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In this study, an efficient algorithm for non-probabilistic reliability-based design optimization (NRBDO) is presented. To improve the convergence rate, the sequential Kriging model is applied to the inner-layer optimization of the double-nested optimization model, maximizing the utility of each sampling point. During the global exploration stage, the algorithm employs an expected improvement criterion and a parallel sampling strategy. In the local exploration stage, a minimum surrogate prediction criterion is utilized to identify new sampling points, resulting in enhanced efficiency and accuracy of Kriging surrogate model. The optimization of each sampling criterion is performed using the differential evolution algorithm. Adaptive switching between global and local exploration is achieved by considering the relationship between new and known sample points, ensuring the identification of the optimal solution. To further enhance optimization efficiency, an Aitken Δ 2 acceleration strategy is applied to improve the current population, while a heuristic pattern-based local search method is employed to enhance the subpopulation, developing of a bilevel accelerated microbial genetic algorithm to solve optimal solution. The efficiency of the proposed method is demonstrated through two numerical cases and an engineering application involving the ram of the TK6932 heavy-duty floor-type milling and boring machine. [ABSTRACT FROM AUTHOR]

The tribological behavior of 42CrMo4/17NiCrMo6-4 under grease lubrication was explored in terms of load, speed, hardness matching, and lubrication quantity. Optical microscopy, scanning electron microscopy, and a surface profilometer were used to investigate the wear mechanism. The results show that hardness matching has the greatest impact on the wear resistance and friction reduction of the friction pair, followed by the load factor, with the impacts of speed and lubricant quantity being minor. Increasing the hardness of 42CrMo4 reduces the friction coefficient and wear volume of the friction pair substantially. When the maximum surface hardness of 42CrMo4 was compared with the lowest surface hardness, the friction coefficient was reduced by 21.5%, and the wear volume was reduced by 87.2%. Abrasive wear is the sort of wear failure that was seen, and as the hardness of 42CrMo4 increased, more severe fatigue wear appeared on 17NiCrMo6-4. While the wear volume initially increases and subsequently lowers with increasing load, the friction coefficient initially decreases and then stabilizes. A synergistic combination of abrasive and adhesive wear occurs under high load, changing the wear type from abrasive wear under low load. The wear volume is decreased by the sticky layer generated under high load conditions, which achieves superior wear prevention. This study is anticipated to offer recommendations for designing gears' required hardness under various operating circumstances. [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]

Dissipative particle dynamics simulations were adopted to study the mechanism of lyotropic liquid crystal (LLC) formation and the synthesis process of silica nanowires by LLC template method. Simulation results show that the one-dimensional (1D) LLC channel can be formed at the Pluronic F127 concentration range of 61 ∼ 67%. With the increase of Pluronic F127 concentration, the diameter of LLC channel decreases and the deformation degree increases. The effect of PEO: PPO ratio on the diameter of LLC channel is slight. The appropriate PEO: PPO ratio of Pluronic is 40:70 for the formation of 1D LLC channel, as the deformation degree of channel is the smallest in this case. The effect of polymerisation degree on the diameter and deformation degree of LLC channel is both little. With the increase of TEOS concentration, the nanowire diameter increases rapidly at first, and then increases quite slowly. This work can provide guidance for silica nanowires synthesis by LLC template method. [ABSTRACT FROM AUTHOR]

With their low weights and excellent mechanical properties, TiAl alloys are expected to be applied in pistons to achieve energy savings and emissions reductions in internal combustion engines. The tribological behaviours of the Ti–48Al–2Cr–2Nb alloy against the QT600-3 material at different applied loads under oil-lubricated conditions were investigated in this study. The results showed that as the load increased, the friction coefficient and wear-scar volume significantly decreased. The wear surface of the TiAl alloy was analysed using scanning electron microscopy and energy-dispersive X-ray spectroscopy. The wear mechanism of the TiAl alloy under oil lubrication conditions was mainly the combined effect of adhesive wear and oxidation wear, different from the wear mechanism under dry-friction conditions. Furthermore, by observing the cross-sectional morphology of the TiAl alloy after focused ion beam, it was found that the QT600-3 wear debris had compacted and accumulated on the surface of the TiAl alloy, preventing further contact between the TiAl alloy and the QT600-3. This study has the potential to accelerate the application of the Ti–48Al–2Cr–2Nb alloy in lightweight pistons. • The tribological behavior between TiAl alloy and QT600-3 under oil lubrication condition is investigated. • The wear mechanism of TiAl alloy under oil lubrication condition is revealed. • The influence mechanism of the adhesive layer on tribological behavior of TiAl alloy is revealed. [ABSTRACT FROM AUTHOR]

Dissipative particle dynamics (DPD) simulations was adopted to investigate the modification of polyvinylidene fluoride (PVDF) membrane by adding zwitterionic polymer brush poly(sulfobetaine methacrylate)- tetraethyl orthosilicate (PSBMA-TEOS) grafted silicon nanoparticles (SNPs) to the casting solution. The effects of polymer concentration and grafting architecture (PSBMA length and SNPs grafting ratio) on membrane morphology are discussed. When the polymer concentration reaches 40%, part of the SNPs is embedded in the membrane; the optimal polymer concentration is around 25–30%. In the SNPs system with the grafting ratio of 1, some SNPs are eluted into solution during phase separation. Compared with different grafting architectures, M 8-5 , M 10-5 and M 12-5 system (M x-y , where x represents the length of the zwitterionic polymer brush and y represents the grafting ratio of the silica nanoparticles) exhibited stable membrane morphologies. This work can provide guidance for the design and modification of organic-inorganic composite membrane and help understand the distribution of modified materials on the membrane surface. [ABSTRACT FROM AUTHOR]

There are alternating impact loads for the diesel engine cylinder block. The topology optimization of the extreme single-working condition cannot guarantee its overall mechanical performance, and the traditional multiworking condition optimization has the problem that the weight coefficients are difficult to determine. Thus, a multiobjective topology optimization method based on analytic hierarchy process is proposed. Firstly, the static, dynamic characteristics and structure efficiency are calculated by the finite element analysis which indicates the direction of topology optimization for the cylinder block. The hierarchical structure model of topology optimization, including 12 weighting coefficients, is constructed considering static multiworking condition stiffness and dynamic multiorder natural frequency. The comprehensive evaluation function for the cylinder block is established by the compromise programming method and the weight coefficients are determined based on analytic hierarchy process. The optimization mathematical model is established and the multiobjective topology optimization of the cylinder block is carried out. The optimization results show that the proposed method can take into account structural multiworking condition performance, which has obvious advantages over the single objective topology optimization. The simulation results show that the static and dynamic characteristics are improved to some extent and the overall mechanical performance of the new model is more uniform with a 5.22% reduction in weight. It shows that the topology structure of the cylinder block is more reasonable. [ABSTRACT FROM AUTHOR]

Rotary mechanism is the core part of the multi-functional sling for turning over and erecting the castings and forgings. The mechanical performance of a nail plate inside the structure determines the working safety. In this paper, the dangerous working condition of the rotary mechanism is first introduced when the castings and forgings with different diameters are clamped by the sling. Then, the temperature field and the thermal–mechanical coupling model for the nail plate are established, and the failure mode is analyzed. Next, the layout and shape of the nails on the nail plate are studied to improve the load-bearing performance. A mathematical model, taking the height and draft angle of the nail as the design variables, is established, and a new nail plate model is given. The comparison is carried out through simulation and experiment. The results show that the mechanical performances of the nail plate are significantly improved, and the failure problem of the rotary mechanism is solved. [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]

A sling is a kind of auxiliary equipment in the manufacturing process of castings and forgings. There are some problems, such as a single function, complex operation, and lack of a safety evaluation index, in the design and application of slings. In this paper, a novel multi-functional sling is designed, and it can carry out the transportation, turning over, erection, and lifting of 550 tons of castings and forgings. A clamping coefficient index is defined, and the mechanical model of the sling is established to evaluate whether it can safely hold castings and forgings. Then, an optimization model with a clamping coefficient is established to reduce the weight and maintain the load-bearing performance. The genetic algorithm is adopted, and the total weight is reduced by 3.4%. Finally, the mechanical performances are verified by finite element analysis and experiment, and the results indicate that the sling can meet the design requirements. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

As the main load-bearing structure of heavy machine tools, cranes, and other high-end equipment, the large-scale box structures usually bear moving loads, and the results of direct topology optimization usually have some problems: the load transfer skeleton is difficult to identify and all working conditions are difficult to consider comprehensively. In this paper, a layout design method of stiffened plates for the large-scale box structures under moving loads based on multiworking-condition topology optimization is proposed. Based on the equivalent principle of force, the box structures are simplified into the main bending functional section, main torsional functional section, and auxiliary functional section by the magnitude of loads and moments, which can reduce the structural dimension and complexity in topology optimization. Then, the moving loads are simplified to some multiple position loads, and the comprehensive evaluation function is constructed by the compromise programming method. The mathematical model of multiworking-condition topology optimization is established to optimize the functional sections. Taking a crossbeam of superheavy turning and milling machining center as an example, optimization results show that the stiffness and strength of the crossbeam are increased by 17.39% and 19.9%, respectively, while the weight is reduced by 12.57%. It shows that the method proposed in this paper has better practicability and effectiveness for large-scale box structures. [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]

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]

Orthonormal basis function (OBF) decomposition method and minimum entropy (ME) detector are two typical methods of magnetic anomaly detection. The OBF detector works effectively only when the assumptions of the target signal and noise are appropriate, and the ME detector is limited by low signal-to-noise ratio (SNR). In order to improve magnetic anomaly detection performance in the case of low SNR and no prior information of the target signal, we proposed a novel detector by using stochastic resonance (SR) method and named it SR detector in the paper. The SR detector consists of a bistable SR system and a corresponding receiver. Firstly, the noise is used to enhance the magnetic anomaly signal with the help of SR system, instead of being suppressed in a traditional method; then the anomaly signal can be detected more effectively by the receiver. Experimental results show that the SR detector did work well, its detection probability approximated to 70% even if the input SNR was −3 dB, and was about 100% if the input SNR was 0dB, when the false alarm rate (FAR) was 1.5%. Furthermore, the SR detector provided higher detection probability than the ME detector under the same basis. In a word, due to the good detection performance and simple implementation, the SR detector would be more attractive in practice. [ABSTRACT FROM AUTHOR]

Owing to the ferromagnetism and stray fields of inertial navigation system, component compensation of magnetic field distortion is significant for geomagnetic field vector measurement system. However, traditional scalar compensation methods cannot be used for component compensation of magnetic field distortion, so some improvement works have been researched for component compensation in the study, the major of which is estimating compensation parameters using the Lagrange multiplier method. Experiment results show that the performance of the component compensation method is much better than traditional scalar compensation method. After compensation, the maximal measurement errors of north, vertical, east components and total intensity caused by magnetic field distortion are reduced to 10.4 nT (0.91% of the raw error), 32.5 nT (5.20%), 12.9 nT (1.27%), and 18.4 nT (2.02%), respectively. In addition, compared with previous component compensation method, the proposed method has three advantages: (i) simplified equipment, (ii) easier operation process, and (iii) better generality. [ABSTRACT FROM AUTHOR]

The tension–compression asymmetry of metallic glasses has shown that the Mohr–Coulomb yield criterion can better determine the fracture characteristics of metallic glasses than the traditional Tresca and Von Mises criterion. Available cutting models based on the traditional yield criterion cannot describe the cutting mechanism of metallic glasses. An orthogonal cutting mechanics model of metallic glasses based on the Mohr–Coulomb yield criterion taking into account the influence of normal stress and temperature on shear plane is established. The analytical expressions for cutting force, shear angle and average temperature rise on shear plane are derived. Orthogonal turning experiments were designed and carried to validate the developed model. The analytically calculated cutting forces from the developed model considering cutting temperature rise were compared with the measured forces and the results show that a small difference of 7.7% was found, which is contrary to the prediction from traditional model based on von Mises yield criterion (nearly 60%) and the prediction from the model based on M–C without considering cutting temperature rise (more than 70%). The comprehensive comparison results show that the developed analytical model can accurately predict cutting process for metallic glasses. [ABSTRACT FROM AUTHOR]