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In metal cutting processing, prediction of the tool wear or VB value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several VB values in the nearest future are predicted by using sequential VB values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step VB values in milling operations. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks have promoted development of the fault diagnosis in the machine prognostics and health management. However, the existing methods have limited applicability under strong noisy conditions. We propose an attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for the rotor fault diagnosis. The parallel multiscale dilated convolution residual module is firstly introduced to filter noise and extract multiscale discriminative features intelligently. The multi-head attention module is then embedded in ConvNeXt for global discriminative features adaptively. Focal Loss is finally implemented for identifying hard-to-classify samples to further improve the diagnostic accuracy. The application results demonstrate the superiority and robustness of our method in two case studies. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life of manufacturing tools based on condition maintenance is a critical task in prognostics and health management systems. Existing methods have limitations in modeling variable-length sequence data and extracting sufficient local and global dependencies. To address these issues, a temporal convolutional approach with residual multi-head attention mechanism is proposed. The temporal convolution efficiently models variable-length sequence data and extracts long-term dependency features in the time dimension. The residual multi-head attention module captures local and global dependency features in the sequence features and extracts the tool key degradation features, which further improves the performance of the model. To validate the predictive performance of the temporal convolutional approach with residual multi-head attention mechanism, two manufacturing tool datasets are used in the remaining useful life experiments. The results demonstrate the effectiveness and superiority of the proposed method compared to other advanced methods. [ABSTRACT FROM AUTHOR]

As a typical combinatorial optimization problem, two-dimensional (2D) irregular layout is a common problem in the engineering field. No-fit polygon (NFP) is a common geometric tool for solving layout problems. Although it is necessary to calculate NFP frequently in the process of layout, the search efficiency of the NFP generation algorithm is affected. The existing unfitting polygon algorithms have difficulties in dealing with complex contours, especially irregular contours with cavities. The large-scale layout process takes the long time with the low material utilization. A vector superposition NFP (VS-NFP) method is proposed in this paper to improve the solution efficiency of 2D irregular layout problems with complex contours. Based on the VS-NFP, an improved mixed integer programming (MIP) model is built. The model increases compression constraints for high solution search efficiency. A hybrid algorithm based on the VS-NFP and MIP is proposed to solve the problem. Comparing with the existing methods, the proposed method shortens the search time and improves the material utilization. The proposed method is verified in the application of large-scale layout problems. [ABSTRACT FROM AUTHOR]

A support vector machine (SVM) is an important method in the detection and classification of the color difference on a polysilicon wafer. However, the accuracy of a SVM is affected by its feature vector and parameters. Owing to the complex color information and random texture features on the wafer surface, the feature design is extremely complicated. Meanwhile, a SVM optimized using a popular intelligent algorithm easily falls into a local optimum, and the convergence of the algorithm needs to be improved. Therefore, a classification method is proposed for detecting the color difference from multi-scale features in polysilicon wafer images. First, to extract the features, an image segmentation method is devised based on the maximum region contrast, which effectively applies a threshold segmentation of the wafer images. Second, the multi-scale features and color representations in different color spaces are used to construct a nine-dimensional feature vector that sufficiently describes the surface characteristics of the wafer. An approach to optimize the SVM is finally proposed using a magnetic bacteria optimization algorithm based on an elitist strategy for parameter optimization. The optimum individual of each generation is used to adjust the magnetic moment such that the solution approaches the optimal direction and enhances the global search ability. A fitness function is also introduced to improve the diversity of the solutions through a cross-validation method. The experiment results show that the proposed algorithm achieves an accuracy of 98.3% with a better classification performance than the other methods and that the color difference of polysilicon wafers can be effectively detected. [ABSTRACT FROM AUTHOR]

Freeform surfaces, also called sculptured surfaces, have wide applications in clinical prosthesis and manufacturing. The free-form contour of product needs to be packed before it is machined from raw materials. The No-Fit Polygon methods are not suitable to the complex contours packing because calculating No-Fit Polygons for non-convex polygons is time-consuming. A contour packing algorithm for free-form contour machining is proposed in this paper. The parking area of materials is discretized based on a bottom-left and the lowest gravity center strategy. The packing contour is collided in four directions to increase the material utilization. Experimental results show that the packing time is reduced and the material utilization is improved using the proposed algorithm. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters. For complex machine learning models such as deep neural networks, it is difficult to determine their hyperparameters. In addition, existing hyperparameter optimization algorithms easily converge to a local optimal solution. This paper proposes a method for hyperparameter optimization that combines the Sparrow Search Algorithm and Particle Swarm Optimization, called the Hybrid Sparrow Search Algorithm. This method takes advantages of avoiding the local optimal solution in the Sparrow Search Algorithm and the search efficiency of Particle Swarm Optimization to achieve global optimization. Experiments verified the proposed algorithm in simple and complex networks. The results show that the Hybrid Sparrow Search Algorithm has the strong global search capability to avoid local optimal solutions and satisfactory search efficiency in both low and high-dimensional spaces. The proposed method provides a new solution for hyperparameter optimization problems in deep learning models. [ABSTRACT FROM AUTHOR]

Chatter is a self-excited vibration that frequently occurs in thin-walled parts milling, which has become a major limitation to productivity and quality. Additionally, convolutional neural network (CNN) has been widely used in detection and classification, but the accuracy and convergence are affected by the initial weight and hyperparameters. Therefore, based on CNN, a method of chatter detection for the milling of thin-walled parts is proposed, which is realized by recognizing the image of the machined surface. First, aiming at the challenges of neural networks in which the weight is randomly initialized, a weight initialization method is proposed based on an improved magnetic bacteria optimization algorithm. The optimal magnetosome of each generation is used to adjust the magnetic moment of the next generation so that the population approaches the optimal solution direction to search for the global optimal value. Second, an improved genetic algorithm is proposed to optimize the network structure and improve the optimization efficiency of hyperparameters. The tabu list and the hill climbing algorithm are introduced into the improved genetic algorithm to avoid repeatedly counting the fitness of the same point and solving the oscillation problem near the optimal solution. The experimental results show that the accuracy, the value of the Matthew correlation coefficient, and the F1 Score value of proposed CNN are 98.3%, 95.5%, and 98.8%, respectively. Compared with other algorithms, the proposed method, which has outstanding recognition performance, is competent in contactless chatter detection. [ABSTRACT FROM AUTHOR]

This study proposes a remaining useful life (RUL) prediction method using limited degradation data with an unknown degradation model for hydraulic pumps with long service lives and no failure data in turbine control systems. The volumetric efficiency is calculated based on real-time monitoring signal data, and it is used as the degradation indicator. The optimal degradation curve is established using the degradation trajectory model, and the optimal probability distribution model is selected via the K-S test. The above process was repeated to optimize the degradation model and update parameters in different performance degradation stages of the hydraulic pump, providing quantification of the prediction uncertainty and enabling accurate online prediction of the hydraulic pump's RUL. Finally, an RUL test bench for hydraulic pumps is built for verification. The results show that the proposed method is convenient, efficient, and has low model complexity. The method enables online accurate prediction of the RUL of hydraulic pumps using only limited degradation data, with a prediction accuracy of over 85%, which meets practical application requirements. [ABSTRACT FROM AUTHOR]

Tool wear monitoring in machine process is essential for quality assurance, efficiency improvement, and cost reduction. Data-driven methods based on deep learning have become an effective solution for tool wear monitoring, but in actual cutting process, multiple sensors are often used for high-frequency sampling to obtain multi-level information, so that tool wear data has multi-channel spatial structure and time series characteristics, respectively. Therefore, aiming at the distribution of tool wear data, a one-dimensional residual structure is designed and a novel hybrid model integrating bi-directional long short-term memory (BLSTM) network is proposed, which is suitable for tool wear monitoring, named Re-BLSTM. Considering the spatial distribution of tool wear signal, a residual structure is constructed based on one-dimensional convolution neural network (1-D CNN) to realize abstract feature extraction and signal denoising, while gradient vanishing and degradation problem can be solved as the deeper of network. Additionally, batch normalization (BN) layer and dropout operation are introduced to form a pre-activation structure, which can alleviate the over fitting problem caused with the increase of model parameters; Subsequently, in view of the temporal correlation of feature, long-term dependence of concatenated feature is explored by integrating BLSTM and the learning range is also expanded. To verify the performance of the proposed model, experiments are carried out compared with several state-of-the-art method. The results show the effectiveness and generalization of the proposed method. [ABSTRACT FROM AUTHOR]

Ti(C,N) composites are widely applied in various advanced manufacturing industries. However, the intrinsic brittleness and relatively high metal content for Ti(C,N) composites significantly limit their further applications, especially in high-temperature and high-speed machining conditions. Herein, a new type of high flexural strength micro-nano-composite ceramic tool material Ti(C,N)/WC/ZrO 2 for integrated micro milling cutter which will be used to mill difficult-to-machine material such as titanium alloy was proposed and developed successfully by hot press sintering technique under vacuum. The Ti(C,N) composites with low metal phase strengthened by nano-3Y-ZrO 2 and micro-WC particles, the effect of compositions and sintering parameters on the mechanical properties and microstructure of the composite ceramic materials were systematically investigated. The results demonstrated that the Ti(C,N) composites with low metal phase containing 15 wt% nano-3Y-ZrO 2 and 15 wt% micro-WC, sintered at 32 MPa and 1500 °C for 45 min had the optimal mechanical properties under vacuum and the flexural strength, Vicker's hardness, fracture toughness and relative density were 1501 ± 86 MPa, 19.97 ± 0.43GPa, 7.25 ± 0.76 MPa.m1/2 and 99.55%, respectively. Meanwhile, the superior mechanical properties of the Ti(C,N) composites with low metal binder phase could be achieved when sintered at higher temperatures and longer holding times compared to conventional cerments. Moreover, the toughening and strengthening mechanisms for Ti(C,N) composites with low metal phase are the synergistic effects of crack bridging, crack branching, crack deflection, pinning effect, particle pull-out, intragranular microstructure, transgranular and intergranular fracture. These toughening and strengthening mechanisms and the solid solution effects generated by the joint action of oxides and carbides could remarkably improve the properties for the Ti(C,N) composites. In this study, a novel insight on developing high-property Ti(C,N) composites was proposed, and it will make a significant contribution to material synthesis and design, application for cutting tools and composite ceramics investigations. • A new type of ceramic tool material Ti(C,N)/WC/ZrO 2 was proposed and developed successfully. • The superior mechanical properties can be obtained compared to conventional cermets. • The toughening and strengthening mechanisms and the solid solution effects remarkably improve the properties of Ti(C,N) composites. • The best mechanical properties are obtained at 1500 °C for 45 min with 15 wt% 3Y-ZrO 2 and 15 wt% WC. [ABSTRACT FROM AUTHOR]

In the interrupted cutting of metal parts, burrs are generally produced at the edge of the exit surface, which will seriously affect the use and assembly performance. Adding deburring process is a way to solve the problem of burr, but it will increase the cost of economy and time, and hinder the automation of production line. This study focused on the active burr control technology, which can prevent burr formation during the cutting process. A new burr-free theoretical model was established based on the analysis of burr formation, and it indicated that changing the exit surface angle of the workpiece was the key to realize burr-free interrupted cutting. The finite element method (FEM) was applied to simulate the cutting burr. In the simulation, ten gradients from 30° to 90° were set for the exit surface angle, both the stress plot and the final burr shape were presented in good agreement with the theoretical model. A cutting experiment of aluminum alloy 7075-T651 was carried out and the burr-free interrupted cutting was realized under the guidance of the derived theoretical model. The critical exit surface angle of burr-free state was also verified by the experiment, which was predicted from the theoretical model. This study provided a theoretical and practical basis for solving burr problem in interrupted cutting. [ABSTRACT FROM AUTHOR]

Load path as a new analysis tool has been widely used in structural design and assessment of load-bearing performance, but it still lacks effective guidance for structural improvement. In this paper, a new structural improvement method of vehicle component based on the load path and load distribution analysis is proposed. Firstly, according to the structural function and design requirements, the potential regions and parameters need to be optimized are identified based on the load path and load distribution analysis using Ustar and U*sum indexes. Secondly, taking the smoother Ustar on the load path and more effective use of the materials as the design criteria, the optimization suggestions are given. Then, as a case study, a safety hook on an automobile is redesigned and the new structure is compared with the original design. The results show that the Ustar becomes smoother and U*sum becomes more uniform. In addition, the stiffness and strength of the safety hook are improved by 25.29 % and 23.44 %, respectively. Finally, the failure tests show that the ultimate load of the safety hook is enhanced by 13.72 %. The method provides clear and effective guidance for structural improvement from the perspective of load path and load distribution. [ABSTRACT FROM AUTHOR]

• A finite-time integral sliding mode (ISM) surface possessing the complete robustness is proposed for disturbance rejection. The complete robustness to the disturbances is guaranteed by removing the reaching phase of sliding mode control. • Based on the proposed ISM surface, a finite-time consensus control framework consisting of nominal part and discontinuous part is developed. • For the directed switching topologies, a new finite-time ISM control protocol is developed. The proposed ISM algorithm achieves finite-time consensus convergence even in the presence of external disturbances. Euler–Lagrange (EL) system is a typical nonlinear system widely used to model robot systems. Although consensus of EL multi-agent systems has been extensively studied in recent years, how to achieve better consensus performance under constraints such as switching topologies and uncertainties is still an open issue. Aiming at fast convergence and effective disturbance rejection, this paper studies the integral sliding mode control problem for robust finite-time consensus of EL multi-agent systems subject to switching topologies and uncertainties. A basic integral sliding mode control (SMC) scheme is first presented for finite-time consensus of EL multi-agent systems subject to undirected topologies and uncertainties. It is shown that the proposed consensus protocol reaches good disturbance rejection and achieves robust finite-time consensus while avoiding the singularity problem in the existing studies. The proposed integral sliding mode control scheme is further extended to finite-time consensus of EL multi-agent systems subject to directed and switching topologies. Compared with the existing algorithms, faster finite-time convergence is achieved. In the simulation studies, the obtained results demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The recognition of color differences in solar cells with complex textures is a significant challenge in cell manufacturing. Traditional methods fail to detect the color difference effectively. Deep learning models have exhibited promise in many engineering fields. A multi-component attention-based convolution approach is proposed for the surface inspection based on the feature information in different color spaces. Wavelet entropy is employed to represent the information of different components, remove redundant components and extract effective feature information. Additionally, a residual attention mechanism is developed to capture local features with contextual semantic information. The best network structure is determined by evaluating the layer depth of the basic model and convolution kernel size. A multi-component network model is constructed based on the formed structure to improve the ability to distinguish different color difference features. Experimental results indicated that the proposed approach exhibits competitive performance. The research solution provides guidance for applications of deep learning to improve the quality of solar cells in manufacturing. [ABSTRACT FROM AUTHOR]

Ti-CFRP-Ti laminated stacks have been widely used in aviation, aerospace, shipbuilding and other industries, owing to its excellent physical and electrochemical properties. However, chip blockages occur easily when drilling into Ti-CFRP-Ti laminated stacks, resulting in a rapid rise of drilling temperature and an increase of axial drilling force, which may lead to the intensification of tool wear and a decline of drilling quality. Cutting force signals can effectively reflect the drilling process and tool condition, however, the traditional plate dynamometer is typically difficult in realizing the follow-up online measurement. Therefore, an intelligent tool holder system for real-time sensing of the cutting force is developed and constructed in this paper, and the variable parameter drilling method of Ti-CFRP-Ti laminated stacks is studied on this basis. Firstly, an intelligent tool holder system with high flexibility and adaptability is designed; Secondly, a cutting force signal processing method based on compressed sensing (CS) theory is proposed to solve the problem of high-frequency signal transmission; Lastly, the drilling experiment of Ti-CFRP-Ti laminated stacks is carried out based on the intelligent tool holder system, and the drilling parameters are optimized using a compromise programming approach and analytic hierarchy process (AHP). The comparison of results show that the optimized drilling parameters can effectively reduce the hole wall surface roughness and improve the drilling efficiency while ensuring a small axial force. [ABSTRACT FROM AUTHOR]