of the issue can be achieved. Finally, future trends and key enabling technologies to drive the advanced control and minimization of machining distortions are outlined. [ABSTRACT FROM AUTHOR]

Model-based definition (MBD) technology has gained popularity in recent years. It provides an opportunity for a deeper integrated digital design and manufacturing of mechanical products. Automated manufacturability analysis (AMA) could solve increasing production costs and the manufacturing cycle caused by design defects and is the crucial support of concurrent engineering. This paper studies a systematic method for automated manufacturability analysis, which converts the design model to a structuralized feature model (SFM) with machining feature recognition. Furthermore, the machining feature-associated graph (MFAG) is proposed to describe SFM, and the meta-model expression of the knowledge rule is detailed. Finally, AMA knowledge modeling and reasoning methods are introduced. The method proposed in this paper addresses the automated manufacturability analysis of machining parts and provides a reference for MBD application in concurrent engineering. [ABSTRACT FROM AUTHOR]

Electrochemical machining is widely used in biomedical, aerospace, automotive, and other fields. The development of electrochemical machining (ECM) is restricted by many factors such as the difficulty of preparing micro-electrode, large machined gap and serious stray corrosion. In this paper, we present a method to fabricate micro-holes by combining micro-graphite electrode with hollow structure. Firstly, a sealed graphite electrode was proposed to guarantee that the sediment can be fully removed by the flowing electrolyte. Then, the effect of machined parameters such as the pulse frequency, machined voltage, electrode feeding speed, and electrolyte concentration on machining quality was studied. By optimizing the machined parameters, the minimum value of micro-hole taper with 0.07 could be obtained. To improve the shape precision of workpiece, this paper designed the hollow structure inside the electrode. With the increasing of the pulse frequency, the machining accuracy of micro-holes increased. The results suggested that the pulse frequency of 100 kHz, the machined voltage of 18 V, the feeding speed of 1 µm/s, and electrolyte concentration of 5% were more suitable for micro-holes machining with ECM. [ABSTRACT FROM AUTHOR]

Electric pulse-assisted cutting has the advantages of reducing cutting force, improving machined surface quality, and delaying tool wear. It provides a desirable machining method for difficult-to-cut materials with high hardness and low plasticity. Therefore, the research of electric pulse-assisted cutting was summarized in this paper. The mechanism of electric pulse-assisted cutting was introduced firstly. Secondly, the implementation method and experimental equipment of electric pulse-assisted cutting were introduced. Thirdly, the experimental research results of electric pulse-assisted cutting were analyzed. Then, the finite element simulation research of electric pulse-assisted cutting was reviewed, and the method of obtaining the materials constitutive model under the condition of pulse current was introduced. Finally, the problems in the current research were analyzed, and the future research directions were pointed out. It can provide a reference for further research on electric pulse-assisted cutting. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is a widely used non-conventional machining technique in manufacturing industries, capable of accurately machining electrically conductive materials of any hardness and strength. However, to achieve low production costs and minimal machining time, a comprehensive understanding of the EDM system is necessary. Due to the stochastic nature of the process and the numerous variables involved, it can be challenging to develop an analytical model of EDM through theoretical and numerical simulations alone. This paper conducts an extensive review of the various experimental (or empirical) modeling techniques used by researchers over the past two decades, including a geographic and temporal analysis of these approaches. The major methods employed to describe the EDM process include regression, response surface methodology (RSM), fuzzy inference systems (FIS), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS). Additionally, the optimization methods used in conjunction with these methods are also discussed. Although RSM is the most commonly used empirical modeling technique, recent years have seen an increase in the use of ANN for providing the most accurate predictions of EDM process responses. The review of the literature shows that most of the investigations on experimental EDM modeling were conducted in Asia. [ABSTRACT FROM AUTHOR]

The quality of workpieces is affected by many factors, such as machine tool errors, and their machining accuracy needs to be improved. Therefore, an accuracy prediction method based on the attention convolutional long short-term memory neural network (ACLSTM) is proposed in this paper. According to an analysis of the operational data of certain equipment, such as the temperature, the current and the rotational speed of each motion axis of the machine tool, this method completes the prediction of the workpiece grinding accuracy. The experimental results show that the ACLSTM method is able to quickly and accurately predict the actual workpiece size after processing. The result of the proposed method was compared with other conventional regression prediction methods, and the performance of ACLSTM is significantly better than other methods, which can be practically applied to the workpiece size prediction in industrial processing to further control processing quality. [ABSTRACT FROM AUTHOR]

In micromachining, the quasi-intermittent vibration—assisted swing cutting technology alleviates the residual height problem of elliptical vibration–assisted cutting (EVC) and inherits its intermittent machining characteristics. The minimum chip thickness has a significant impact on cutting forces, tool wear, and process stability when working with difficult-to-machine materials. This study thoroughly examines the impact of cutting parameters and tool parameters on the quality of the workpiece during machining in order to better understand the time-varying characteristics of the quasi-intermittent vibration–assisted swing cutting (QVASC) machining process and the size effect on micro-cutting of silicon carbide crystal. This paper created the minimum chip thickness prediction model suited to QVASC machining process. The effects of variables such as cutting velocity and tool inclination on the minimum chip thickness were discussed as well as the scribing tests that were conducted on such challenging materials as silicon carbide. The research findings shown that during the machining process, the critical undeformed chip thickness of silicon carbide decreased continuously as the cutting velocity sequentially increased (1.51 mm/min, 1.88 mm/min, 2.26 mm/min); under the down inclination angle (0–10°), the critical undeformed chip thickness also continuously decreased. These results confirm that cutting too fast reduces the instantaneous undeformed chip thickness, which is not conducive to ductile removal. [ABSTRACT FROM AUTHOR]

This study concentrates on comparing effects of conventional and laser-assisted machining (LAM) of laser-borided Monel 400 alloy. For this purpose, the shaft made from Monel 400 was covered with 200-µm thick boron layer and surface melted using diode laser. For determining the influence of laser beam scanning velocity on final microstructure, microhardness, and depth of melting, four laser beam scanning velocities were set: 5, 6, 8. and 10 m/min. Obtained microstructures are typical for laser-melted metal and are composed of fine crystals. Microhardness increased significantly due to enriching Monel 400 with boron and the level of this increase depends on the laser beam scanning velocity carried out for the process. During both conventional and laser-assisted turning, cutting forces were measured for comparison. Other compared parameters after carrying out these processes were obtained surface roughness and tool life. It was found that it is possible to form laser-borided surface of Monel 400 alloy by machining and using additional laser-assist has a positive impact on the efficiency of the process. Laser-assisted machining allows to lower cutting forces during machining and this leads to extended tool life. On the other hand, this method provides lower values of roughness parameters on the machined surface due to the effect of softening the surface during the process. The paper is the first description of effects obtained by laser-assisted machining of laser-borided surface. [ABSTRACT FROM AUTHOR]

Compact graphite cast iron (CGI) is utilized extensively in the production of vehicle engines because it has numerous thermo-mechanical benefits. The research of CGI is primarily concentrated on cutting experiments, with few theoretical works such as prediction models of cutting forces, particularly under high temperature and large strain circumstances. In this paper, a prediction model is optimized to predict the thrust force and torque of GJV450 (a kind of CGI) at high temperature, large strain and high strain rate. The model employs a calculus method to represent the change of partial working angles during tool machining, as well as an improved constitutive model to account for the flow softening phenomenon and the weak thermal softening effect of the material during the machining process. Drilling experiments and machining simulations are used to verify the model, and the comparison findings show that the developed prediction model and the optimized constitutive model are both applicable. [ABSTRACT FROM AUTHOR]

In order to reduce static deformation and improve machining accuracy, deformation error compensation by offline adjustment of machining parameters is a practical approach in the flank milling process of thin-walled workpieces. Since machining parameters are coupled with cutting force and deformation, iterative methods are required for error compensation. However, current error compensation models lack an in-depth understanding of the compensation process, resulting in low convergence rate of the compensation process. To overcome these problems, a double flexible error compensation model (DFCM) for deformation is proposed in this paper. As the basis of the proposed model, iterations of machining parameters in error compensation are first introduced. After that, the deficiencies of the flexible error compensation model (FCM) for deformation are revealed, and the reasons for its large iteration space and low convergence rate are given. Then, the DFCM is developed from the flexible iteration process of machining parameters and the FCM. The proposed DFCM reveals the actual error compensation process and has significant advantages over the FCM; thus, it converges with the faster rate. Finally, effectiveness and advantages of the DFCM are verified by simulations and experiments of flank milling of thin-walled workpieces. [ABSTRACT FROM AUTHOR]

This paper deals with the concerns of industrial demand for enhancing the machinability of Ti-6Al-4V titanium alloy (TC4). In this regard, the electroplastic effect of high-frequency pulse current can improve the plasticity of the material and reduce the processing resistance of the material. Its application in the processing and manufacturing of difficult-to-machine materials can greatly improve the processing performance. The research compared the effect on turning performance of titanium alloy workpieces after applying 50–400 A (700 Hz) current intensity and 300–1900 Hz (150 A) pulse frequency. Results indicated that the electric pulse–assisted turning (EPT) can promote the plastic flow of grains in the surface layer of Ti-6Al-4V workpiece and then reduce the turning resistance during machining. The residual stress on surface of workpiece after machining can be able to release under thermal effects. In addition, the VB value of tool flank face wear has been reduced in EPT compared to that of conventional turning (CT). It is also found that the change of current intensity has a greater impact in multiple properties during turning process than pulse frequency. [ABSTRACT FROM AUTHOR]

The optimization of machining allowance has an important influence on the machining quality of workpieces. This paper determined an optimization method for rough-finish milling allowance based on depth control of milling affected layer. Firstly, the coupling influence of rough-finish milling cutting depth on milling affected layer depth was studied by experiment. Secondly, the influence rule of rough milling and finish milling on the affected layer depth was studied by experiment, and the prediction model of the milling affected layer depth based on the cutting depth of rough milling and finish milling was established, as well as the surface roughness prediction model of the finish milling cutting depth. Finally, the effectiveness of the optimization results was verified by experiments. The experimental results show the optimized machining parameters can increase the machining efficiency by 31.2%, and the milling affected layer is 91 μm, which indicates that the depth of milling affected layer is effectively controlled. [ABSTRACT FROM AUTHOR]

Due to their greater workspace and lower cost, industrial robots can provide an alternative solution to CNC machine tools for milling of aerospace parts. However, current machining robots fail to meet the required finishing tolerances (0.1 mm) of the aerospace industry. To improve the accuracy of industrial robots during milling, this paper proposes a new feedback method using an off-the-shelf passive CMM-arm. Unlike laser trackers and optical CMMs, CMM-arms are not susceptible to occlusion or line of sight issues, which may arise due to machining debris or robot movements. In this work, the performance and robustness of a CMM-arm used to guide a robotized machining process in optically challenging conditions are studied. First, the kinematic model of the CMM-arm is developed and the shared workspace between the CMM-arm and a KUKA KR300 Ultra industrial robot is analyzed. This ensures adequate reach of the robot's tool center point (TCP) while the CMM-arm is attached to the machining spindle. The CMM-estimated tool center point is then calculated in real time on an external PC based on the combined kinematic chain of the robot and the CMM-arm. The positioning error of the TCP is determined in real time at a rate of 80 Hz, and the corresponding correction signals are sent to the robot via the Robot Sensor Interface (RSI). Experimental results show over 80% reduction in maximum error during 5-axis milling of aluminum. [ABSTRACT FROM AUTHOR]

Linear motion commands of multi-axis computer numerical control (CNC) machine tools need to be smoothed at the transition corners, because the velocity discontinuities at corners can result in fluctuations on machine tool motions and lead to poor surface quality. However, few studies have reported on the transition between non-line contour. The paper proposes a novel two-step local corner smoothing method for non-line contour transition, including path smoothing and feedrate scheduling. In path smoothing, a non-uniform rational B-spline (NURBS) curve is adopted to smooth the adjacent non-line contour of screw rotor section while constraining the cornering error with the set tolerance. The maximum deviation between the original trajectory and smoothed toolpath can be analytically calculated. In feedrate scheduling, a jerk-limited S-curve feedrate scheduling scheme is developed based on machine tool dynamics. Finally, in order to verify the correctness of the proposed method, a series of numerical simulations and actual machining and measurement experiments are conducted, and the simulations and experimental results have showed the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]