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In this paper, a new dynamic multiscale topology optimization method for cellular composites with multi-regional material microstructures is proposed to improve the structural performance. Firstly, a free-material distribution optimization method (FMDO) is developed to generate the overall configuration for the discrete element densities distributed within a multi-regional pattern. The macrostructure is divided into several sub regions, and each of them consists of a number of elements but with the same densities. Secondly, a dynamic topology optimization formulation is developed to perform the concurrent design of the macrostructure and material microstructures, subject to the multi-regional distributed element densities. A parametric level set method is employed to optimize the topologies of the macrostructure and material microstructures, with the effective macroscopic properties evaluated by the homogenization. In the numerical implementation, the quasi-static Ritz vector (QSRV) method is incorporated into the finite element analysis so as to reduce the computational cost in numerical analysis, and some kinematical connectors are introduced to make sure the connectivity between adjacent material microstructures. Finally, 2D and 3D numerical examples are tested to demonstrate the effectiveness of the proposed dynamic multiscale topology optimization method for the material-structural composites.

Design of experiment and active learning strategy are vital for the surrogate-based reliability analysis. However, the existing sampling and modeling methods usually ignore some useful information that can guide the choice of training samples, or heavily rely on the characteristics of surrogates. These lead to the inefficiency of sampling strategies or limit the application respectively. Therefore, this work proposes a failure-pursuing sampling framework, which is able to adopt various surrogate models or active learning strategies. In each iteration, it organically and sequentially takes into account the joint probability density function of random variables, the individual information at candidate points and the improvement of the accuracy of predicted failure probability. To measure the probability of the improvement, a global predicted failure probability error is proposed based on the real-time reliability analysis result. Furthermore, Voronoi diagram is applied to partition the sampling region into some local cells for keeping the uniformity of the training samples. Besides, a model-free response-distance function is developed and combined with the framework to avoid relying on the characteristics of surrogates, such as the statistical information provided by Kriging. Finally, four examples are investigated to demonstrate the applicability, stability and generality of the proposed method.

Reliability-based design optimization has gained much attention in many engineering design problems with the consideration of uncertainties. Nevertheless, the application is limited by the huge computational cost for the repeated reliability analysis in the optimization process. To address this issue, an iterative reliable design space approach is proposed with less number of reliability analysis required. Specifically, a sequential optimization strategy is proposed to identify the iterative reliable design space and perform the equivalent deterministic optimization in a serial loop. Thus, the identification of the reliable design space, which is time-consuming, is eliminated from the equivalent probabilistic constraints. Furthermore, an improved shifting vector strategy is put forward for the identification. In this strategy, an approximate shifting vector is first constructed utilizing the partial derivatives at the current design point, and it is then modified based on an increment of the shifting vector and a correction provided by the true reliability analysis. Besides, several mathematical examples and engineering problems are tested to validate the performance of the proposed method. In conclusion, the proposed method is able to deal with different kinds of nonlinear problems with relatively high accuracy and efficiency.

No-wait permutation flow shop scheduling problem (NWPFSP) is a variant of permutation flow shop scheduling problem (PFSP), where the processing of each job must be continuous from start to end without any interruption. That is, once a job starts its processing, it has to be processed until the last machine without any interruption. The aim of this study is to propose an energy-efficient NWPFSP for the determination of a trade-off between total flow time and total energy consumption by obtaining the Pareto optimal set that is the non-dominated solution set. A bi-objective mixed-integer programming model is developed where the machines can operate at different speed levels. Since the problem is NP-complete, an energy-efficient discrete artificial bee colony (DABC), and an energy-efficient genetic algorithm (MOGA), also a variant of this algorithm (MOGALS) are developed, as heuristic methods. First, the performance of these algorithms for comparison with the mathematical model is represented in small size instances in the scope of cardinality and quality of the non-dominated solutions, then it is shown that DABC performs better than two other algorithms in larger instances.

The distributed permutation flow shop scheduling problem with sequence dependent setup times (DPFSP_SDST) is a generalization of permutation flow shop scheduling problem with sequence dependent setup times (PFSP_SDST), where there exists a set of identical factories in a PFSP_SDST structure. It is concerned with first assignment of jobs to factories, and then scheduling jobs in each factory. In this paper, we try to find a trade-off between makespan and total energy consumption in a DPFSP_SDST environment, where machines are assumed to operate at varying speed levels. A multi-objective mixed integer linear programming model is presented based on (1) allocating jobs among factories, (2) determining velocity upon each machine, and (3) scheduling the jobs in each factory. Due to the NP-complete nature of the problem, a multi-objective whale swarm algorithm (MOWSA) is presented to solve this complex multi-objective DPFSP_SDST. We propose a problem specific encoding scheme, crossover and mutation operators as well as a very effective local search in MOWSA. The extensive experimental results show the effectiveness of MOWSA over NSGA-II, SPEA2 and PAES for approximating the Pareto front solution sets.

To achieve sustainable manufacturing of large-scale end-of-life products, disassembly for recycling and remanufacturing has been widely adopted by industries. Disassembly line balancing becomes an important and challenging issue. The disassembly efficiencies of workers are different in the actual disassembly line due to some factors, including disassembly environment, skill level, work enthusiasm, etc. However, efficiency differences are often ignored in previous studies, which ultimately lead to unbalanced workloads among stations. Therefore, this article establishes a disassembly line balancing model that considers workers with different efficiencies and introduces the bucket brigade model into the disassembly line. Its optimization objectives include workload smoothness, cost of workers, disassembly risk, and disassembly demand. To obtain high-quality solutions, a discrete flower pollination algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by comparing it with 11 algorithms. Finally, the proposed model and algorithm are applied to an actual television disassembly case considering workers with different efficiencies, and provide decision makers with multiple disassembly schemes.

Tasgetiren, Mehmet Fatih (isu author) Production scheduling is of great significance in improving production effectiveness while the energy-efficient problem is one of most concerned problems for researchers and manufacturers. Thus, this study investigates the energy-efficient distributed permutation flow shop scheduling problem (DPFSP) with the objectives of makespan and energy consumption. The DPFSP is an extension of permutation flow shop problem (PFSP) considering a set of identical factories. This paper presents a multi-objective mixed integer programming model based on the three sub-problems: allocating jobs among factories, scheduling the jobs in each factory and determining speed upon each job. A multi-objective whale swarm algorithm (MOWSA) is proposed to solve this energy-efficient DPFSP. A new problem-dependent local search is developed to improve the exploitation capability of MOWSA. Moreover, the updating exploitation mechanism is presented to enhance energy efficiency without affecting production efficiency. Finally, the extensive comparison experiments are designed to demonstrate the effectiveness of proposed MOWSA, problem-dependent local search and updating exploitation mechanism. The results indicate the effectiveness of MOWSA and the superior performance over NSGA-II, SPEA2, PAES and MDEA, and also demonstrate that the proposed algorithm can significantly reduce the energy consumption compared with other algorithms. © 2020 Elsevier B.V. Natural Science Foundation of Hubei Province: 2018CFA078 National Science Fund for Distinguished Young Scholars: 51825502 National Natural Science Foundation of China: 51775216 2017QYTD04 This work was supported by National Natural Science Foundation for Distinguished Young Scholars of China (Grant No. 51825502 ), National Natural Science Foundation of China (Grant No. 51775216 ), Natural Science Foundation of Hubei Province (Grant No. 2018CFA078 ) and Program for HUST Academic Frontier Youth Team (Grant No. 2017QYTD04 ). WOS:000564923700010 Q1

Intelligent fault diagnosis has attracted considerable attention due to its ability in effectively processing massive data and rapidly providing diagnosis results. However, in the traditional intelligent diagnosis methods of bearing, features are extracted manually. Such process is not only a grueling and time-consuming work but also greatly affects the diagnosis results. In this study, we propose a new intelligent diagnosis method of bearing, which can learn features automatically. First, a new subset approach is developed and it is helpful to learn the discriminative features from different fault patterns. Second, a subset based deep auto-encoder (SBTDA) model is proposed to realize the automatic feature extraction. Additionally, a new self-adaptive fine-tuning operation is designed to ensure the good convergence performance of SBTDA. Finally, to obtain the appropriate configuration, several key parameters are optimized with particle swarm optimization algorithm. The proposed method is evaluated on three public bearing datasets, and achieves the average testing accuracies of 99.65%, 99.66% and 99.60% respectively. The comparisons with 13 intelligent diagnosis methods demonstrate that SBTDA can obtain higher diagnosis accuracy.

Weld width is commonly used as one of main factors to assess joint performances in laser welding. However, it changes significantly through the thickness direction in conditions of medium or thick plates. In this study, high-power autogenous laser welding was conducted on 7 mm thickness 201 stainless steel to elucidate the factor of whole weld transverse shape critically affecting the mechanical properties with the aim of predicting the performance visually through the weld appearance. The results show that single variation of welding parameters could result in great changes of weld pool figures and subsequently weld transverse shapes. All the obtained welds are composed of austenite containing small amount of cellular dendritic δ-Ferrite. The 0.2% proof stresses of Nail- and Peanut-shaped joint reach 458 MPa and 454 MPa, 88.2% and 87.5% of the base material respectively, while that of Wedge-shaped joint only comes to 371 MPa, 71.5% of the base material. The deterioration effect is believed to be caused by the axial grain zone in the weld center. The fatigue strength of joint P is a bit lower than N, but much better than W. Significant deformation incompatibility through the whole thickness and microstructure resistance to crack initiation should be responsible for the poor performance of W-shaped joints.

Support vector regression (SVR), as a promising surrogate model, has been widely used to approximate expensive simulations in engineering design problem. To build a SVR model accurately and efficiently, a two-stage support vector regression (TSSVR) assisted sequential sampling approach is proposed in this paper with the consideration of SVR’s two unique features. In each sampling iteration of TSSVR, two SVR models are constructed successively based on the same training data. As for the first feature that only support vectors (SVs) have impact on the construction of SVR, the first-stage SVR with lower e precision is built to prescreen some important samples as current SVs. As for the second feature that SVR model does not completely go through the samples, the second-stage SVR with higher e precision is built to calculate the prediction errors at the SVs without any other computational cost, and the prediction errors are used to approximately measure the accuracy of the local regions around the SVs. Moreover, to describe the local regions around the SVs, the design space is partitioned into a set of Voronoi cells according to the current samples before prescreening SVs from the sample points. Then a new sample can be exploited in the corresponding Voronoi cell with the largest prediction error. In the next sampling iteration, the Voronoi cells and SVs are redefined. As the change of the local cell with the largest prediction error, global exploration is achieved. Finally, the proposed approach is validated by seven numerical examples and an engineering example. An overall comparison between the proposed approach and some other methods demonstrates that the proposed approach is efficient and suitable for engineering design problems involving computational-expensive simulations.

Disassembly planning aims to search the best disassembly sequences of a given obsolete/used product in terms of economic and environmental performances. A practical disassembly process may face great uncertainty owing to various unpredictable factors. To handle it, researchers have addressed the stochastic cost and time problems of product disassembly. In reality, the uncertain environment of product disassembly is associated with both randomness and fuzziness. Besides uncertain disassembly cost and time, the quality of disassembled components/parts in a process has uncertainty and thus needs to be assessed via expert opinions/subjects. To do so, this paper presents a new AND/OR-graph-based disassembly sequence planning problem by considering uncertain component quality and varying disassembly operational cost. Important disassembly planning models are built on the basis of different disassembly criteria. A novel hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony is proposed to solve them. Its effectiveness is well illustrated through several numerical cases and comparison with a prior method, i.e., fuzzy-simulation-based genetic algorithm. Note to Practitioners —This paper deals with the uncertainty management problem of product disassembly. It builds some fuzzy programming models for product disassembly and proposes a hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony to solve them. Previously, such a problem was handled through a methodology based on stochastic planning, which was ineffective without considering the fuzzy characteristic of completing a disassembly task. The goal of this paper is to analyze the disassembly uncertainty feature from the perspective of fuzzy programming. Both theoretical and simulation results demonstrate that the proposed approach is highly effective. The obtained results can help decision-makers better determine a disassembly process of a used/returned/obsolete product.

Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and new-generation intelligent manufacturing. New-generation intelligent manufacturing represents an in-depth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for “parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China. Keywords: Advanced manufacturing, New-generation intelligent manufacturing, Human-cyber-physical system, New-generation AI, Basic paradigms, Parallel promotion, Integrated development

Quality inspection is a critical step in the process of manufacturing of free-form surface parts. For dimensional quality control, registration is normally used to match the measurement model to the design model. Then, the machined errors can be visualized in the misalignment regions. Because these two models are located in different coordinate systems, an optimal spatial transformation matrix is necessary. In this area, iterative closest point (ICP) is the best known method for registration. However, the global optimum solution is intractable to compute in terms of ICP. Considering of the accuracy and efficiency of free-form surface parts quality inspection, in this paper, the maximin distance approach is introduced for model simplification with normal deviation information. Moreover, to find the optimal transformation parameters, an improved fruit fly optimization algorithm is proposed in terms of the saccade behavior of drosophila. The performance of the developed algorithms has been tested via both computer simulations and real experiments, and the results demonstrate that the proposed methods are superior to several classical approaches for free-form surface parts quality inspection.

In the community of topology optimization , the contradiction between the frontier research with structured meshes and the complex geometry in the real-world problems has existed for a long time. Structured mesh is attractive in convenient application of loads and boundary conditions, and easy implementation of GPU parallel calculation. Therefore, unlike existing researches solving the issue through unstructured elements, this paper presents an easy-to-accomplish virtual phase method (VPM) based on structured meshes, to systematically solve the problems with complex geometries and arbitrary non-design domains. Structural geometry is denoted by the extended regular design domain and void region initially placed at the border of the extended domain. Based on the extended idea of composite material interpolation in multi-phase density-based topology optimization approaches, the extended structural design domains and the pre-defined non-design domains are considered as the optimized phases in the structure, and they are defined by discrete variable fields. Subsequently, the structural optimization is implemented like as the traditional multi-material topology optimization process. Numerical examples accounting for minimum compliance are presented to demonstrate the effectiveness of the proposed method, including multi-phase macrostructural design, the ease with which the method can accommodate passive regions, and how the length scale constraints are used to devise graded porous infill morphology within a given shell. At last, the manufacturability of the optimized designs is discussed to further verify the applicability of the proposed method. Our approach provides those front researches based on structured meshes with an easily accessible access, to be straightforwardly applied for any complex-domain design problem.

Auxetic composites, which can be viewed as a type of rationally man-made meta -composites with Negative Poisson’s Ratios (NPRs), have received widespread discussions and applications because of the promising, multifunctional and advanced properties. Micro-architectures work as building blocks of unit cells to make up composites, which possess complex topologies of auxetic multi-material. However, it should be noted that a computational design approach with the reasonability and powerful capability of optimizing auxetic composites cells-by-cells is still in need, particularly for composites with three or more materials. In this study, the main focus is to develop an effective and efficient computational design framework for auxetic composites with the tri-material using Isogeometric Topology Optimization (ITO) method, in which a tri-material topology representation model is constructed by non-uniform rationally B-splines (NURBS) to effectively represent micro-structural topology with clear and smooth boundaries. Numerical examples are discussed to present the effectiveness of the computational design framework, where a series of novel and interesting auxetic micro-architectures with the re-entrant or chiral deformation mechanisms can be found. The significance of the current work contributes to developing a platform that enables a scientific design of auxetic composites, rather than heavily depending on human intuition.

The timely recovery and disassembly of waste electrical and electronic equipment (WEEE) can not only obtain a higher economic benefit but also can reduce the impact of hazardous substances on the environment. The parallel disassembly line can disassemble different kinds of WEEE synchronously and improve disassembly efficiency. Therefore, a parallel partial disassembly line balancing model with stochastic disassembly time is established in this paper. The evaluation indexes of the disassembly line include the number of workstations, workload smoothness, and disassembly profits. A new genetic simulated annealing algorithm is proposed to optimize the model. The encoding and decoding strategies are constructed according to the characteristics of partial disassembly and parallel layout. Two-point mapping crossover and single-point insertion mutation operations are designed to ensure that the disassembly sequence meets the precedence constraints and disassembly constraints. The simulated annealing operation is applied to the results of the genetic operation. The proposed algorithm obtains better solutions than the tabu search algorithm in stochastic parallel assembly line balancing problems, and the proposed algorithm has better performance than the CPLEX solver, genetic algorithm, and simulated annealing in parallel disassembly line balancing problems. Finally, a parallel partial disassembly line for waste televisions and refrigerators is constructed, and the performance of the proposed multi-objective algorithm is superior to those of five classical multi-objective algorithms. The results show that the proposed model has a better practical application ability and that the proposed algorithm can improve the performance of disassembly lines.

While the concept of remanufacturing is gaining popularity in practice, its effect on environmental protection and energy saving is significant, which has attracted widespread concerns, especially in the automotive components field. Automotive components remanufacturing (ACR), as a prominent part in automotive life-cycle development, can dramatically reduce resources-waste and mitigate energy-crisis, which has become a prominent direction to promote sustainable development of Chinese economy. Thus, influence factors analysis and enterprise operation patterns assessment which primarily contribute to ACR industry development are of paramount importance. This paper elaborates the criteria influencing the ACR industry operation and analyzes the main operation patterns for ACR in China. To overcome the uncertainty of the evaluation process, a new hybrid multi-criterion decision-making (MCDM) model that combines fuzzy AHP (analytical hierarchy process) and fuzzy G-TOPSIS (combining gray relation analysis and technique for order performance by similarity to ideal solution) is proposed to determine weights of influence criteria and evaluate ACR production operation patterns. In addition, sensitivity analysis is applied to monitor the robustness of the method. The study refers to the experience of Chinese end-of-life vehicle (ELV) laws and implementation requirements of extended producer responsibility (EPR). The obtained results provide the new guideline to revise the regulations of Chinese ACR industry.

Increasing demands on precision manufacturing of complex free-form surface parts have been observed in the past several years. Although some advanced techniques have been employed to solve the design and machining problems for such parts, quality inspection remains a difficult problem. Registration is a crucial issue in surface inspection; it is used to transform the design model and measurement model into a common coordinate system. The comparison results are then outputted in a report and displayed visually by color gradients. This paper presents a design model-based inspection method with range image registration, in which the measurement model is represented by a series of 3D discrete points. In the model preprocessing, the directed Hausdorff distance (DHD) method is employed for point cloud simplification, and a novel point descriptor is designed to evaluate the property of each point. Subsequently, a differential evolution (DE) algorithm-based optimizer is proposed for error evaluation. Combined with the properties of 3D points, the optimizer can measure the similarity between the design model and the measurement model with a recursive process. The proposed algorithms have been implemented and tested with several sets of simulated and real data. The experiment results illustrate that they are effective and efficient for free-form surface part quality inspection.

Single-loop approach (SLA) is one of the most promising methods for solving linear and weakly nonlinear reliability-based design optimization (RBDO) problems. However, since SLA locates the current approximate most probable point (MPP) by using the gradient information of the previous one to reduce the computational cost, it may lead to inaccuracy when the nonlinearity of probabilistic constraints becomes relatively high. To overcome this limitation, a new adaptive hybrid single-loop method (AH-SLM) that can automatically choose to search for the approximate MPP or accurate MPP is proposed in this paper. Moreover, to get the accurate MPP more efficiently and alleviate the oscillation in the search process, an iterative control strategy (ICS) with two iterative control criteria is developed. In each iterative step, the KKT-condition of performance measure approach (PMA) is introduced to check the validity of the approximate MPP. If the approximate MPP is infeasible, ICS will be further carried out to search for the accurate MPP. The two iterative control criteria are used to update the oscillation control step length, then ICS can converge fast for both weakly and highly nonlinear performance functions. Besides, numerical examples are presented to verify the efficiency and robustness of our proposed method.

The weld geometry characteristics are highly related to the mechanical properties and performance of the joints. The top surface of the weld is often assumed as the flat surface during calculating the fluid flow in the molten pool and hence weld reinforcement is ignored among the geometric parameters. In this paper, a three-dimensional numerical simulation model is developed to investigate the characteristics of the weld generated in the fiber laser keyhole welding. The gas layer above the molten pool has been considered to simulate the top surface of the weld in the simulation model and the free surface shape of the keyhole is calculated by the volume of fluid method. The hybrid heat source model is determined by the analysis of weld bead characteristics. Based on the numerical simulation results, the evolution of the weld profile characteristics have been demonstrated and the results are adopted to estimate the shape and sizes of the generated weld bead. From the comparison, the simulated results of the weld bead agree well with that of experiments. Furthermore, weld beads formed under different process conditions are calculated and the errors between the simulated and experimental results are analyzed in details.

The quality of the welded joints is greatly affected by the appearance and geometry of the weld bead. Understanding the formation mechanism of whole weld bead has great theoretical significance and engineering value. This paper proposes a novel three-dimensional model considering the heat transfer, keyhole free surface, surface tension and recoil pressure for simulating the formation process of the weld bead and predicting its full sizes in the fiber laser keyhole welding. To improve the simulation accuracy, the relationship between the weld geometry and welding process parameters is established and taken into consideration in the numerical modeling. Based on the model, the keyhole phenomena and weld bead formation process have been revealed. During the numerical simulation, the weld shape including the width, reinforcement and penetration are all calculated, as well as the corresponding size. The validity of the proposed model is confirmed by experiments, and the simulated results show good agreement with the experimental values. Additionally, the changing tendency of the weld geometry affected by the welding process parameters is analyzed by simulation and the results are found to be consistent with the theoretical and experimental data. Therefore, the proposed method is very effective for improving the weld shape and welded joints quality.

Single- and few-layer chromium triiodide (CrI3), which has been intensively investigated as a promising platform for two-dimensional magnetism, was usually prepared by mechanical exfoliation. Here, we report on the growth of single-layer CrI3 by molecular beam epitaxy under ultrahigh vacuum. The atomic structures and local density of states have been revealed by scanning tunneling microscopy (STM). Iodine trimers, each of which consists of three I atoms surrounding a three-fold Cr honeycomb center, have been identified as the basic units of the topmost I layer. Different superstructures of single-layer CrI3 with characteristic periodicity around 2-4 nm were obtained on Au(111), but only pristine structure was observed on graphite. At elevated temperatures (423 K), CrI3 was partially decomposed, resulting in the formation of single-layer chromium diiodide. Our bias-dependent STM images suggest that the unoccupied and occupied states are distributed spatial-separately, which is consistent with our density functional theory calculations. The effect of charge distribution on the superexchange interaction in single-layer CrI3 was discussed., Comment: 18 pages, 6 figures

In this study, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed for energy-efficient scheduling of distributed heterogeneous welding flow shop (DHWFSP). This problem is extended from distributed flow shop with different amounts of machines in different factories. In addition, it is allowed that multiple machines could operate one job simultaneously. Considering energy efficiency and productivity, this problem could be treated as three sub-problems: job assignment among factories, job scheduling within each factory and deciding the amount of multi-machines upon each job. A multi-objective mathematical model and modified MOEA/D are proposed to minimize the total energy consumption and makespan simultaneously. In modified MOEA/D, various genetic operators and problem-specific local search strategies are designed for multi-level optimization. The comparison experiment with some well-known algorithms shows the effectiveness of the proposed MOEA/D in optimizing and balancing two contradictory objectives.

Free-form surface part inspection can be conducted by comparing an ideal design model with a real measurement model. Because these two models are in different coordinate systems, the measurement model, represented by a set of 3D points, should be consistently registered to the design model. The final aim of the registration is to determine the optimal transformation matrix. In this research area, the iterative closest point (ICP) method is the best-known algorithm for the registration of two point sets. However, the ICP method needs a good initial parameter to obtain the global optimum transformation matrix, which is difficult to guarantee in the actual inspection process. To improve the precision and robustness of complex parts quality inspection, an ensemble parameters fruit fly optimization (EFFO) algorithm is proposed in this study. This paper provides a parameter pool using the smell-based search process of a fruit fly swarm and sorts the individuals based on the fitness to identify the leaders in a vision-based search process. Additionally, an ICP-based initialization strategy is introduced into the EFFO. We compared the EFFO algorithm with other registration methods and some variants of FFO. The proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the experimental results show that the proposed algorithm is effective.

Dissimilar materials welded joints provide many advantages in power, automotive, chemical, and spacecraft industries. The weld bead integrity which is determined by process parameters plays a significant role in the welding quality during the fiber laser welding (FLW) of dissimilar materials. In this paper, an optimization method by taking the integrity of the weld bead and weld area into consideration is proposed for FLW of dissimilar materials, the low carbon steel and stainless steel. The relationships between the weld bead integrity and process parameters are developed by the genetic algorithm optimized back propagation neural network (GA-BPNN). The particle swarm optimization (PSO) algorithm is taken for optimizing the predicted outputs from GA-BPNN for the objective. Through the optimization process, the desired weld bead with good integrity and minimum weld area are obtained and the corresponding microstructure and microhardness are excellent. The mechanical properties of the optimized joints are greatly improved compared with that of the un-optimized welded joints. Moreover, the effects of significant factors are analyzed based on the statistical approach and the laser power (LP) is identified as the most significant factor on the weld bead integrity and weld area. The results indicate that the proposed method is effective for improving the reliability and stability of welded joints in the practical production.

Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

Tolerance analysis is frequently used in predicting the product quality and balancing the design tolerances in mechanical assemblies. Generally, the tolerance analysis procedure is rather complex and cumbersome, and the existing computer-aided tolerance analysis methods are insufficient in dealing with some assembly information and user-defined quality requirements. This paper presents a new comprehensive tolerance analysis and optimization framework using deviation propagation and small displacement torsor (DP-SDT) theory. In this framework, four modules are designed to model the tolerances, analyze the assembly processes, predict the product quality, and optimize the tolerances respectively. Comparing with the existing methods, this framework can better support the complex assembly information like 3D dimensional tolerances, geometric dimensioning and tolerancing (GD&Ts), different tolerance zones, geometric information, assembly sequence, and various kinds of locating modes. And more practical quality requirements besides the distance precisions can be analyzed. The framework is a helpful supplement in tolerance analysis field. An application prototype using the framework has been developed for SolidWorks, and a tolerance optimization example of lathe saddle is provided to verify the performance of the framework.

Laser butt welding (LBW) of dissimilar materials has received great attention in automotive, power, chemical, nuclear and aerospace industries. The quality of welded joints is significantly affected by the generated defects in the dissimilar materials welding process. This paper proposes a defect-reducing optimization method that considers the geometric features of weld bead as evaluation indexes of welding defects and process parameters effect on the responses. The former aims to reduce welding defects for welding operations, and the latter seeks to identify the extent of contribution of actual process parameters on welding defects. The particle swarm optimization and back propagation neural network (PSO-BPNN), which has proved to be good modeling for no-linear problems, are utilized to establish the mathematical model and the defects reduction objective is combined with weld area. The genetic algorithm (GA) is adopted to solve the model. The effect of significant factors on the responses is identified based on the calculation of signal to noise (S/N) ratio and analysis of variance (ANOVA). The proposed method is evaluated by macro weld profile, microstructure and mechanical properties in confirmation tests. The results show that the proposed method is effective at reducing weld defects for dissimilar materials welding in practical production. Keywords: Laser butt welding, Welding defects reducing, BPNN, PSO, GA

Xuzhou is located in the northwest of Jiangsu Province, at the junction of the four provinces of Sulu, Henan and Anhui. It belongs to a warm temperate and semi-humid climate. Based on the field investigation in Xuzhou area, a total of 1037 species, 595 genera and 144 families of vascular plants were obtained. The flora has certain importance and particularity in Jiangsu area. Based on the analysis of the species diversity of vascular plants and their floristic characteristics and geographical components, it is found that there are a lot of vascular plants in Xuzhou, most of which are distributed in the world and north temperate zone, and the tropical distribution also accounts for a certain proportion, which shows that the flora is mainly the type of transition from tropical to temperate, At the same time, its flora also has some ancient elements.

This paper proposes a sampling-based system reliability-based design optimization (SRBDO) method with local approximation of constraints. To enhance the optimization efficiency of SRBDO problems with time-consuming constraints, Kriging metamodels are employed to replace the true constraint functions. A new composite active learning strategy based on the possibility of correctly predicting the state of the cut-set system is developed to locally approximate constraints. Furthermore, to ensure the accuracy of the system reliability analysis at the final SRBDO solution, the Kriging update in the developed strategy is terminated by quantifying the influence of the Kriging uncertainty on the prediction of the system failure probability and the confidence that the solution satisfies the prescribed system failure probability. This approach can avoid the unnecessary burden of Kriging construction during system reliability analysis at intermediate solutions far from the final solution. Based on the updated Kriging metamodel, the system failure probability of constraints is estimated by Monte Carlo simulation, and its partial derivative is calculated by stochastic sensitivity analysis. The performance of the proposed method is tested and verified by using four examples. Compared with the existing methods, the proposed method has high computational accuracy and efficiency for solving SRBDO problems.

The quantity of waste electrical and electronic equipment (WEEE) is very large. WEEE not only occupies resources, but also easily pollutes the environment. The disassembly line is the most efficient way to address large-scale WEEE. How to improve disassembly profit and reduce energy consumption has become a significant and challenging research topic. However, the existing literature only considers the completely normal disassembly mode, ignoring the uncertainties such as corrosion and deformation of parts, and the evaluation system of the disassembly line cannot take into account both economic benefits and environmental impacts. Therefore, this paper introduces the destructive disassembly mode into the disassembly line and proposes a partial destructive disassembly line balancing model. The model aims to comprehensively optimize the number of stations, smoothness index, energy consumption, and disassembly profit. To obtain high-quality disassembly schemes, an improved genetic algorithm based on task precedence relationship is developed. Finally, the proposed model and method are applied to an engineering example of a television disassembly line. The performance of the proposed method is verified by comparing it with ant colony optimization, particle swarm optimization, artificial bee colony, and simulated annealing. The analysis of the disassembly schemes shows that the partial destructive mode can improve the disassembly profit and reduce energy consumption.

Intelligent fault diagnosis methods based on deep auto-encoder have achieved great success in the past several years. However, these methods cannot effectively handle the data collected under noisy environment. Therefore, this paper proposes a new ensemble deep contractive auto-encoder (EDCAE) to address the problem. First, we design fifteen deep contractive auto-encoders (DCAE) to learn invariant feature representation automatically. Due to the Jacobian penalty term in DCAE and different characteristics, these models can deal with various noisy data effectively. Second, fisher discriminant analysis is applied to select low-dimensional features with the maximum class separability. Softmax classifier is adopted to identify the selected features and produce fifteen classification results. Finally, a new combination strategy is developed to combine these individual results. Benefitting from the combination strategy, it can produce accurate diagnosis results even under strong background noise. Additionally, to prove the effectiveness of EDCAE, theory analysis about error bound is conducted. The proposed method is verified on three case studies including bearing, gear box and self-priming centrifugal pump. Experiments are conducted under seven different signal-to-noise-ratios. Results show that EDCAE is better than other intelligent diagnosis methods, including individual DCAE, deep auto-encoder, sparse deep auto-encoder, deep denoising auto-encoder and several ensemble methods.

In traditional intelligent fault diagnosis methods of rotating machinery, features are designed manually by experts, which makes these methods less automatic. Deep auto-encoder (DA) provides an effective way to learn discriminative features. However, individual DA is of low generalization and not robust. This work develops an ensemble DA(EDA) method for intelligent fault diagnosis. EDA is constructed by combining sparse DA, denoising DA and contractive DA. Thus, EDA can effectively handle redundant information, noisy corruption and signal perturbation. To ensure the feature learning performance of each DA model, a self-adaptive fine-tuning is designed to obtain stable convergence. To enhance the discriminative features, a dynamic weighted average method is designed to aggregate these learned features. EDA is verified on three public datasets, and achieves the testing accuracies of 100%, 99.69% and 99.92%. Comparisons with other methods, including both traditional methods and individual DA methods, demonstrate that EDA obtains higher diagnosis accuracy.

Micro-structured materials consisting of an array of microstructures are engineered to provide the specific material properties. This present work investigates the design of cellular materials under the framework of level set, so as to optimize the topologies and shapes of these porous material microstructures. Firstly, the energy-based homogenization method (EBHM) is applied to evaluate the material effective properties based on the topology of the material cell, where the effective elasticity property is evaluated by the average stress and strain theorems. Secondly, a parametric level set method (PLSM) is employed to optimize the microstructural topology until the specific mechanical properties can be achieved, including the maximum bulk modulus, the maximum shear modulus and their combinations, as well as the negative Poisson's ratio (NPR). The complicated topological shape optimization of the material microstructure has been equivalent to evolve the sizes of the expansion coefficients in the interpolation of the level set function. Finally, several numerical examples are fully discussed to demonstrate the effectiveness of the developed method. A series of new and interesting material cells with the specific mechanical properties can be found., Comment: 27 pages, 9 figures, 4 tables, published in the International Journal of Computational methods

Intelligent manufacturing technology has become a major trend in the development of the manufacturing industry around the world, and is being studied and applied by numerous industrially developed countries. For example, the United States has proposed an intelligent manufacturing layout based on the Industrial Internet and the “New Generation of Robots”; Germany has proposed the Industry 4.0 initiative to boost the competitiveness of the manufacturing industry through intelligent manufacturing; and the European Union (EU), Japan, Korea, China, and other large manufacturing countries have put forward corresponding strategies for the development of intelligent manufacturing. Clearly, intelligent manufacturing has become an important direction in the development of the manufacturing industry.

The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.

This paper proposes a new level set method for topological shape optimization of 3D structures considering manufacturing constraints. First, the boundary of structure is implicitly represented as the zero level set of a higher-dimensional level set function, and the implicit surface is parameterized through the interpolation of a given set of compactly supported radial basis functions. In this way, the original Hamilton–Jacobi partial differential equation is transformed into a system of algebraic equations. Correspondingly, the topological shape optimization is changed to the easiest size optimization in structural optimization. Many more efficient gradient-based optimization algorithms can be directly applied to the size optimization. Second, to save the expensive computational cost in the 3D large-scale optimization problems, the discrete wavelet transform is introduced into the level set method to compress the size of the coefficient matrix in compactly supported radial basis function interpolant. The discrete wavelet transform converts the original matrix into a set of wavelet basis and therefore eliminates the “noise” elements in the new matrix, so that the linear system can be replaced by a sparser one. Finally, a cross section projection strategy is utilized to ensure the satisfaction of the extrusion constraint and reduce the number of the design variables, simultaneously. Several numerical examples in 3D structures are employed to demonstrate the effectiveness of the proposed method.

Three dimensional (3D) point clouds are typically used in computer vision and pattern recognition areas. In general, the raw point cloud has large numbers of redundant points which require excessively large storage space and lots of time for post-processing. This paper presents a synthetic point cloud simplification method to obtain computationally manageable point sets. First, a coarse-to-fine feature extraction manner is designed with normal vectors deviation and k-means clustering methods, which can concentrate more sample points in regions of high curvature. Additionally, the directed Hausdorff distance is performed directly on the point cloud which samples the point cloud judiciously with an edge-preserving manner. Experimental results demonstrate that the proposed method is effective for point cloud simplification, and it exhibited superior performance compared to existing techniques.

Mechanical fault diagnosis is an essential means to reduce maintenance cost and ensure safety in production. Aiming to improve diagnosis accuracy, this paper proposes a novel data-driven diagnosis method based on deep learning. Nonstationary signals are preprocessed. A feature learning method based on deep learning model is designed to mine features automatically. The mined features are identified by a supervised classification method – support vector machine (SVM). Thanks to mining features automatically, the proposed method can overcome the weakness that manual feature extraction depends on much expertise and prior knowledge in traditional data-driven diagnosis method. The effectiveness of the proposed method is validated on two datasets. Experimental results demonstrate that the proposed method is superior to the traditional data-driven diagnosis methods.

Reliability-based design optimization (RBDO) combined with metamodel is a powerful tool to deal with variation of system output induced by uncertainties during practical engineering design. In this paper, the importance boundary sampling (IBS) method is proposed to enhance the efficiency of Kriging-model-based RBDO. Rather than fitting all the parts of the limit state constraints precisely within the design region, the proposed IBS mainly selects sample points on the critical parts of the limit state constraints. Two importance coefficients are proposed to identify these critical boundary parts: the first importance coefficient is determined by the objective function value; and the second one is calculated using the joint probability density value of the design variables. The sampling and optimization processes are conducted alternately to select the sample points more rationally. The computation capability of the proposed method is demonstrated using several mathematical RBDO problems and a box girder design application. The comparison results show that the proposed IBS method is very efficient.

Construction of the quality tolerances (Q-T) function is a key step in assembly tolerance analysis process. This paper presents a new feature-based tolerance analysis method for mechanical assemblies with GDT (2) the analysis process and acquired Q-T function is relatively concise to the other feature-based method; (3) the integrated assembly information is more intact. A case study is provided to illustrate the proposed method.

The paper considers the dynamic job shop scheduling problem (DJSSP) with job release dates which arises widely in practical production systems. The principle characteristic of DJSSP considered in the paper is that the jobs arrive continuously in time and the attributes of the jobs, such as the release dates, routings and processing times are not known in advance, whereas in the classical job shop scheduling problem (CJSSP), it is assumed that all jobs to be processed are available at the beginning of the scheduling process. Reactive scheduling approach is one of the effective approaches for DJSSP. In the paper, a heuristic is proposed to implement the reactive scheduling of the jobs in the dynamic production environment. The proposed heuristic decomposes the original scheduling problem into a number of sub problems. Each sub problem, in fact, is a dynamic single machine scheduling problem with job release dates. The scheduling technique applied in theproposed heuristic is priority scheduling, which determines the next state of the system based on priority values of certain system elements. The system elements are prioritized with the help of scheduling rules (SRs). An approach based on gene expression programming (GEP) is also proposed in the paper to construct efficient SRs for DJSSP. The rules constructed by GEP are evaluated in the comparison of the rules constructed by GP and several prominent human made rules selected from literatures on extensive problem sets with respect to various measures of performance.

Reliability-based design optimization (RBDO) in practical applications is hindered by its huge computational cost during structure reliability evaluating process. Kriging-model-based RBDO is an effective method to overcome this difficulty. However, the accuracy of Kriging model depends directly on how to select the sample points. In this paper, the local adaptive sampling (LAS) is proposed to enhance the efficiency of constructing Kriging models for RBDO problems. In LAS, after initialization, new samples for probabilistic constraints are mainly selected within the local region around the current design point from each optimization iteration, and in the local sampling region, sample points are first considered to be located on the limit state constraint boundaries. The size of the LAS region is adaptively defined according to the nonlinearity of the performance functions. The computation capability of the proposed method is demonstrated using three mathematical RBDO problems and a honeycomb crash-worthiness design application. The comparison results show that the proposed method is very efficient.