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Singularities in five-axis machining are a series of positions where the exact motion of the rotary axis becomes unpredictable or incalculable. In the neighborhood of a singular point, machining may cause unstable movements of drive axes and deteriorative dynamic performance of machine tools. In this paper, we proposed a closed-loop inverse kinematics method to solve accurately the five-axis joints' parameters around singular points. To achieve this purpose, the damped Jacobian pseudoinverse algorithm is introduced and the machine joints' velocities are calculated directly by the velocities of the five-axis cut points. The joints are then obtained by integrating the corresponding velocities. Furthermore, a feedback control method is applied to reduce the integrating error. In the end, simulations and experiments' results demonstrated the effectiveness of the closed-loop inverse kinematics method. [ABSTRACT FROM AUTHOR]

In automated fiber placement (AFP), the abrupt variations of orientations affect layup quality and kinematic performance. This paper proposes a quaternion-based orientation smoothing method constrained by the Domain of Admissible Orientation (DAO) to solve this problem. The DAO is the orientation domain attached to each orientation and defined by the interference avoidance and layup process constraints. All the placement orientations are interpolated into a quaternion curve, and the quaternion curve is smoothed by minimizing its energy function. The energy function is presented in three-dimensional (3D) space, so to facilitate this minimization, the DAO is also transformed into 3D space to constrain the orientation. Then, a spline shortcut generation (SPSG) algorithm is proposed to geometric smooth the curve by generating a spline passing through all DAOs. After that, a Resampling method nonlinearly optimizes the energy function by sampling the curve parameters to redefine the layup orientation within the DAO. Experiments show that the method effectively smooths the orientation trajectory under layup constraints and improves the kinematic performance of APF machines. [ABSTRACT FROM AUTHOR]

For the drilling of micro-holes using micro-EDM technology, it is not easy for debris to be removed from the narrow gap. Aiming at this issue, a relative three-dimensional ultrasonic vibration (RTDUV)-assisted micro-EDM method was proposed to effectively improve the debris removal rate and micro-hole machining performance. The RTDUV consists of circular electrode vibration and vertical workpiece vibration. To better analyze the impact of RTDUV assistance on flow field distribution and debris removal, a flow field simulation was conducted. Micro-hole drilling experiments were conducted to investigate the machining performance with RTDUV assistance. From the simulation results, it is known that RTDUV assistance effectively accelerates debris removal in micro-hole machining, and the debris removal rate with RTDUV assistance is higher than that with the assistances electrode vibration (EV) and workpiece vibration (WV). Compared with the case without vibration assistance, EV, WV, and RTDUV assistances improved the consistency accuracy of the arrayed micro-hole (10 × 10) entrance by 24.8%, 21%, and 33.8% respectively, and the inlets of micro-holes have better morphologies under RTDUV assistance. The machining efficiency when using RTDUV assistance has been improved by 19.5%. The advantage of RTDUV assisted micro-EDM is that the lateral and underside gaps are in an alternating state, which greatly promotes the removal of discharge debris in the discharge gap and increases the renewal rate of the gap dielectric. Consequently, the reduction in processing stability caused by the high concentration of gap debris is avoided to some extent. The proposed novel ultrasonic vibration assistance method is prospective to further enhance the performance of micro-EDM. [ABSTRACT FROM AUTHOR]

Milling force is one of the key output variables related to milling efficiency and accuracy. However, the time-consuming identification of milling forces cannot be evaluated online for machining performance during the milling process. In this paper, a novel millisecond-level method is developed to identify the milling forces online using acceleration signals. Firstly, the milling force identification problem is transformed into an ill-posed problem in the time-domain convolution framework. Next, the advantages of the regularization method for the ill-posed problem are analyzed. Then, the properties of the transfer matrix and the regularization operator are used to reduce the milling force identification time. Lastly, the proposed method is verified with several sets of milling experiments, and the results show that the accuracy of milling force identification is acceptable and the identification time is less than 10 ms. [ABSTRACT FROM AUTHOR]

Ultrasonic vibration–assisted electrical discharge machining (UEDM) was studied to improve the machining performance for TC4 micro-holes. First, a computational fluid dynamic (CFD) model of inter-electrode gap for the micro-hole was established using COMSOL Multiphysics, and the effect of ultrasonic amplitude on the distribution of fluid and debris in the inter-electrode gap was analyzed. The experimental study of UEDM for machining performance of TC4 micro-hole under different machining parameters was conducted by using the controlled variable method. Results showed that the ultrasonic vibration of the electrode is beneficial to improve the machining performance for micro-hole. Under the optimal processing parameters, the material removal rate is improved four times, and RTWR and θ are reduced by about 50%. Finally, the neural network and genetic algorithm were used to predict and optimize the experimental results. The average error is 4.1% between the optimal processing parameters [Xn] obtained by the above method and from the experiment. [ABSTRACT FROM AUTHOR]

In traditional multi-axis machining, the linear tool path generated by computer-aided manufacturing systems is still in a broad tool path format, resulting in discontinuities at the corners, which slow down tool motion, and also trigger tool vibration, reducing machining accuracy and surface quality. In order to ensure the C3 continuity of the tool during the multi-axis machining process, this paper proposes an analytical B-spline local smoothing method with curvature control. The tool position trajectory and tool orientation are both smoothed by a 5th spline curve with 9 control points. Optimal control points are determined for position and orientation splines to achieve C3 continuity at the junctions between the spline and linear segment while preserving the predefined geometric tolerance limits and minimizing the maximum curvature of the inserted B-spline curve. The smoothed tool path is expected to have less curvature fluctuation, which can significantly improve both the kinematic performance of the machine tool and the machining efficiency. Both computer simulations and physical printing experiments are carried out to demonstrate the improvements of our proposed method in motion smoothness and tracking accuracy in multi-axis machining tasks. [ABSTRACT FROM AUTHOR]

Longitudinal ultrasonic vibration-assisted high-speed dry milling (LUVAHSDM) is adopted to improve the cutting performance and surface quality of Ti-6Al-4V (TC4). Multifaceted comparisons are presented in terms of milling force, milling temperature, tool wear, 3D surface roughness, and residual stress, utilizing AlCrN, AlCrSiN, AlTiN, and AlTiSiN-coated tools. The results show that the maximum milling forces in the feed and longitudinal directions both decreased at first and then increased with the increasing of Al element contents, during which the single edge and average flank wear increased. Therefore, the surface roughness of TC4 becomes more uniform using AlTiN-coated tools, and the residual stress is significantly reduced to 98.57 MPa. Combining the results of milling force, milling temperature, tool wear, 3D surface roughness and residual stress data, it is found that AITiN-coated tools achieve excellent cutting performance and surface quality. The excellent machining performance of coated carbide tools demonstrates the feasibility and effectiveness of applying LUVAHSDM methods. [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 design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study. [ABSTRACT FROM AUTHOR]

In modern machine tool design, precision is an important index to characterize machine tool performance and precision allocation has become a key task. Since the middle of the twentieth century, precision allocation methods using optimization technology to balance manufacturing cost and precision level have gradually developed, but most methods mainly take the cost minimization as the goal to optimize the precision allocation. As the precision and manufacturing costs are a pair of factors to be comprehensively considered, balance between them is needed to meet different design requirements. This paper proposes a comprehensive optimization method to trade-off between precision and cost. A multi-object precision allocation optimization model aiming at minimizing fuzzy manufacturing cost and comprehensive precision of machine tool is constructed. A multi-object optimization algorithm to solve the model is designed, combining the multi-objective gray wolf optimization algorithm with multi-objective decision analysis method Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A case study based on a large-scale hobbing machine shows that the comprehensive optimization of manufacturing cost and machining precision is realized by using the proposed multi-object precision allocation optimization method. [ABSTRACT FROM AUTHOR]

In longitudinal vibration-assisted milling (LVAM), the tool flute is retracted from the bottom of the machined surface along the axial/longitudinal direction. Nevertheless, there is still a limitation in the knowledge of LVAM during Ti-6Al-4 V cutting regarding the cutting forces pattern in analysis using finite element (FE) simulations and analytical prediction comparatively. This study developed an FE model of the cutting force using ABAQUS and a mathematical model of the cutting force using MATLAB. The FE model was developed based on the Johnson–Cook plastic deformation and the Johnson–Cook damage model. The analytical prediction was developed based on the fluctuated undeformed axial chip thickness and the influence of the longitudinal vibration on the undeformed chip thickness. According to this comparative study, the analytical prediction of the cutting force coded in MATLAB has remarkable similarity with the FE simulation model cutting force. The experimental cutting forces were also carried out to validate both the FE simulation model and the mathematical model. According to the comparison results in the LVAM, the FE simulation model has higher quantitative error than that in the mathematical model, with the highest error in the mathematical model being about 7.146% and the highest error in the numerical model being about 9.913%. In addition, a comparative study between LVAM and CM in terms of the experimental cutting temperature, machined surface morphology, and chip morphology was also carried out. In evidence, LVAM provides better machining performance than CM. [ABSTRACT FROM AUTHOR]

Machining accuracy is an important index for evaluating the performance of machine tools. To improve the machining accuracy and efficiency of an internal thread grinding machine and reduce the cost, this paper carries out a forward design of the tolerance of the tool motion axis based on the required precision of the workpiece. Using the parameters of a nut raceway, a calculation model of a sand profile under ideal conditions is established under the principle of spiral surface machining and gear meshing. The tolerance criteria are used to evaluate the machining accuracy through multi-body system theory, Monte Carlo simulation, and least squares fitting. Global sensitivity analysis based on variance is used to identify the key factors influencing error. Finally, statistical analysis is completed with the Monte Carlo method to obtain the reliability of machining accuracy. The geometric design is optimized according to reliability to ensure that the tolerance zone meets the required machining accuracy. The feasibility of this method is verified by designing 30 error tolerance zones for an SCS-180 T grinder. [ABSTRACT FROM AUTHOR]

The surface integrity of machined workpiece is an important index to evaluate the service performance of parts. In order to improve the service performance and machining efficiency of titanium alloy, the concept of variable distribution density micro-textured ball end milling cutter is proposed based on texture effect. Firstly, based on the ball end milling model of titanium alloy, a variable density micro-texture model is established in the chip contact region. Secondly, a milling titanium alloy test platform was built to reveal the change law and mechanism of micro-texture parameters on the machined surface integrity. Finally, based on the hierarchical structure weight method and fuzzy comprehensive evaluation theory, the integrity of the machined surface is comprehensively evaluated and the micro-texture parameters are optimized. The results show that the variable distribution density micro-texture has a positive effect on improving the machining quality of titanium alloy. The micro-texture in the bonding zone can reduce the tool-chip contact area, improve the friction state, increase the heat dissipation area, and reduce the plastic deformation of titanium alloy. The micro-texture in the slip zone can improve the chip curl and promote the stability of the system. In addition, reasonable micro-texture parameters can obtain better surface quality of the workpiece. [ABSTRACT FROM AUTHOR]

Ti6Al4V alloy has been widely used in aerospace and medical fields due to its advantages in superb high strength-to-weight ratio and biocompatibility. However, the hard machinability of Ti6Al4V alloy seriously affects the surface integrity and performance of components. This paper adopts a hybrid process of picosecond laser ablation and micro-milling to fabricate a high-quality U-shape groove on a Ti6Al4V alloy surface. Material removal mechanisms under a single micro-milling process and hybrid process are obtained through experiments and simulations. Picosecond laser ablation with material removal is utilized to produce different textures on the workpiece surface prior to the micro-milling process, and the effect of surface textures on the subsequent micro-milling process is revealed. Furthermore, simulation results show that surface textures ablated by picosecond laser can directly reconstruct the axial cutting depth of micro-milling, thus converting continuous chip forming into an intermittent process and effectively reducing the cutting force. This study offers a promising process to effectively improve the machining performance of hard-to-machine materials. [ABSTRACT FROM AUTHOR]

Tungsten alloy has excellent performance and has been widely used in military, aerospace and nuclear energy, and other cutting-edge industries. However, tungsten alloy has large hardness, high strength, and poor plastic deformation ability, which resulted in high cutting force and serious tool wear during the cutting process, leading to low surface quality of the workpiece after molding. Therefore, it is of great significance to strengthen the research on tungsten alloy cutting technology to promote the development of tungsten alloy application. Firstly, the research progress of tungsten alloy cutting process technology has been systematically reviewed, and the current status of cutting parameters optimization, new cutting methods and devices, and cutting fluid technology have been emphatically reviewed. Secondly, the types of tungsten alloy cutting tools, the relevant tool micro-texture, and tool coatings technology have been briefly described, and the composite cutting technology such as cryogenic cutting, electroplastically assisted cutting, and ultrasonic vibration assisted cutting and the effect of cutting performance prediction technology on tungsten alloy machining performance have been summarized. Finally, the development prospect of tungsten alloy cutting technology has been prospected. [ABSTRACT FROM AUTHOR]

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones. [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]

This study explores the application of machine learning algorithms for supporting complex product manufacturing quality through a focus on quality assurance and control. We aim to take advantage of ML technics to solve one of the complex manufacturing problems of the tempered glass manufacturing industry as a first attempt to automate product quality prediction and optimization in this industrial field as an alternative to destructive testing methodologies. The choice of this application field was motivated by the lack of a robust engineering technique to assess the production quality in real time; this arises the need of using advanced smart manufacturing solution as AI to save the extremely high cost of destructive tests. As methodology, this paper investigates the performance of machine learning techniques including Ridge Regression, Linear Regression, Light Gradient Boosting Machine, and Lasso Regression, for predicting the product thermal treatment quality within the selected type of industry. In the first part, we applied the selected machine learning models to a dataset collected manually and made up of the more relevant process parameters of the heating and the quenching process. Evaluating the results of the applied models, based on several performance indicators such as mean absolute error, mean squared error, and r-squared, declared that Ridge Regression was the most accurate model with a mean error of 14.33 which is significantly acceptable in a business point of view and not reachable by any human level experience of prediction. The second part consists of developing a digitalized device connected to the manufacturing process to provide predictions in real time. This device operates as an error-proofing system that sends a reverse signal to the machine in case the prediction shows a non-compliant quality of the current processed product. This study can be expanded to predict the optimal process parameters to use when the predicted values do not meet the desired quality and can advantageously replace the trial-and-error approach that is generally adopted for defining those parameters. The contribution of our work relies on the introduction of a clear methodology (from idea to industrialization) for the design and deployment of an industrial-grade predictive solution within a new field which is the glass transformation. [ABSTRACT FROM AUTHOR]

This paper studies the improvement in machining performance by implementing a hybrid coolant approach for the machining of difficult-to-machine Inconel 625 superalloy. This study differs from the pre-existing hybrid cooling methods by using a novel modified tool holder for the supply of cryogenic coolant. An external nozzle is also involved as part of the coolant system to provide MQL spray to the cutting zone in hybrid cooling. Turning experiments using MQL, cryogenic cooling with an external nozzle, cryogenic cooling with a modified tool holder and hybrid cryo-MQL cooling were performed at different parameter levels to analyze the machining performance of the proposed new cooling technique. The results show that a maximum reduction of 30.52%, 63.67%, and 43.27% was observed for surface roughness, tool wear, and cutting forces using the hybrid cooling technique. The tool wear types and mechanisms underlying the wear formation when using various techniques for cooling are studied using SEM imaging of tooltips. Also, the chip morphology study was carried out to compare the effects on machinability. [ABSTRACT FROM AUTHOR]

A novel porous aggregated cBN (AcBN) wheel was prepared under high-temperature sintering processes to ensure the desired machining efficiency and quality of Ti–6Al–4 V alloys. The designations of pore structures and AcBN grains within metallic grinding wheels operating at the high efficiency deep grinding processes were conducted. Variations of grinding forces and force ratio, grinding temperature, specific grinding energy along with grinding parameters, were carried out. In addition, characterization analyses of grain wear morphologies of wheel surface were then performed with vitrified monocrystalline cBN (McBN) wheels and porous AcBN wheels. Findings show that the employment of Ti-coated cBN particles contributes to the improvement of chemical bonding strength between grains and metallic matrix alloys inside AcBN grains. Compared with the severe grain wear of vitrified McBN wheels, porous AcBN wheels possess the excellent wear resistance ability and desired machining quality, owing to the abundant chip storage space and micro-fracture properties of cBN particles. [ABSTRACT FROM AUTHOR]

In this paper, a novel mask electrochemical additive and subtractive combined manufacturing technique was proposed. This is a machining method at the atomic level, and it can be used to produce metal microstructures with high-profile accuracy and low surface roughness. Due to the accumulation of electric field lines during mask electrochemical deposition, the height of the edges of the microcolumns is usually twice or more than the height of the central position in the deposition plane. A combined machining method based on the electric-field constraint of the mask is thus proposed to improve the accuracy of the profile and its surface roughness. The feasibility of the proposed method was verified by both simulations and experiments. The height difference between the column center and the surrounding layer on the surface of nickel microcolumns was reduced from 13 to 2 µm, and the roughness of the tops of the microcolumns was also improved. Experiments to examine electrolysis leveling were carried out to verify the correctness of the results of the simulations and theoretical calculations. Finally, the parameters were optimized using orthogonal experiments, and an array of nickel microcolumns with a diameter of 200 µm and a height of nearly 50 µm was obtained using these optimal parameters. The profile accuracy and surface roughness of the high-precision microcolumn array were improved by using the mask electrochemical additive and subtractive combined machining technique, and a high-precision microcolumn array structure was manufactured. [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]

The particle size of fixed abrasive pad (FAP) is an important factor affecting its machining performance and surface condition. The surface condition of consolidated FAP is closely related to the tribological characteristics. However, the relationship between tribological characteristics of FAP and surface condition has not been studied in depth. Therefore, the mapping relationship between friction characteristic signal and FAP surface condition is established in this paper, and the influence of FAP abrasive particle size on the friction signal characteristic in the lapping process of quartz glass is explored. Four FAPs (W3.5, W7, W10, and W14) were used for the lapping experiments and tribological experiments on quartz glass, and the friction signals in each processing stage were processed and feature signals were extracted. Moreover, the material removal rate, debris particle size distribution and micro morphology, friction coefficient, surface micro morphology of workpiece, and FAP at each processing stage were analyzed. The experimental results showed that there is a close relationship between energy of wavelet packet frequency band 8 and marginal spectrum amplitude and cutting performance of FAP surface abrasive particles. Their values increase with the increase of the abrasive particle size. There is a close relationship between energy of wavelet packet frequency band 7 and cutting performance of FAP surface matrix. Their value decreases with the increase of abrasive particle size. This study provides a theoretical basis for FAP online monitoring. [ABSTRACT FROM AUTHOR]

As an effective alternative to traditional pouring lubrication method, minimum quantity lubrication (MQL) has been recognized by more and more scholars and applied in factories. However, there are still some technical shortcomings in the application of traditional oil-based MQL technology, such as poor cooling capacity and low lubrication efficiency. Aiming at the technical defects of oil-based MQL, a magnetic field–assisted minimum quantity lubrication (mMQL) technology using Fe3O4 nanofluid as cutting fluid was proposed. First, MQL equipment with controllable magnetic field was built, and the contact angle, surface tension, and dynamic viscosity of Fe3O4 nanofluid droplets were measured under different magnetic induction intensities. Based on these variation parameters of physical properties of the magnetic nanofluid droplets, the influence of magnetic induction on the droplet penetrability was subsequently analyzed. Finally, the heat transfer performance and machining characteristics of the nanofluid droplets under different magnetic inductions were compared and investigated. Results showed that the Fe3O4 nanofluid droplets exhibited lower contact angle and higher viscosity under the influence of magnetic field, thus showing better penetrability and heat transfer performance. Compared with traditional vegetable oil MQL, the cutting temperature, tool wear, and cutting force under mMQL were reduced by ~ 35.5%, ~ 52.4%, and ~ 43.2%, respectively. Under the influence of magnetic field, the contact angle of magnetic droplets was decreased, and the contact area between the droplets and heat transfer surface was larger, which led to the improvement of droplet evaporation heat transfer efficiency. In addition, the penetrability of Fe3O4 nanofluid assisted by magnetic field was improved, more nanofluid could penetrate into the tool–chip contact area, and finally improved its lubrication and cooling efficiency. [ABSTRACT FROM AUTHOR]

In order to ensure the mechanical performance and machining accuracy of the machine tool, the problem of mutual deviations of the machine tool moving parts in the machining process, which causes machining errors and subsequent accuracy prediction difficulties, is solved. Firstly, the structure and motion mechanism of the machine tool are analysed; a static accuracy model of the machine tool machining posture relationship is established using multi-body system theory and coordinate transformation; the measured deviation values are fitted and solved according to the formula; and the geometric error law affecting machining accuracy and the distribution of machining point error values in the machine tool motion space are explored. Then, the response surface method is used to simplify the solution. Finally, the blade is selected as the machined part, and the machining trajectory is extracted for experiments to obtain the error distribution range of the machining surface of the blade. The final experimental results surface, along the Z-directional component, and the integrated average compensation rate reached 42.6% and 89.6%, respectively, verifying the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

Elliptical vibration-assisted cutting (EVC) is a method for rapidly creating ultra-precision free-form surfaces with micro groove features, which have a wide array of industrial applications such as drag reduction, self-cleaning action, optical tuning behavior, etc. The complicated fabrication process and limited shape and deep control of micro groove features by using EVC limit the applications of current EVC process in the fabrication of micro groove. To overcome these limitations and improve overall performance, a layered elliptical vibration-assisted cutting (LEVC) process is presented to fabricate deep micro groove with different shape. First, a detailed layered elliptical vibration-assisted cutting method is described, and a key step, the layered elliptical vibration-assisted tool path planning, is analyzed and established. Then, the machining stress under the proposed LEVC method and layered conventional cutting (LCC) process were simulated and compared through finite element analysis. The simulation results show that the LEVC process can generate the lower residual stress compared to the LCC process. To validate the machining performance of the proposed LEVC process, the machining experiments for two groups of micro grooves under the proposed LEVC process and LCC process are performed. The performance tests and comparison results between the generated micro grooves under the proposed LEVC method and LCC process indicate that the proposed LEVC method is effective for generating micro groove with the controllable shape and depth. The actual experiments show that the machining capability of the developed LEVC method for micro groove is satisfactory. [ABSTRACT FROM AUTHOR]

Machinery condition monitoring is a crucial application to enable condition-based predictive maintenance, realizing smart manufacturing in the context of the Industry 4.0 revolution. Machine learning that is built upon data correlation has recently become mainstream for machinery fault diagnosis. To boost the fault diagnosis performance of the machine learning methods, extracting high-quality fault-related features from raw time-series response signals is the key. In this research, we propose a signal processing-based feature extraction method, the so-called recurrence plot (RP), to evaluate the hidden dynamic characteristics of the vibration signals collected from the machinery system that is subject to fault. To further examine the properties of RP, the recurrence quantification analysis (RQA) is employed to quantitatively measure the patterns in the RP. The main strength of the proposed methodology lies in its excellent capability in handling the nonstationary signals with strong system nonlinearity and ambient noise, thereby yielding high-quality features with a high degree of separation in terms of the fault condition. A novel quantitative metric based on the silhouette is established to thoroughly assess the feature quality, providing systematic guidance for machinery condition monitoring. Comprehensive case studies using publicly accessible gear and bearing fault datasets are carried out to validate the proposed methodology. The enhanced performance of the proposed methodology also is highlighted by comparing it with other benchmark methods. [ABSTRACT FROM AUTHOR]

This study aimed to compare the performance of three machine learning (ML) models, including support vector regression (SVR), random forest (RF), and extreme learning machine (ELM) for kerf width prediction of pulsed laser cutting. Selected features from the optimal base wavelet transformation of vibration signals from the optimal base wavelet selection were adopted as the inputs to the ML models. Averaged kerf width of a straight cut of a 0.1 mm thickness silicon steel sheet was chosen as the output. The performance comparison of three ML models was divided into two stages. In the first stage, the effects of varying the validation data size and data randomness analyses were investigated using training data. In the second stage, the prediction accuracy of these machine learning models on testing data was compared. The results from the first stage revealed that the RF model emerged to be the best model in the validation data size and random state analyses with averaged mean average percentage error (MAPE) scores being of 5.32% and 7.61%, respectively. Compared with the SVR and ELM models, the RF model had the least discrepancy between the MAPE scores, training (2.83%) and testing (1.69%), in the second stage of analysis. This indicates that the selected vibration features from the optimal base wavelet selection combined with the RF model are efficient for forecasting the straight kerf width of the workpiece by pulsed laser cutting. [ABSTRACT FROM AUTHOR]

Rolling bearings are extensively employed in the modern machinery industry and are critical industrial base elements. The machining accuracy of bearing rolling elements has a great impact on the service performance of rolling bearings. In this work, advances in the ultra-precision machining of bearing rolling elements are summarized, and corresponding outlooks are presented. An introduction to the accuracy requirement of the rolling element and the corresponding basic principles are first discussed. Subsequently, advancements in the ultra-precision machining of bearing balls are reviewed and analysed. Meanwhile, research progress on the ultra-precision machining of bearing rollers is also summarized and compared with the investigations on bearing balls. It is demonstrated that due to the absence of an intercomparison effect in the machining process, the batch accuracy of the bearing roller is lower than that of the bearing ball. Furthermore, more attention should be given to the machining accuracy of the crown profile and spherical datum surface of the bearing roller. Finally, in regard to the above academic achievements, conclusions and outlooks on the ultra-precision machining of bearing rolling elements are given. The advances and outlooks summarized in this work are beneficial to both academic scientists and industrial researchers. [ABSTRACT FROM AUTHOR]

The present research work focuses on developing a mechanistic model for the specific grinding energy in UAG process. Based on the developed model, the effect of cutting parameters has been studied. The cutting force plays a significant role in appraising the performance of machining process. The effect of various forces such as tangential, normal, and vibrational forces has been considered during UAG machining process, and a model has been proposed based on these forces. In the UAG process, the grinding wheel is rotated, and the workpiece is simultaneously vibrated with small amplitude and high frequency. The experimental studies revealed that ultrasonic vibration in grinding operations enhanced the overall performance by reducing cutting forces and improving surface integrity, resulting in less energy consumption. The developed model included various parameter effects such as frequency, amplitude, speed of the wheel, table speed, depth of cut, and width of cut. This mathematical model showed favorable outcomes validated with the data obtained from an experimental setup. This model can be utilized for future research in the direction of effective force and specific grinding energy and can contribute towards realizing sustainable grinding studies. [ABSTRACT FROM AUTHOR]

The volumetric positioning accuracy of multi-axis CNC machine tools indicates the deviation from desired to actual position of the tool; it directly influences the machining accuracy of the workpiece, and it is also one of the important indicators to measure the performance of the machine tool. The volumetric positioning accuracy of machine tools is supported to be fundamentally improved. The accuracy design methods for machine tools currently include a robust design method for machining accuracy and static geometric accuracy. The robust design method for machining accuracy is set up based on the effective evaluation of the volumetric positioning accuracy of machine tools, and the geometric error elements are used as the analysis variables without directly directing the tolerance design. The joint surface tolerance of key components is used as the analysis variable in the design method for static geometric accuracy, which is supported to be optimized according to the accuracy design requirements of different machine tools. The present paper summarizes and analyzes the aspects including the volumetric accuracy modeling, identification of geometric error elements for axis of motion, robust design method for machining accuracy, tolerance modeling, and design method for static geometric accuracy of machine tools, and analyzes the key problems to be solved in the method of improving the volumetric positioning accuracy of multi-axis CNC machine tools, and then a feasible research idea to improve the volumetric positioning accuracy of multi-axis CNC machine tools is proposed. [ABSTRACT FROM AUTHOR]

Coordinate measuring machines (CMMs) are massively exploited as measuring tools in the modern manufacturing industry. The performance of these machines, in terms of accuracy, has been considerably improved in recent years by using quasi-static errors compensation. Considering the shorter cycle times required during measurement tasks, CMMs are to be operated at high measuring velocity. In such measuring conditions, dynamic errors have a critical impact on measuring accuracy. Consequently, dynamic errors assessment, modeling, and compensation are needed to improve the overall CMM metrological performances. In this paper, a comprehensive predictive modeling strategy for dynamic error compensation is developed and applied successfully. The main measuring parameters that influence CMM dynamic performance are identified and used in a systematic experimental investigation. The positioning accuracy is then evaluated concerning dynamic conditions using a high-precision laser interferometer. Based on the experimental results, neural network models are built according to a structured modeling procedure inspired by the Taguchi method. Improved statistical analysis tools and performance measurement criteria are used to extract the most appropriate variables and conditions leading to well-founded predictive modeling. The resulting models are implemented on a bridge-type CMM to compensate for both geometric and dynamic errors. The results demonstrate a reduction of more than 80% of dynamic errors. This demonstrates that the compensation of dynamic-induced errors using high-speed measurement is achieved leading to shorter cycle times of measurement tasks while maintaining high accuracy measurements. [ABSTRACT FROM AUTHOR]

In the present scenario, citizens' concern about environment preservation creates a necessity to mature more ecological and energy-efficient manufacturing processes and materials. The usage of glass fiber reinforced polymer (GFRP) is one of the emerging materials to replace the traditional metallic alloys in the automotive and aircraft industries. However, it has been comprehended to arise a sustainable substitute to conventional emulsion-based coolants in machining processes for dropping the destructive effects on the ecosystem without degrading the machining performance. So, in this study, the comparison of the two sustainable cutting fluid approaches, i.e., dry and LCO2, has been presented based on machining performance indicators like temperature, modulus of cutting force, tool wear, surface roughness, power consumption, and life cycle assessment (LCA) analysis for end milling of GFRP. The cutting condition of LCO2 has been found to be superior in terms of machining performance by providing 80% of lower cutting zone temperature, tool wear, 5% lower modulus of cutting force, and reduced surface roughness with 9% lower power consumption that has been observed in the case of LCO2 in comparison with dry machining. However, to compress the CO2 for converting in liquid form, a higher amount of energy and natural resources is consumed resulting in a higher impact on the environment in comparison with dry machining. Considering the 18 impact categories of ReCiPe midpoint (H) 2016, 95% higher values of impacts have been observed in the case of LCO2 in comparison with dry machining. [ABSTRACT FROM AUTHOR]

Tool wear has been a great impact on machining quality and machining efficiency during cutting. The serious tool wear will even lead to workpiece failure and catastrophic equipment failure. Accurate and effective tool wear monitoring is important to evaluate the degree of tool wear, replace tools in time, and promote the intelligent development of the manufacturing industry. To improve the accuracy of online prediction of tool wear, a new method based on whale optimization algorithm (WOA) optimized support vector machine (SVM) is proposed to predict the tool wear. Specifically, the multi-domain features of cutting force and vibration signals are extracted based on the time domain, frequency domain, and time–frequency domain, and the signal sensitive features closely related to tool wear are selected by the Pearson correlation coefficient method. SVM is applied to predict the evolution of tool wear. WOA is used to improve prediction accuracy by optimizing the internal parameters of SVM. By learning the nonlinear correlation between sensitive features and tool wear, a model for predicting tool wear based on WOA-SVM is constructed to predict the change of tool wear value. The effectiveness and prediction performance of the proposed method are verified by milling experiments. Results show that this method can predict tool wear value based on limited historical data information accurately and effectively. Compared with SVM prediction methods optimized by some common optimization algorithms (particle swarm optimization (PSO) and genetic algorithm (GA)), the prediction accuracy and stability are higher and the generalization is stronger. These findings may be of great significance for the improvement of machining quality and efficiency of parts, the stable operation of manufacturing system, and the intelligent development of manufacturing industry. [ABSTRACT FROM AUTHOR]

To improve the machining performance of different processing materials, a three-excitation ultrasonic spatial vibration-assisted turning system is proposed, which realizes the non-unity of the plane where the cutting trajectory of the tool is located. The influence and formation law of three-excitation ultrasonic spatial vibration-assisted turning on the surface roughness of the workpiece under different vibration parameters (amplitude) and machining parameters (cutting speed, cutting depth, and feed) were analyzed by response surface methodology. The results show that in terms of vibration parameters, the influence of ultrasonic vibration applied in the horizontal direction on surface roughness is significantly greater than that of ultrasonic vibration applied in the vertical direction, while the feed has the greatest influence on surface roughness, followed by cutting speed. The surface roughness of common turning, one-dimensional ultrasonic vibration-assisted turning, ultrasonic elliptical vibration-assisted turning, and three-excitation ultrasonic spatial vibration-assisted turning was theoretically analyzed and experimentally compared. The results show that compared with the other three turning methods, the three-excitation ultrasonic spatial vibration-assisted turning can obtain a lower surface roughness and have good machinability. [ABSTRACT FROM AUTHOR]

At present, solar energy is the key development of clean energy. Diamond wire sawing (DWS) is the most used processing method of solar crystalline silicon cell wafer. In recent years, the application of crystalline silicon cutting that can be applied by single wire has gradually turned to that of EDWS. However, the research on the equipment and process requirements of endless diamond wire is limited, and the mastery of the equipment factors affecting the performance of EDWS is not perfect. This paper takes the influence of some constituent factors of EDWS equipment on processing performance as the research object; focuses on the analysis of the wire length, wire brand, guide wheel diameter, cutting wheel span, driving wheel position, and wheel number of the EDWS system; and finds out the deep causes and correlation of the influencing factors. It provides a basis for giving full play to the performance of endless wire sawing from the equipment composition. [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]

Diamond abrasive belt has been widely used for ultra-precision grinding and polishing of hard-brittle materials due to its excellent rigid-flex compound characteristics. Nevertheless, severe wear affects its machining performance for plastic difficult-to-machine materials. Therefore, a series of experimental research are carried out to investigate the abrasive wear of electroplated diamond belt in grinding nickel-based superalloys in this paper. The height variation of diamond grits measured by a super depth-of-field microscope is defined as the evaluation indicator of wear degree. Orthogonal experiments are conducted to obtain the dominant influential process parameters of abrasive belt wear height. The single-factor experiments of the main influential parameters grinding speed and normal force are performed to furtherly explore the relationship between abrasive belt wear and process parameters as well as state parameters. The results show that increased grinding speed and normal force contribute to higher grinding temperature and more severe belt wear. Consistently, the change tendency of the material removal and surface roughness corresponds to the wear degree. Additionally, electron microscope detection, Raman scattering, and energy dispersive spectroscopy were used to explore wear mechanism more deeply. The results reveal that various granularity mainly affect the physical wear modes subdivided into abrasion, fracture, and pull-off. What's more, graphitization transition of the diamond grit peaks and oxidative wear indicate that chemical wear is also one of the main forms of abrasive grit wear. [ABSTRACT FROM AUTHOR]

As the most promising carbon fiber reinforced plastic (CFRP) hole making method, orbital drilling is widely concerned. This paper aims to understand the influence of the cutting parameters, tool diameters and ratio between milling and drilling ( R m & d ) on machinability in CFRP orbital drilling. The effects of cutting parameters on thrust force and cutting temperature were studied by orthogonal experiments, and experiments were performed to investigate the variations of tool diameters, ratio between drilling and milling on thrust force, cutting temperature, tool wear and machining quality. The experimental results show that the spindle speed and axial feed rate have apparent effects on thrust force, and the tangential feed rate appreciably impacts on the cutting temperature. The selection of tool diameter and the R m & d has specific influence on tool wear, machining quality and cutting temperature. The result is helpful for selecting cutting parameters and tool diameters for high quality hole machining in CFRP orbital drilling. [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]

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]

The Ni-base single crystal superalloy DD5 is commonly employed in critical parts of turbine blades and turbine disks in aeronautical engines. To improve the surface machining quality, single-factor experiments were carried out to investigate the changes of grinding force, surface morphology, and chip morphology under different grinding conditions (dry, conventional flood, minimum quantity lubrication). The experiments showed that the conventional flood lubrication and minimum quantity lubrication (MQL) could enormously reduce the surface roughness, grinding force, and work-hardened layer thickness of DD5. At the depth of 5 ~ 15 μm, the DD5 subsurface microhardness decreased sharply with the increase of depth, and the microhardness under different grinding conditions was in the order of dry grinding, conventional flood, and MQL. However, at the depth of 20 ~ 150 μm, the DD5 subsurface microhardness tended to equilibrate and fluctuate around 540 HV. In addition, compared with dry grinding, the degree of chip serration was obvious in flood or MQL environment, and its chip formation frequency fluctuated less and chip formation was stable. Within the experimental parameters, the MQL grinding machining method obtained the lowest surface roughness and the highest surface quality with the lowest associated cost, which is the ideal grinding and cooling lubrication method for the Ni-base single crystal superalloy. [ABSTRACT FROM AUTHOR]

Characterized by a complex contour profile involving arc, cycloid, and involute, the screw rotor is usually manufactured by a forming tool. The finished surface quality and efficiency of the screw rotor are determined by the cutting performance of the forming tool. However, the machining performance of the forming tool is closely related to the structure shape of cutting edge, which is then determined by the installation parameters of the forming tool. Therefore, to make the cutting performance of forming tool controllable, it is essential to investigate the relationship between the cutting performance of the forming tool and its installation parameters at the design stage. In this paper, a novel installation parameter optimization design method of forming tool for screw rotor is presented. A parametric optimization program is designed to finalize the range of installation parameters satisfying the spatial meshing relation and machining equipment parameters. The profile characteristics of forming tool under different center distances and mounting angles have been investigated. For validation, several screw rotors were ground in experiments and the resulted profile errors were analyzed. The results show that the cost of precision grinding of screw rotor can be significantly reduced, without compromise of machining quality. As such, the proposed design method could serve as a promising platform to facilitate screw rotor manufacture. [ABSTRACT FROM AUTHOR]

The vibration in the milling process plays a key role in machining, which can significantly affect the machining quality of the workpiece. Some vibrations have negative influences on the workpiece surface, while other vibrations can improve machining stability. Therefore, it is critical to distinguish the influence of different types of vibration on machining quality. A simulation method of undeformed chip thickness considering process vibration is presented in this article, in which a finite element model is established to analyze the dynamic milling process of 7075-T651 aluminum alloy from the aspects of cutting force and temperature. A series of experiments are carried out to verify the effectiveness of the simulation model, and the results show that the proposed model is accurate in predicting both milling force and temperature. Furthermore, the effect of milling vibration on machining performance is studied with the proposed method, in which the relationship between the amplitude-frequency characteristics of vibration and milling force-temperature fluctuation is revealed. The results show that the proposed method can determine the influence of milling vibration and provide a basis for distinguishing favorable and unfavorable vibration parameters of machining quality in milling. [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]

The present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density. [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]

Anti-abrasion thin-film-coated tool is well known for its enhanced micro machining performances. However, coating increases tool edge radius, which spurs additional ploughing and rubbing. Therefore, selecting appropriate thin-film thickness and suitable abrasion-resistant coating material for micro tool is necessary to reduce friction and size effects together. To meet these objectives, first, single-layer TiAlN coating having various thin-film thicknesses has been deposited on uncoated micro end mills by PVD process. By analyzing the cutting force, surface quality and edge radius of both fresh and worn tools in micro milling of P-20 steel, appropriate thin-film thickness has been found to be ≈ 1 μm. Next, single layer TiN and diamond-like-carbon (DLC) coating of thickness ≈ 1 μm have been coated on uncoated WC tools. Then coefficient of friction (COF) and hardness of all coated and uncoated surfaces are assessed. Finally, the performance of all the coated and uncoated tools have been evaluated analytically and experimentally by analyzing dynamic stability and machinability, respectively. All the coated tools manifested enhancement in performance by uplifting stability limit and reducing tool wear, cutting forces, surface roughness and burr heights compared to the uncoated tool. Owing to the least COF, the DLC-coated tool exhibited the best performance by uplifting stability limit by 23.37% and reducing cutting force, surface roughness and burr height by 27.35%, 67.7%, and 30.58%, respectively. However, for a long machining length (1200 mm), the DLC-coated tool could not exhibit such performance as compared to TiAlN-coated tool due to significant delamination. [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]

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]