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Nowadays, with the increasing demands for quality and precision in microelectronic packaging, the study and prediction of droplet parameters before packaging have become increasingly important. This paper aims to derive a predictive formula for droplet volume dispensing to improve packaging quality, optimize process parameters, reduce resource waste, enhance process control, and enhance product quality and reliability. Based on the needle transfer method for ultra-microdroplet dispensing, a droplet dispensing device that meets packaging requirements is established, and precise control of droplet volume is achieved by changing the factors that affect droplet formation. The droplets obtained from experiments are analyzed using pixel extraction, and a numerical fitting method is employed to establish a droplet volume model. It is found that the pixel method is a practical approach for determining droplet volume, with an average deviation of approximately 1.85%. By conducting droplet dispensing experiments with varying initial distances, the smallest droplet volume achieved is 24.33 pL, and it is concluded that larger initial distances result in smaller droplet volumes and reduced droplet contact area. Based on the droplet dispensing experimental data obtained by varying the initial distance, mathematical predictive models for droplet volume and droplet contact area are derived. The results show that the average difference rate between predicted and actual volumes is 0.51%, and the average difference rate between predicted and actual contact areas is 0.56%. [ABSTRACT FROM AUTHOR]

This paper introduces a prediction model based on machine learning techniques for dimensional control in the manufacturing process of side flange bearing housings, according to the technical standard DIN 31693. The process is implemented in a journal-bearing manufacturing industry positioned among the three brands with the highest participation in the international market in 2023. The manufacturing process consists of rigid machining processes composed of a universal horizontal machining center and dimensional control composed of a coordinate measuring machine. After machining, the part is measured, and its dimensional report is generated. Qualified professionals use deviations obtained from this report to support the decision-making. The method used is based on the holistic monitoring of the surface geometry of the machined part. The approach used to compensate for dimensional deviations is based on monitoring and modeling the total deviation. In this context, the effects of all sources of systematic errors are compensated regardless of their origin. The heuristic is used for the steps that make up the decision-making process. The way to implement the predictive model in the production line is based on the interaction between human and machine experience. This paper proposes using the regression decision trees for defining the displacement parameters of the machining center axes from the dimensional results of housings obtained in the coordinate measuring machine. The model is validated if the mean absolute error is less than or equal to 0.003 mm. A comparison between an assembled model is performed to verify the performance between different predictive models. [ABSTRACT FROM AUTHOR]

Machining time and energy consumption are common process planning evaluation metrics. However, it is difficult to obtain accurate machining time and energy consumption values in the process planning stage, which may result in non-optimal obtaining process routes. To solve these problems, this paper proposes a flexible process planning method based on processing time and energy consumption prediction models. The historical processing data is analyzed, and a prediction model of machining time and energy consumption based on a radial basis function neural network is proposed. Considering the flexibility of multiple processes, a mathematical model of the flexible process planning problem based on the machining time and energy consumption prediction model is established. A multi-objective algorithm using a multidimensional real number coding method is proposed for problem-solving with the completion time and energy consumption minimization as the optimization objective. Two algorithms with better multi-objective optimization performance, the strength Pareto evolution algorithm 2, and the dominance ranking genetic algorithm II are selected for comparative analysis to obtain a better effect of optimization. The better algorithm is selected with the best parameters. Finally, case studies are conducted to verify the validity of the prediction model and the flexible process planning model. [ABSTRACT FROM AUTHOR]

Traditional on-line surface roughness prediction models are mainly established by surrogate models, which can achieve well prediction accuracies with a fixed tool-workpiece combination. However, a poor prediction accuracy comes to an established model when the tool or workpiece is changed. Then, multiple experiments are required to obtain sufficient new data to establish a new prediction model, increasing the time and economy costs. This paper proposes a data-driven method using transfer learning for on-line classifying the surface roughness under multiple milling conditions. First, a source tool is selected to perform the milling experiments to construct the source data. A stack sparse autoencoder (SSAE) is pre-trained to online classify the surface roughness, where the inputs are the machining parameters and the features derived from the force signals in time and frequency domains. Then, a new tool is selected to perform the milling experiments under fewer milling conditions to construct the target data. The pre-trained SSAE are fine-tuned by re-training the network using the limited target data. Finally, a surface roughness classifier of the target tool is established to adapt to the new milling conditions. Furthermore, a detailed experimental validation is carried out on three different tools of a vertical machining center, indicating a significant potential in establishing an accurate surface roughness classifier with limited milling experiments. [ABSTRACT FROM AUTHOR]

Precise prediction of milling force is a crucial issue in multi-axis milling, which is important for evaluating the tool life and surface quality. In this paper, an improved force model for ball-end milling is proposed based on a novel uncut chip thickness (UCT) model as well as the consideration of edge effect and size effect. The computational model of UCT takes into account the influence of lead and tilt angles in ball-end milling. In addition, it utilizes a new discretization method of the uncut chip area that ensures the local cutting edge is perpendicular to the length of the elemental uncut chip area, making it more compatible with the classical oblique cutting model. For each elemental cutting edge, the force components are calculated through a purely analytical approach, considering the shear force, edge force, and size effect caused by cutting edge geometry in milling process. The proposed model is comprehensively validated with both FEM and multi-axis milling experiments for aluminum and titanium. The results indicate that the proposed model can predict the dynamic milling force under various cutting conditions in a good manner. Moreover, considering edge and size effects can further improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

Integral blisk blades are typical complex curved structures with small thickness, low stiffness, and complex boundaries. Due to the high material removal rate during the machining process, they are prone to force-induced deformation, resulting in significant machining errors. This seriously contradicts the requirements for high design accuracy and surface accuracy in the aerospace industry. Therefore, accurately predicting the distribution pattern of deformation caused by dynamic milling forces during the machining process and proposing reasonable error compensation strategies are urgent issues to be solved in the manufacturing of integral blisk blade. Firstly, this paper establishes a prediction model for the deformation distribution during the milling process of the integral blisk blade. Secondly, based on the finite element deformation prediction model of blades, the selection of blade milling processing methods and parameters was completed with the goal of minimizing machining deformation. Finally, a reverse reconstruction geometric modeling compensation strategy is proposed, and the tool path program containing the deformation error compensation amount is regenerated. The prediction model and compensation strategy proposed in this paper were validated through milling experiments and profile accuracy measurement experiments on a certain aeroengine integral blisk blade. The results showed that the prediction model has high reliability, with an average error of 7.96%; the new compensation strategy can reduce the tool-yielding error to within the tolerance range. This study will provide technical support for improving the machining accuracy and efficiency of integral blisk blade. [ABSTRACT FROM AUTHOR]

Composite strips combine the advantages of each component, and the thickness of each layer directly determines whether it can meet the special requirements of different environments and service conditions. Prediction of rolling force and layer thickness is very important for process control and product performance. There are many complex strong coupling variables in bimetal clad rolling (CR), and it is difficult to build a concise mathematical model suitable for production. The idea of equivalent clad rolling (ECR) is creatively proposed and the corresponding relationship between CR and ECR is established. By analyzing the deformation zone of each metal in the CR process, the equivalent roll radius and equivalent deformation length of each layer are established, the calculation methods of improved Stone and Bland-Ford-Hill rolling force applicable to the bimetal CR process are presented, the calculation connection bridge of equal rolling force is proposed, the coupling iteration relationships of flattening radius and rolling force are established through adjusting the reduction rate of each layer, and then the prediction model of layer thickness and rolling force in bimetal CR is developed. The ECR analytical model is verified by finite element simulation and related CR experiments. Using the proposed model, the relationships between the layer thicknesses before and after CR are established, and the effects of different process parameters such as reduction rate, inlet clad ratio, total inlet thickness, friction factor ratio on outlet clad ratio and the rolling force are expounded. Based on the mature single-strip rolling calculation model, the proposed ECR prediction model is short in calculation time and convenient in application. [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]

Inkjet 3D printing using array nozzles allows efficient deposition of complex shapes on free-form surfaces. However, the slip of droplets on curved substrates and the flow of printing materials can cause edge collapse and poor surface morphology of curved multilayer printed parts. In this study, a part morphology prediction model for curved inkjet 3D printing is developed to predict the surface morphology of printed parts. First, from the perspective of computational fluid dynamics, a surface drop prediction model is formed to predict the slip and deposition patterns of ink droplets on curved substrates. The model calculates the spreading diameter of droplets and slip distance on curved surfaces based on the physical parameters of droplets, substrate roughness, and droplet impact angle. Then, based on the droplet slip distance and spreading diameter data obtained from the droplet drop prediction model, a multilayer stacking model for surface printing is established to predict the evolution of part height and contour shape in printing. After discretizing the print area, the morphology prediction of curved inkjet 3D printed parts is achieved by analytically modeling the physical processes such as droplet deposition, local ink flow, and solvent volatilization. Finally, multilayer printing experiments on a spherical surface are conducted to verify the accuracy of the prediction model. The results show that the predicted shape of the sample parts is in good agreement with the experimental results. The model provides a theoretical basis for the closed-loop control of surface inkjet 3D printing and printing defect compensation. [ABSTRACT FROM AUTHOR]

Multipass abrasive waterjet (AWJ) milling is preferred for achieving high material removal, surface quality, and the local form of the target feature. However, the lack of generic models for the kerf generated in multipass erosion for realizing complex features limits AWJ's penetration into the industry. The kerf formed in multipass erosion involves a complex physical phenomenon, which is influenced by the jet characteristics and the way of material removal (MR). This work proposes a generic kerf cross-section profile (CP) prediction model in multipass milling on Ti-6Al-4 V alloy. The proposed approach integrates the jet flow dynamics (JFD) model with the analytical-based MR model, along with understanding the complex physical phenomenon involved in kerf formation towards realizing a comprehensive model. It considers the abrasive's kinetic power change with effective jet divergence, effective standoff distance, local particle impact angles made with the previous kerf, and the dynamic jet-material property, under varying jet traverse rate (Vf) and the number of passes (N). The proposed model was assessed for its generalization capability by comparing the predicted CPs against experimental ones under varying Vf and N. The results show that the evaluated abrasive kinetic power distribution obtained from the JFD model resembles the inverted kerf CPs. The comprehensive model predicted the CPs with a maximum mean absolute error of 64.5 μm and the centreline depth with a maximum error of 2.2% under the variation of Vf (1000–5000 mm/min) and N (1–9). The modelled kerf CPs conformed with the experimental ones with a 0.97 correlation coefficient. [ABSTRACT FROM AUTHOR]

Thermal error compensation of high-speed motorized spindle is one of the effective methods to improve the machining accuracy of CNC machine tools. In this paper, the variation law of the thermal error of the motorized spindle is studied, a method for selecting temperature sensitive points is proposed, and the thermal error prediction model is established. First, the temperature and thermal error of the motorized spindle at different speeds are measured by thermal error experiment. Secondly, the clustering by fast search and find of density peaks (CFSFDP) is used to solve the problem that the optimal cluster number is unknown in the process of selecting temperature sensitive points, and the temperature sensitive points under multi speed of the motorized spindle are obtained. Finally, the adaptive boundary harris hawk algorithm (ABHHO) is used to optimize the hyperparameters of the least squares support vector machine (LSSVM), and a prediction model for the thermal error based on ABHHO-LSSVM is established, which improves the prediction accuracy. The proposed method and model provide a technical basis for thermal error compensation of motorized spindle. [ABSTRACT FROM AUTHOR]

Multi-material additive manufacturing (MMAM) offers new opportunities to realize components with more integrated features and functionalities in reduced manufacturing costs by eliminating assembly processes. However, the weak mechanical bond between different materials often results in unexpected weakness sections that reside around the multi-material boundary interface. Thus, strengthening the boundary interface is critical to enabling the wide application of MMAM processes in production. Our work approaches this challenge by introducing a new virtual prototyping method to strengthen MMAM parts by facilitating the design and planning process. In our work, a computational part strength prediction model is built, and this model is used to quickly and realistically predict the mechanical strength of a part design within the context of its manufacturing plan. This enables fast iteration of redesigns to create parts that can be directly printed with improved strength. Compared to the commonly used design of experiment-based approaches, this new virtual prototyping method offers a more time and cost-efficient solution that delivers better designs in a shorter design cycle and with no material wastage by eliminating the need for physical test printing. [ABSTRACT FROM AUTHOR]

Ambient temperature control and active internal cooling can effectively reduce the impact of spindle thermal error. At the same time, the residual spindle thermal error fluctuates violently, which makes it difficult to predict the thermal error accurately. To combat this issue, experiments to identify the spindle thermal error with constant and variable spindle speed spectrums were designed. A deep learning model is proposed for thermal error prediction, wherein speed parameter is incorporated as an input variable alongside temperature variables. The proposed model adopts a 1D convolutional neural network-gated recurrent unit-attention (1DCNN-GRU-Attention) architecture. Within this model, the 1DCNN module is responsible for managing the highly collinear input data and extracting spatial information, followed by the GRU module which extracts temporal information. Furthermore, the attention mechanism of the model can re-weight the learned features. Ultimately, the regression layer is responsible for predicting thermal error. To verify the proposed model's effectiveness, it was compared against seven other thermal error prediction models. The experimental results revealed that the convolutional module of the proposed model is highly effective and can replace the traditional temperature-sensitive point selection (TPS) method. Under multiple coupled factors, the proposed method achieved a prediction accuracy of 81.53%, with root-mean-square error (RMSE) lower than the traditional method by 40%. [ABSTRACT FROM AUTHOR]

The die steel NAK80 is used in specular optical molds, deep drawing forming dies, and cold extrusion dies in large quantities; high strength and hardness often induce tool wear during machining. This study established a tool wear prediction method for measuring, using the cutting temperature and chip chromaticity characteristic values to predict the tool life. The back propagation neural network (BP-LM) was compared with a long-short term memory (LSTM) model in the prediction method, and different characteristic signals were imported into the BP-LM and LSTM methods to predict the tool wear. In Taylor's curve diagram, the repeatability accuracies of tool wear and cutting temperature are 2.83% and 9.29%, respectively. The BP-LM method was used for prediction in the comparison of prediction methods. When the input characteristic were temperature, chip chromaticity, and temperature and chip chromaticity, the MAPE percentage errors are 24.23%, 31.87%, and 19.88%, respectively. The error was reduced by 29% when the input characteristics were temperature and chip chromaticity. When the LSTM model was used for prediction, and the input characteristics were temperature, chip chromaticity, and temperature and chip chromaticity, the MAPE percentage errors are 30.33%, 28.55%, and 22.1%, respectively. The error was reduced by 25% when the input characteristics were temperature and chip chromaticity. Therefore, using the characteristic temperature and chip chromaticity in the BP-LM and LSTM prediction models resulted in good forecast accuracy, and a new model prediction form for tool life was provided. [ABSTRACT FROM AUTHOR]

In order to improve the prediction accuracy of low rigidity inclined rib milling force model in the milling process of hollow thin-walled structural parts, the static deformation of low rigidity inclined rib was solved by means of numerical analysis and finite element simulation method; moreover, the deformation mixed prediction model was established by combining general regression neural network with fruit fly optimization algorithm and cross validation algorithm to efficiently predict the dynamic deformation of low rigidity inclined rib under different parameters. Thereafter, based on this model, the dynamic feed per tooth solving model was established to predict the dynamic milling force, and it was verified by experiments. The results show that under the processing conditions of 6000 r/min, 0.14 mm/z, and 4 mm cutting width, the theoretical values of the maximum milling forces in X, Y, Z directions at the right diagonal rib position are 361 N, 788 N, and 229 N, respectively, and the experimental values are 398 N, 802 N, and 218 N, respectively. Under the processing conditions of 7000 r/min, 0.14 mm/z, and 3.5 mm cutting width, the theoretical values of the maximum milling forces in X, Y, Z directions at the right diagonal rib position are 361 N, 788 N, and 229 N, respectively, and the experimental values are 398 N, 802 N, and 218 N, respectively. The increase of cutting width leads to the increase of Y-direction force, besides, the increase of feed speed will lead to the increase of X-direction forces and Z-direction forces. The range of X-direction prediction error rate is 8.45–16.15%, Y-direction prediction error rate is 1.50–4.85%, Z-direction prediction error rate is 10.5–15.65%, and the milling force prediction error is kept below 16%. [ABSTRACT FROM AUTHOR]

Due to gravity, a change in the welding position will significantly affect the flow of the molten pool, thus affecting the weld bead shape. An all-position welding process of low-power pulsed laser-MAG (metal active gas) hybrid welding was proposed to improve the welding efficiency and quality of all-position welding. This paper used the response surface method (RSM) to develop the weld bead geometry mathematical model. The analysis of variance confirmed the significance and accuracy of the model. The results show that the models can accurately predict the weld geometry, percentage errors for any models are less than 8.1%. Then, the effects of welding parameters and welding position on weld width, weld reinforcement, and weld penetration were analyzed. Finally, the optimal welding parameters at five positions of 0°, 45°, 90°, 135°, and 180° were obtained by numerical optimization with RSM. The study results can provide a reference for applying laser-MAG hybrid welding in all-position welding. [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]

The uniformity of the machining results in the rough polishing of optical components that determines the length of the subsequent fine polishing cycle. In this paper, full-calibre polishing of large-calibre flat optical components using the motion mode of the ring pendulum double-sided polisher is studied. The processing motion model of the ring pendulum-type double-sided polisher is built to predict the processing results. Based on the model predictions, the simulated machining experiment was carried out to analyze the experimental results. The processing characteristics of different-sized polishing discs are used to optimize the polishing process. Experiments show that the optimization scheme can effectively improve the uniformity of the machined surface. [ABSTRACT FROM AUTHOR]

Tangential ultrasonic vibration-assisted grinding gear (TUVAGG) is a new promising process combining the ultrasonic vibration with conventional grinding gear (CGG). In this study, an assessment of grinding forces from the point of theoretical and experimental was provided in TUVAGG, revealing its mechanism. Therefore, a prediction model of abrasive grain force was established based on the micro-removal mechanism of materials under ultrasonic vibration. First, the motion characteristics of a single abrasive grain were investigated in TUVAGG. Then, taking into account the two stages of the material removal mechanism, the grinding force model of the plowing stage and cutting stage is determined. Finally, TUVAGG experiments were performed to verify the developed model and to determine that the predicted results coincide with the experimental ones. The grinding force decreases with the increase in spindle speed and ultrasonic amplitude, but increases with the increase in feed speed and radial grinding depth. The prediction and experimental results show that the maximum difference between normal and tangential grinding forces is 12.63 and 14.82%, respectively. The developed model provides a deeper insight into the TUVAGG mechanism and a reference for practical technical applications. [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]

This paper presents an analytical model for identifying process damping caused by interference between tool and workpiece during the cutting process. In contrast to the traditional discrete method, the model identifies the interference area between tool and workpiece using a complete analytical method. The obtained interference area can achieve the accuracy of the result obtained by using a shorter discrete step, but the time required for the model is shorter than that of the discrete method and is unaffected by the discrete step. The influence of tool wear band on the process damping coefficient is also considered. The process damping coefficient is identified based on the interference area obtained by the complete analytical model, and the damping coefficient of the non-linear process is linearized by the energy method. Finally, the cutting stability lobe diagram is drawn considering the influence of process damping, and the cutting stability region is predicted. Compared with the lobe diagram without process damping, it is found that process damping can significantly improve the cutting stability in low-speed cutting, and a series of cutting tests are carried out to verify the correctness of the prediction model. [ABSTRACT FROM AUTHOR]

In order to realize real-time and precise monitoring of the tool wear in the milling process, this paper presents a tool wear predictive model based on the stacked multilayer denoising autoencoders (SMDAE) technique, the particle swarm optimization with an adaptive learning strategy (PSO-ALS), and the least squares support vector machine (LSSVM). Cutting force and vibration information are adopted as the monitoring signals. Three steps make up the unique feature extraction and fusion method: multi-domain features extraction, principal component analysis (PCA)-based dimension reduction, and SMDAE-based dimension increment. As a novel feature representation learning approach, the SMDAE technique is utilized to fuse the PCA-based fusion features to enrich the effective information by increasing the dimension, thus helping polish up the predictive performance of the proposed model. PSO-ALS is used to obtain the optimal parameters for LSSVM, simplifying the problem and increasing the population diversity. Twelve sets of milling experiments are conducted to demonstrate the reliable performance of the proposed model. The experimental results show that the presented model is superior to models such as PSO-LSSVM in predictive performance, and the SMDAE technique effectively improves the prediction accuracy of the established model. The findings of this paper offer theoretical guidelines for monitoring milling tool wear in real industrial situations. [ABSTRACT FROM AUTHOR]

Complex curved thin-walled structures, typically produced by multi-axis milling, are highly susceptible to deformation induced by residual stress. It is, therefore, that there is a considerable amount of research on developing predictive models for machining-induced residual stress. However, these developed models for residual stress prediction mainly focus on turning and three-axis milling. In the current study, a hybrid model combining experimental results and a finite element (FE) model is established to predict the residual stress profile of Ti-6Al-4 V titanium alloy for multi-axis milling. The residual stress profile is fitted by using the hyperbolic tangent function with the firefly algorithm (FA) based on the simulation and experiment. The R2 values change from 85.3 to 99.1% in the σx direction and change from 80.7 to 98.1% in the σy direction, which indicates a high fitting accuracy. The radial basis function (RBF) neural network is employed to establish the association between the process parameters and the model coefficients. Thus, the residual stress profile can be expressed by the cutting speed, feed rate, and inclination angle. And the prediction accuracy is verified to achieve 92.7% and 91.4% in the σx and σy directions, respectively. The effects of the cutting speed, feed rate, and inclination angle on surface residual stress and response depth are investigated. The findings demonstrate a strong nonlinear connection between process parameters and surface residual stress. The proposed hybrid prediction model of residual stress can be used for further machining optimization of complex curved thin-walled structures. [ABSTRACT FROM AUTHOR]

The transformation of manufacturing industry to green manufacturing is one of the important tasks to achieve the carbon peaking and carbon neutrality goals, which needs to improve the use efficiency of unit carbon emission. In order to describe the processing state in real time and improve the accuracy of carbon emission prediction, a dynamic prediction and simulation model of carbon efficiency based on digital twin was proposed. First, the dynamic characteristics of carbon emission during hobbing process was analyzed, and three carbon efficiency targets were defined to assess carbon emissions from processing processes. Then, a dynamic prediction and simulation model of carbon emissions was constructed based on convolutional neural network and dynamic discrete event system specification. On this basis, the framework of the carbon efficiency digital twin (CEDT) of the hobbing process was built, and the dynamic prediction and simulation models were integrated into CEDT as virtual models. The application in hobbing process showed that the presented model has higher accuracy in carbon emission prediction. The root-mean-square error, mean absolute error, and mean absolute percentage error of the real-time power prediction were reduced by 43.98%, 34.55%, and 30.67% on average, compared with the traditional method. Meanwhile, the validity of CEDT was verified and the effect of dynamic parameters on carbon efficiency was discussed. [ABSTRACT FROM AUTHOR]

Flatness prediction is a critical technical concern in flatness feedforward control during strip cold rolling. This work realized a high-precision prediction of flatness for strip cold rolling by the data-driven and industrial Internet of Things (IIoT) technology and provided an effective mode for industrial data utilization. A flatness prediction model based on general regression neural network (GRNN) optimized by the differential evolutionary (DE) algorithm was proposed; an intact dataset was established by collecting data from the hot rolling and cold rolling production lines by developing a cross-process IIoT platform, and the proposed model and other common data-driven models are trained and tested based on that. The experiment results obtained based on a dataset with 50,000 samples show that the proposed model is feasible and can achieve accurate prediction of flatness during the strip cold rolling, and compared with the BP and SVM model, it has a better performance. [ABSTRACT FROM AUTHOR]

Corner is one of the typical characteristics of aerospace monolithic components. In order to achieve a stable processing environment and reduce the mutation of milling force in the process of machining, the manufacturer has been searching a method to control the milling force of corner machining. Based on the cutting contact relationship and the instantaneous chip thickness, the milling force prediction model of corner machining is established. By analyzing different types of corner contact relationship, the influence of cutting contact angle and cutting arc length on milling force is established. The corner feed rate iterative calculation method is used to improve the corner milling force mutation. The corner milling experiment proves that the proposed method can not only effectively control the corner milling force, but also improve the surface quality of the bottom surface of the workpiece. The proposed method is suitable for automatic programming of multi-corner pocket machining and provides theoretical support for stable machining of frame part. [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]

It is difficult to measure the surface roughness of large-pitch internal threads; predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in the scope of use. The homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using the Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and radial basis function neural networks (RBF-NNs) were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. It was verified that the absolute error of the SVM model was less than 0.05 μm, and the relative error was less than 4% for turning both left and right threaded surfaces, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness is difficult to measure due to structural characteristics. [ABSTRACT FROM AUTHOR]

The micro structures milled on Ti6Al4V tubes are so narrow that to be damaged in deburring by conventional methods. In this study, a deburring method for the top burrs on micro slots of Ti6Al4V tubes was proposed. The ultra-small-grinding (USG) tool was applied to grind the roots of top burrs for deburring. The method can replace the big milling burrs with the small grinding burrs. A prediction model of the grinding burr size considering workpiece shape and tool deflection was proposed. The ideas of volume balance and energy balance were applied in modeling. Corresponding experiments were conducted with ultra-small-grinding (USG) tools with diameters ~ 100 µm. The effectiveness of the method and the prediction model were proved by the experimental results. The varieties of the grinding burrs in different deburring parameters were studied. The size of grinding burr increase with the increasing of the feeding velocity. The proper deburring parameters are 1–3 mm/min feeding velocity and 10 µm axial cutting depth. The morphologies of the grinding burrs can be divided into four types, which are continuous stacked tiny burr, continuous stacked small burr, discontinuous stacked modern burr, and discontinuous single big burr. The grinding burrs along the side of micro slot can be divided into four kinds of contours, which are smooth contour, rippled contour, wavy contour, and surge contour. The grinding burrs along the sides of micro slots were stacked in the form of laminated tiles, and some strip structures were on the surface of the grinding burrs. [ABSTRACT FROM AUTHOR]

This paper proposes an online prediction method for the roundness of grinding workpieces based on vibration signals. Vibration sensors are used to collect vibration signals during grinding, and wavelet packet denoising is used to preprocess original signals to obtain effective vibration signals. Then use time domain analysis and frequency domain analysis to extract features and normalize them to form feature vectors. The roundness of the finished workpiece is measured using a shape-measuring instrument and integrated with the feature vectors to generate a usable data set. The support vector machine (SVM) algorithm is implemented using A Library for Support Vector Machines (LIBSVM), and a prediction model is constructed. Use the data set to train the model and evaluate the accuracy of the model to verify the effectiveness of the model. The results show that the prediction accuracy of the prediction method can reach 92.86%, and it can better predict whether the roundness is qualified. [ABSTRACT FROM AUTHOR]

Drilling is one of the most classical machining operations. Real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a tool wear assessment and life prediction model based on image processing and deep learning methods, which works effectively for small sample datasets and for low-quality images. The normal areas and worn areas of the drill bits are extracted using the U-Net network and traditional image processing methods, respectively. Moreover, the original dataset is classified using the migration learning technique. The wear level of a drill bit can be accurately evaluated through experimental tests. Testing results show that the proposed method is more convenient and efficient than previous methods using manual measurements. These results can be applied to real-time drill wear monitoring, thus reducing part damage caused by tool wear. [ABSTRACT FROM AUTHOR]

Accurate prediction of the machining quality such as the surface roughness is one of the main objectives of the intelligent manufacturing research. In this study, we investigate the feasibility of combining milling stability analysis and a back propagation (BP) neural network model to predict the surface roughness of aerospace aluminum alloy 7075Al in high-speed precision milling. The difference between the critical depth of cut obtained from the milling stability lobe diagram (SLD) and the actual depth of cut (termed the "chatter stability feature") was used as a critical input variable of the neural network model, thereby improving its prediction performance. It is demonstrated that the proposed chatter stability feature has a strong correlation with the surface roughness, making it an information-rich input feature of the prediction model. The experimental results show that the prediction accuracy can be improved by 7.8% compared with a neural network model that only uses the cutting parameters (i.e., spindle speed, feed rate, depth of cut, and feed rate per tooth) as predictors. In addition, univariate and multivariate sensitivity analysis results suggest that the performance of the proposed approach is robust to errors in the SLD measurements. Compared to conventional methods which only consider the cutting parameters, an improvement in prediction accuracy can be expected with up to 10% errors in modal parameters. [ABSTRACT FROM AUTHOR]

The number of materials processed via additive manufacturing (AM) technologies has rapidly increased over the past decade. As of these emerging technologies, electron beam powder bed fusion (EB-PBF) process is becoming an enabling technology to manufacture complex-shaped components made of thermal-cracking sensitive materials, such as AISI H13 hot-work tool steel. In this process, a proper combination of process parameters should be employed to produce dense parts. Therefore, one of the first steps in the EB-PBF part production is to perform the process parameter optimization procedure. However, the conventional procedure that includes the image analysis of the cross-section of several as-built samples is time-consuming and costly. Hence, a new model is introduced in this work to find the best combination of EB-PBF process parameters concisely and cost-effectively. A correlation between the surface topography, the internal porosity, and the process parameters is established. The correlation between the internal porosity and the melting process parameters has been described by a high robust model (R2adj = 0.91) as well as the correlation of topography parameters and melting process parameters (R2adj = 0.77–0.96). Finally, a robust and information-rich prediction model for evaluating the internal porosity is proposed (R2adj = 0.95) based on in situ surface topography characterization and process parameters. The information-rich prediction model allows obtaining more robust and representative model, yielding an improvement of about 4% with respect to the process parameter-based model. The model is experimentally validated showing adequate performances, with a RMSE of 2% on the predicted porosity. This result can support process and quality control designers in optimizing resource usage towards zero-defect manufacturing by reducing scraps and waste from destructive quality controls and reworks. [ABSTRACT FROM AUTHOR]

The functional behaviors and service performances of aeronautic gears are significantly determined by its surface integrity. Vibratory polishing, as one of the most promising ultra-high-precision finishing approaches for free-form surfaces, shows strong capability to improve the surface integrity of aeronautic gears. In order to gain a scientific insight into the surface generation, a three-dimensional predictive model is proposed in this study for the vibratory polishing surfaces of aeronautic gear considering the initial workpiece topography. The single abrasive particle is regarded as a micro-tool and the removed material is quantitatively determined based on the calculation of Brinell hardness. Then, the predictive model of the surface micro-topography can be developed on the basis of the initial workpiece topography and the interaction between workpiece and abrasive particles. The vibratory polishing tests are further carried out to verify the validity of the proposed surface topography prediction model. The results indicate that the predicted three-dimensional surface roughness agrees well with that of the measured data with an average error of 6.35%. Finally, parametric analysis is conducted to investigate the influence of polishing time, surface hardness, and abrasive particle diameter on the surface parameters. The preliminary work presented in this study is not only helpful to lay a scientific foundation for the more in-depth understanding of the surface generation and material removal mechanism in vibratory polishing, but also meaningful to provide an engineering guidance for the prediction of surface quality and optimization of machining parameters. [ABSTRACT FROM AUTHOR]

It is essential to improve the absolute position accuracy of industrial robot milling systems. In this paper, a method based on an incremental extreme learning machine model (IELM) is proposed to improve the positioning accuracy of the robot. An extreme learning machine optimized by the improved sparrow search algorithm (ISSA) to predict the positioning errors of an industrial robot. The predicted errors are used to achieve compensation for the target points in the robot's workspace. The IELM model has good fitting and predictive power and can be fine-tuned by adding fewer samples. Combined with an offline feed-forward compensation method, the solution was validated on the milling industrial robot KUKA KR160. The method has been validated on a KUKA KR160 industrial robot, and experimental results show that after compensation; the absolute positioning error of the milling robot is improved by 86%, from 1.074 to 0.154 mm. After fine-tuning the industrial robot's error prediction model using a small number of measurement points once the robot had moved to a new machining position, experimental results showed that the average absolute positioning error of the robot's end-effector was reduced by 70.76%, from 1.71 before compensation to 0.5 mm after compensation. [ABSTRACT FROM AUTHOR]

Incremental sheet forming (ISF) is an emerging flexible manufacturing technique. However, it is currently facing the challenge of popularization in industry since the poor geometric precision of the finished parts cannot meet the engineering standard. Aiming at enhancing product geometric precision, online path planning algorithms had been proposed by researchers. However, the existing algorithms had been applied successfully in manufacturing products with very simple geometries only and had limited capabilities of being generalized to complex products, which prevented the widespread application of ISF. To solve this long-lasting problem, more powerful and generic path planning algorithm needs to be proposed for the ISF process. In this study, an adaptive MPC-based path planning algorithm was developed for the ISF process, aiming at enhancing the geometric accuracies of products with more complex geometric features. The algorithm was formulated based on the state-space modelling of the process, the learning-based parameter identification approach, and quadratic programming optimization. The performance and the generality of the present adaptive MPC algorithm were experimentally validated in manufacturing parts with several complex geometric features that were typical in industrial parts using closed-loop ISF processes. In the first test, the present adaptive MPC algorithm showed a significant performance improvement of 56% in the reduction of maximum error on the base compared to the non-adaptive MPC. In the other two tests, the present algorithm also received satisfactory performances. Compared to the non-adaptive algorithm, the test results indicated that the present adaptive algorithm had a better performance and a higher generality. [ABSTRACT FROM AUTHOR]

The violent vibrations generated during turning large-pitch screws seriously affect the tool wear, which reduces the cutting edge profile stability and affects the surface quality of the threads. We propose a new SVM-based design method for cutting edge profile stability of large-pitch thread turning tool considering the influence of vibration, and the model has also been validated experimentally. The simulation model of cutting process considering the influence of cutting vibration was established by simulating tool trajectory, and the influence of edge parameters and tool structure parameters on cutting edge profile stability was analyzed. Then, the data obtained from finite element simulation are used for training to establish the prediction model of cutting edge profile stability based on SVM. The average errors of cutting force, cutting temperature, and tool wear are 3.14%, 2.33%, and 3.91%, respectively, which proves the effectiveness of the prediction model. Furthermore, the optimization objective functions of blunt and chamfered edges were established, and the cutting parameters and tool structure parameters were optimized based on Artificial Bee Colony Algorithm. Results indicate that cutting edge profile-stabilized tool can be obtained by this method. The study builds a theoretical basis for suppressing vibration during the cutting process and provides technical assistance for tool design of different cutting edges. [ABSTRACT FROM AUTHOR]

Aiming to further improving the calculation accuracy of rolling force in the FGC process of tandem cold rolling, the exit thickness accuracy and exit flatness accuracy of the strip in the first few coils after the gauge changing rolling process, a rolling force prediction method based on IQGA-WNN ensemble learning is proposed in this paper in light of the large change of strip parameters and the unstable quality of coils during the FGC process. Firstly, the traditional QGA is improved by quantum variation to avoid falling into local optimal solution. Secondly, the improved QGA is used to optimize the initial parameters of the network to improve the prediction ability of WNN. Finally, the WNNs improved by IQGA are used as the base learners for ensemble learning and are effectively integrated through bagging algorithm to further improve the prediction ability of the model. The rolling force prediction model proposed in this paper is tested on a 1450-mm five-stand tandem cold rolling production line. The results show that, compared with the traditional rolling force model, the separate WNN and IQGA-WNN models, for the first three coils of strip after FGC rolling, the ensemble learning model obtains the minimum rolling force calculation error and strip exit thickness and flatness deviation, the accuracy and stability of the rolling process are improved significantly, which proves the feasibility and effectiveness of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

In the assembly process of aeroengine, the aeroengines have been rejected at pass-off test for high vibration of high-pressure rotor under the condition of meeting the geometric accuracy. Repeated disassembly and trial assembly will lead to interface damage, which will affect the safety and reliability of the engine. Therefore, a multi-objective collaborative optimization process method for geometric accuracy and dynamic performance is put forward based on the practical engineering problems. In the proposed method, the primal problem firstly reveals the transfer mechanism of coaxiality and unbalance of rotor system, and the prediction model of the coaxiality and unbalance of the rotor system was established. Then, combined with the high-precision reduced-order model of aeroengine high-pressure combined rotor, the system vibration responses under different assembly states are obtained. Finally, a multi-objective collaborative optimization assembly process method considering coaxiality, unbalance and vibration response is obtained. The results show that the collaborative optimization process method considering assembly accuracy and dynamic performance is the optimal assembly strategy, which can reduce the vibration amplitude of the key nodes of the rotor system while ensuring high assembly accuracy and achieve the low-level vibration of the rotor system. [ABSTRACT FROM AUTHOR]

In fused deposition modeling (FDM), the prediction and optimization of surface roughness distribution by varying the process parameters of the printing is required during the process planning stage. During this stage, the traditional screening design, such as fractional factorial design, is commonly used to identify the process parameters that have the most considerable effect on process outcomes. The screening design is followed by further experiments, including only essential process parameters to develop prediction models and identify the optimal process parameters. Recently developed custom design approaches such as I-optimal design and definitive screening design (DSD) eliminate the need for follow-up experiments by identifying the critical process parameters and optimum process conditions in a single experimental design. Therefore, the present study is intended to compare the performance of I-optimal design and DSD in terms of prediction and optimization of average surface roughness (Ra) of fused deposition modeling (FDM) printed parts from poly lactic acid (PLA). The following process parameters were considered to reduce the prediction error of Ra models: layer thickness, number of contours, infill density, raster angle, printing speed, extrusion temperature, bed temperature, and built orientation. The results revealed that regression models based on the I-optimal design are saturated and complex, with 38 terms (including eight main effects, twenty three interaction effect, and seven quadratic effects) in the final model. However, the DSD model has 12 model terms (including seven main effects, two interaction effects, and three quadratic effects), making it unsaturated and less complex compared to the I-optimal design. Both models have comparable prediction accuracy based on validation tests. Finally, Ra is optimized based on the desirability function. The results of the present study will add knowledge to the existing literature about the performance of I-optimal and DSD methods in predicting and optimizing responses. [ABSTRACT FROM AUTHOR]

Abrasive belt grinding is a commonly used machining technique for many key components, especially on aerospace equipment. The surface roughness results from a belt grinding process are essential for the fatigue performance of workpieces; however, little research was reported on the surface topography generation of abrasive belt grinding. This study proposed a modeling and simulation method for the surface topography of abrasive belt grinding considering its elastic contact. Abrasive grain cloud forming tool was established combining standard grains number with Gaussian distribution. Workpiece surface and tool trajectory were discretized based on Brinell theory, which considered the microscopic cutting effects. Besides, Hertz theory is used to calculate the elastic deformation of rubber contact wheel with the selection of active abrasive grains and redistribution of forces in contact area; as a result, the interaction between multiple abrasive grains and workpiece could be simulated. Moreover, to verify the proposed method, numerical simulations and abrasive belt grinding experiments were conducted and compared. The results show that the minimum profile gap and error can reach 0.003 mm and 1.03% in terms of material removal depth and surface profile, and the error calculation is deeply affected by the denominator (simulation results). The grinding surface texture simulated by the model agrees well with the experimental observations, and the average predicting error of surface roughness is 3.25%. [ABSTRACT FROM AUTHOR]

The prediction accuracy of the rolling force is crucial for the strip hot rolling process, which will significantly affect the dimensional control accuracy of the strip product. The rolling force prediction models used in actual strip hot rolling production before are mainly analytical models and simple neural network models, but with the improvement of product quality, these models can no longer meet the requirements of high-end product production. A new online model based on the gradient boosting decision tree (GBDT) method is proposed to improve the accuracy of the online prediction of rolling force, in which the random forest method based on feature importance is adopted to select feature parameters. A model self-training function was developed in the control system to ensure the accuracy of the model used online. By comparing various machine learning methods, the results show that the rolling force prediction model proposed by the GBDT is better than that based on other regression methods. The established model has been successfully applied to predict the rolling force for the finishing rolling in a 2250 mm strip hot rolling production line. Compared with the traditional model, the rolling force prediction accuracy and thickness control accuracy are significantly improved. [ABSTRACT FROM AUTHOR]

Predicting the penetration depth during electron beam welding (EBW) is important, but the accuracy of current predictive models is highly varied, depending on the type and number of data used. This paper develops and compares several penetration depth prediction models for EBW and uniquely compares the influence of the number and type of data used, as well as the measurement and modelling methods. Although accelerating voltage, beam current and welding speed data are essential modelling inputs, additional data for beam focal position and beam shape, measured using a novel 4-slit beam probing method, greatly improve the accuracy of predictions for models based on an empirical equation, a second-order regression and an artificial neural network (ANN). Optimised models predict weld depths that deviate, on average, by less than 5% from measured depths, are valid for very broad linear electron beam power density ranges (86–324 J/mm) and are close to the estimated 4% inherent variability in the process and its measurement. Within this linear electron beam power density range, the ANN yields accurate and reliable depth predictions, demanding as few as 36 welding trials, decreasing the number required for models that do not consider beam focal position and shape, for the same targeted accuracy, by more than 60%. Adding large volumes of virtual data generated by less reliable analytical or regression models did not improve the predictive capability for the ANN developed in this study. [ABSTRACT FROM AUTHOR]

Existing researches on coated tools do not provide predicted data about machining performance while exploring their changing rules. Meanwhile, the traditional cutting process parameters neither guarantees the surface quality of the 30CrMnSiNi2A nor attains high material removal rate (MRR). Accurate control and prediction of workpiece three-dimensional surface roughness (Sq) and specific cutting energy consumption (SCEC) play an important role in improving the quality, reducing the cost of workpieces, and improving the processing efficiency. In this paper, according to the new SCEC geometric calculation approach and the influence of measuring position on Sq, the SCEC and Sq values can be accurately obtained. Then, based on the idea of the fitting formula, the influence of cutting parameters on SCEC and Sq in high-speed dry (HSD) milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts is analyzed. Finally, the SCEC and Sq prediction models considering coating type, cutting speed, feed per tooth, and cutting width are established by using the XGBoost algorithm. The R2 values of SCEC and Sq are 0.92465 and 0.91527, respectively, indicating that the model has a good prediction effect on experimental data. The feasibility of HSD milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts is verified by analyzing SCEC, Sq, and cutting temperature, which provides an experimental basis for high efficiency and high precision machining of 30CrMnSiNi2A steel. [ABSTRACT FROM AUTHOR]

In order to establish a more accurate prediction model of turning forces, this paper proposed an analytical model for cylindrical turning with the consideration of the effect of the main cutting edge angle and the nose radius. Meanwhile, the unequal division shear zone theory in orthogonal free cutting is extended and applied to the oblique non-free cutting in the interaction between the chip units. To take into account the real tool nose geometry, the tool nose involved in the cutting is discretized into a series of cutting edge units. The geometrical parameters associated with the cutting edge units are analysed by using the coordinate transformation approach. Then, the improved oblique cutting model is applied to each cutting edge unit to acquire the component forces along the tool rake face. Finally, the resultant cutting forces in the turning process are calculated by the numerical integration method. To verify the effectiveness of the proposed model, the turning force experiment of 304 stainless steel was carried out by changing the cutting conditions, the main cutting edge angle, and the nose radius. Through the comparative analysis between the measured results and the calculation values of the proposed model, it was found that the analytical prediction of cutting force is in good agreement with the experiment. [ABSTRACT FROM AUTHOR]

Abrasive waterjet (AWJ) cutting technology is widely used for nonconventional cold machining of ductile or brittle materials in various manufacturing fields. However, this technology has a significant limitation in terms of the effective depth of cut. As a solution, in this study, a model for the effective depth of cut is proposed based on the Gaussian distribution of the cut profile curve. The proposed model is used to investigate the dynamic evolution of the depth of cut and its influence mechanism. First, a prediction model for the effective depth of cut is established using the material removal mechanism and single particle erosion theory. In addition, an experimental analysis is conducted to improve and optimize the cutting performance of the AWJ considering the effect of machining parameters on (i) the effective depth of cut and (ii) cutting performance. The key factors that affect the geometric profile characteristics of the kerf are investigated, and the optimal parameters for achieving the effective depth of cut model are further determined. According to the AWJ cutting performance experiments conducted by using Ti-6Al-4 V, the prediction model is strongly correlated with the experimental data, and the average difference between the prediction model and experimental results is 6.46% of the effective depth of cut. Notably, this model can predict the effective depth of a cut for different cutting parameters. Furthermore, it exhibits significant industrial value by expanding the application fields of finishing machining using AWJ technology. [ABSTRACT FROM AUTHOR]

The previous research on machined surface topography in milling processes usually focuses on simple and single machining features such as free flat and form surfaces. However, the industrial components are composed of complex machining features such as slots and grooves finished by profile milling cutters. The formation mechanism and prediction method of machined surface topography for the complex milling features are required in industrial applications. Firstly, the machined side surface topography formation mechanism in profile milling straight slot machined by single-pass processing with a solid end mill is presented. Then, the numerical prediction models for machined side surface roughness in straight slot profile end milling are proposed. The proposed model can accurately predict the surface topography, and the relative prediction errors of the surface roughness (Sa) are within 6.34% for the whole cases in this research. Finally, the effect of cutting parameters on the residual heights of the machined side surface is analyzed. The formation mechanisms of machined two side surface topographies on the straight slot are distinct, for which one side surface is machined by up milling while another is by down milling. It is shown that the different tool trajectories cause the distinction for milling each slot side. The machined side surface topography can be controlled by selecting optimized tool motion parameters and cutting parameters. The influences of tool deflection and tool wear on the surface topography are ignored in the current research, which will be considered in the future. [ABSTRACT FROM AUTHOR]

Residual compressive stress can effectively improve fatigue the life of aerospace thin-walled parts. In this study, residual compressive stress control is taken as the target. Firstly, a surface residual stress prediction model is proposed, which considers both machining parameters and milling force heat. The model of the relationship between milling force, thermal load, and residual stress is established, which quantifies the effects of mechanical and thermal loads on the formation of residual compressive stresses. The results show that the feed rate of each tooth and the cutting-edge radius play an important role in the residual compressive stress of the milling surface. The prediction models of surface residual stress for thermal load, mechanical load, feed per tooth, and radius are established. Secondly, the ratio α of the feed rate per tooth fz to the cutting-edge radius r is quantified. When αxx = 0.42–0.65 and αyy = 0.36–0.7, the surface residual compressive stress in the x and y directions of the workpiece reaches the maximum value. Thus, the ratio of the feed rate per tooth fz to the cutting-edge radius r is optimized to control the mechanical and thermal load quantization. It realizes active control of residual compressive stress on the workpiece surface. [ABSTRACT FROM AUTHOR]

The flexible circuit pattern is the key component of flexible electronics. A near-field electrohydrodynamic direct-writing (NFEDW) method was proposed to fabricate the flexible circuit pattern. The prepared poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/graphene/single-walled carbon nanotubes mixed solution was deposited on the flexible substrate to fabricate the flexible circuit patterns, which could improve the fabricated quality of flexible circuit patterns. The influence of the multi-parameters on the pattern width of flexible circuit was analysed by the direct-writing experiment method. In order to explore the mapping law of multi-field coupling parameters on the pattern width of the direct-writing flexible circuit, a multivariable grey regulation method with a small number of test sample data was proposed by combining a small number of experiments and optimization design, and a multi-field coupling parameter regulation model of the NFEDW method was constructed. The multi-parameters with the expected width of the flexible electronic pattern could be effectively controlled. The experimental results show that the average correlation error between the actual measured pattern width and the predicted width of the prediction model is 4.04%. The proposed optimization method can effectively improve the fabrication efficiency and reduce the manufacturing cost of the flexible circuit with the expected pattern width. [ABSTRACT FROM AUTHOR]

Sawing by circular saw blades is predominant in the mechanized treatment of natural stone. The predictive sawing force is crucial for optimizing, controlling, and monitoring the sawing process. In this work, a novel model for predicting sawing force, which is based on the distribution of the undeformed chip thickness at the sawing contact arc, was proposed. Based on the analysis of the undeformed chip geometry of the circular saw blade, the segment surface was divided into the front end and rear end according to the contact pattern between the segments and granite, and the models of the maximum undeformed chip thickness of diamond particles on the front end and rear end were established. Results showed that the new proposed force model fully considered the distribution of undeformed chips compared with the current model. The novel model has higher prediction accuracy, the mean maximum absolute error is within 3.77%, and the maximum absolute error is within 7.83%. Through theoretical analysis, the ratio of the maximum undeformed chip thickness of the front segment to the rear segment is 1.67, which can fully explain the wear non-uniformity of the segment. The proposed model is of great significance to the optimization of the saw blade structure and process parameters. [ABSTRACT FROM AUTHOR]