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With the aggravation of market competition, strategic supplier is becoming more and more critical for the success of manufacturing enterprises. Suppler selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the long-term development of organizations. Hence, strategic supplier selection must be restructures considering the long-term relationships and prospects for sustainable cooperation. This paper proposes a novel multi-stage multi-attribute group decision making method under an interval-valued q-rung orthopair fuzzy linguistic set (IVq-ROFLS) environment considering the decision makers' (DMs) psychological state in the group decision-making process. First, the initial comprehensive fuzzy evaluations of DMs are represented as IVq-ROFLS. Subsequently, two new operators are proposed for aggregating different stages and DMs' preferences respectively by extending generalized weighted averaging (GWA) to IVq-ROFLS context. Later, a new hamming distance based linear programming method based on entropy measure and score function is introduced to evaluate the unknown criteria weights. Additionally, the Euclidean distance is employed to compute the gain and loss matrix, and objects are prioritized by extending the popular Prospect theory (PT) method to the IVq-ROFLS context. Finally, the practical use of the proposed decision framework is validated by using a strategic supplier selection problem, as well as the effectiveness and applicability of the framework are discussed by using comparative analysis with other methods. [ABSTRACT FROM AUTHOR]

Aiming at the logistics dynamic scheduling problem in an intelligent manufacturing workshop (IMW), an intelligent logistics scheduling model and response method with Automated Guided Vehicles (AGVs) based on the mode of "request-scheduling-response" were proposed, and they were integrated with Internet of Things (IoT) to meet the demands of dynamic and real time. Correspondingly, a mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO) to minimize the finish time with the minimum AGVs and limited time. The mathematical model optimized the logistics scheduling process on two dimensions which include the sequence of tasks assigned to an AGV and the matching relation between transfer tasks and AGVs (AGV-task). The effectiveness of the model was verified by a set of experiments, and comparison among DLH-GA-ACO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO), and tabu search algorithm (TSA) was performed. In the experiments, the DLH-GA-ACO ran in a distributed environment for a faster computing speed. According to the comparisons, the superiority and effectiveness of DLH-GA-ACO on dynamic simultaneous scheduling problem were proved and the intelligent logistics scheduling model was also proved to be an effective model. [ABSTRACT FROM AUTHOR]

The typical business processes in building material equipment manufacturing enterprises are analyzed, and the framework and functional models of the tracking system for building material equipment manufacturing enterprises in the order lifecycle are presented. Based on the model, using a mobile agent and XML technology, the order tracking system’s framework is built for building material equipment manufacturing enterprises based on a mobile agent in the Aglet platform. The key technologies in the process of tracking the order lifecycle are introduced in detail. Finally, a prototype system is developed and used in an enterprise successfully, verifying the feasibility and availability of the system. [ABSTRACT FROM AUTHOR]

A method to deal with the design of automatic parting in calibrator computer-aided design (CAD) for plastic profile extrusion dies via feature recognitions is presented. An integral design method to design all the calibrator plates simultaneously is presented. The approach to automatic parting in calibrators is proposed. For a given calibrator integral plate, the positions of calibrators' parting faces are identified automatically based on recognitions of the visibility, attribute adjacent graphs of calibrator cavity surfaces. To meet the machining process requirements of calibrators, a parting process to achieve the final position of the calibrator parting faces is presented. A case study is presented to validate the proposed methodology. [ABSTRACT FROM AUTHOR]

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The high-dimensional features of defective bearings usually include redundant and irrelevant information, which will degrade the diagnosis performance. Thus, it is critical to extract the sensitive low-dimensional characteristics for improving diagnosis performance. This paper proposes modified kernel marginal Fisher analysis (MKMFA) for feature extraction with dimensionality reduction. Due to its outstanding performance in enhancing the intraclass compactness and interclass dispersibility, MKMFA is capable of effectively extracting the sensitive low-dimensional manifold characteristics beneficial to subsequent pattern classification even for few training samples. A MKMFA- based fault diagnosis model is presented and applied to identify different bearing faults. It firstly utilizes MKMFA to directly extract the low-dimensional manifold characteristics from the raw time-series signal samples in high-dimensional ambient space. Subsequently, the sensitive low-dimensional characteristics in feature space are inputted into K-nearest neighbor classifier so as to distinguish various fault patterns. The four-fault-type and ten-fault-severity bearing fault diagnosis experiment results show the feasibility and superiority of the proposed scheme in comparison with the other five methods. [ABSTRACT FROM AUTHOR]

Since the different matching schemes between manufacturing tasks and services have a great influence on the life cycle sustainability of complex product system (CoPS), the strategy used to find cost-effective, environmentally friendly, and socially acceptable matching results is essential for sustainable development. This paper proposed a novel two-sided matching model for CoPS based on life-cycle sustainability assessment (TSMM-CoPS-LCSA). First, the two-sided matching model based on the life-cycle sustainability assessment of manufacturing tasks and services for CoPS is presented. Then, a bi-level decision-making method is proposed, where learning and synergy effect are considered to ensure stability in the matching process. Sequentially, to facilitate the optimization solution, the Karush-Kuhn-Tucker (KKT) conditions are adopted to convert the bi-level model to a single model which aims to minimize the total cost, environmental impact, and social impact. Subsequently, for solving this nonlinear and complex multi-objective decision-making model, an improved hybrid algorithm that integrated Tabu Search and non-dominated sorting genetic algorithm II (TS-NSGA-II) is applied to solve the above model. Finally, a case study of CoPS manufacturing project is illustrated to demonstrate the effectiveness and superiority of the proposed TSMM-CoPS-LCSA model. The results indicate that the proposed method can find a better services scheme in line with actual demand, which provides a guiding significance for promoting the sustainable development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

For the left Bernstein-Durrmeyer quasi-interpolants $M_{n}^{(2r-1)}f$, we prove that, for some l, This is a strong converse inequality of type B. [ABSTRACT FROM AUTHOR]

To address manufacturing resource combinatorial optimization (MRCO) for large complex equipment in group manufacturing and provide high-quality manufacturing resource, a multi-objective optimization model and heuristic approach are investigated in this study. First, considering the extensive manufacturing resource, a four-tuple model with use-cost, trading period, credibility and resource consumption for manufacturing resource clustering is proposed, as well as MRCO model associated with the four indicators. Then, an improved cluster-based genetic algorithm (CGA) has been carried out with a fuzzy c-means algorithm (FCM) for manufacturing resource clustering. The proposed algorithm uses a fuzzy analytic hierarchy process (FAHP) to successfully determine the priority weights of multi-objective. Finally, a case study and comparison of performances with simple genetic algorithm (SGA) and non-cluster-based genetic algorithm (Non-CGA) are presented. Experimental results show the proposed method is more preferable in optimal solution searching and more efficient as the dimension of problem increases. [ABSTRACT FROM AUTHOR]

Large-sized end-of-life products are particularly suitable for disassembly operations on the two-sided disassembly line, and destructive disassembly is often required in the disassembly process. However, the negative impact caused by destructive disassembly during the disassembly process has been ignored. Therefore, in this paper, a probability-based task destructive disassembly model is constructed in the two-sided disassembly line balancing problem, the negative impact on the total disassembly cost, workload smoothness index, and the impact of destructive disassembly tasks on other adjacent but unconnected tasks is defined. Then a mixed-integer programming model for the problem is formulated to minimize the total disassembly cost, smoothness index and the impact on other adjacent tasks by destructive disassembly tasks. A multi-objective restart genetic flatworm algorithm is proposed, which combines the genetic operations and flatworm regeneration processes, and embeds a restart mechanism. The effectiveness of the model and the efficiency of the algorithm are verified by disassembling mobile phones, laptops, printers and engines on the two-sided disassembly line. Finally, through the application of car disassembly, it is proved that the model and algorithm can assist decision-makers to choose the preferred disassembly schemes. [ABSTRACT FROM AUTHOR]

Supplier selection has significant impact on life cycle cost of complex product system (CoPS). In this paper, a new variant of supplier selection problem named life cycle supplier selection of CoPS (LSS&CoPS) problem is addressed. There are three kinds of choices for a manufacturer to complete a CoPS: self-made, purchasing the finished component and outsourcing. Different selection not only results in difference of procurement cost of CoPS, but also results in reliability changing after it delivered to customer which greatly influences the operating cost in CoPS’s lifecycle. However, the minimizing of two objectives is mutually conflicted. This paper presents a bi-objective LSS&CoPS model which considering operating stage of CoPS to balance the procurement cost and operating cost. Moreover, a hybridization of Pareto genetic algorithm (PGA) with multi-intersection and similarity crossover (MSC) strategy is proposed to solve the bi-objective problem. Also, a dual-chromosome is used to represent the variable-length chromosome. Finally, a cement equipment supplier optimal in a cement equipment enterprise is provided. Example indicates that the procurement cost and operating cost have been optimized, yields a Pareto optimal solution of supplier schema for project managers to make-decision and decrease the life cycle cost of CoPS. Additionally, the results show that the proposed approach is more preferably in Pareto optimal solution searching. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) is emerging as an advanced service-oriented manufacturing model for the manufacturing industry. It is changing the way manufacturing enterprises organize production activities in that tangible and intangible manufacturing resources encapsulated as virtualized manufacturing services are provided to customers. In this background, CMfg task semantic modeling and description is proposed to address different challenges such as matching with manufacturing services. Firstly, some of the essential features of CMfg tasks are briefly discussed and classification as well as the modeling requirements of CMfg tasks is also investigated, and then this paper proposes the modeling framework of CMfg task to solve the general CMfg task ontology (GCMT_Ontology) construction and the task sub-ontology matching from GCMT_Ontology. In GCMT_Ontology construction process, the original CMfg task ontology (OCMT_Ontology) is built from the CMfg task description model, and then an ontology learning approach is advanced to perfect the GCMT_Ontology with graph-based semantic similarity algorithm. In task sub-ontology matching process, semantic feature values of concepts are calculated to extend the semantic content of the task document, and the extended concepts are input to find the sub-ontology by computing minimum connected sub-graph. Finally, the proposed framework is realized in a prototype system, and an example of cement grinding mill assembling task is carried out in a group enterprise to illustrate the application. The results show that the proposed method improves enterprise collaboration and interoperation effectively. [ABSTRACT FROM AUTHOR]

The quality of the products is one of the key factors for the enterprises to increase their core competitiveness. In order to resolve the problems that the quality diagnosis with respect to manufacturing process is not effective or the low efficiency in the large equipment manufacturing enterprises, we propose a quality diagnosis approach based on the similarity of product quality gene. First, we use a product quality gene model to calculate the similarity between the organisms and the products. Second, a kind of quality gene codes is adopted to describe the complex quality information for manufacturing process. Then, string and grey relation similarity are used to evaluate whether the quality is normal or not by considering the similarity between the detected product quality gene and the case genes. Finally, we use the rotary kiln bearing body as an example to illustrate the proposed algorithm process. Experimental results have shown that the proposed approach is able to support quality diagnosis. [ABSTRACT FROM AUTHOR]

Highlights: [•] We develop four types of time model for computing project duration. [•] We embed Pareto genetic algorithm with variable neighborhood search strategy. [•] We propose three VNSs combing of process optimization specific knowledge. [•] The results show that PGVNS can obtain a better Pareto solution than NSGA-II. [ABSTRACT FROM AUTHOR]

Abstract: Coupled activities are the main reasons to cause collaboration in product development (PD) process. Previous modeling approaches such as Petri net, IDEF and DSM fail to represent the collaboration characteristic of PD process well. Considering the characteristics of optoelectronic PD process especially for demand of collaborative development, this paper proposes the process planning framework, establishes the three-dimensional collaboration model, and analyzes nine collaboration types among activities. The Process Collaboration Degree (PCD) and Activity Collaboration Degree (ACD) considering information delivery times and probability are defined to strengthen the modeling ability of DSM, and then the Collaboration Degree Design Structure Matrix (CD-DSM) is constructed to model the collaborative development process of optoelectronic products. In order to decrease the coupled complexity, PCD is applied to decompose the nested activities into atomic activities based on the information input/output points and ACD is used to express the value of the elements in the CD-DSM. Furthermore, the upstream and downstream relationship of atomic activities is optimized based on the CD-DSM to plan the collaborative PD process. Finally, the proposed framework is realized in a prototype system, and an example of LED display module development process is carried out in an optoelectronic company to illustrate the application. And the results show that the proposed method improves the process planning of collaborative PD effectively. [Copyright &y& Elsevier]

Abstract: E-alliance is the union of e-commerce and its success and efficiency is related to comprehensive quality of e-commerce. Thus, ranking e-commerce websites in e-alliance is of importance, which is a multi-criteria decision-making (MCDM) problem. This paper proposes an evaluation model based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to tackle the issue in fuzzy environment. The AHP is applied to analyze the structure of ranking problem and to determine weights of the criteria, fuzzy sets is utilized to present ambiguity and subjectivity with linguistic values parameterized by triangular fuzzy numbers, and TOPSIS method is used to obtain final ranking. Case analysis is conducted to illustrate the utilization of the model for the problem. It demonstrates the effectiveness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract: In order to accurately forecast and prevent customer churn in e-commerce, a customer churn forecasting framework is established through four steps. First, customer behavior data is collected and converted into data warehouse by extract transform load (ETL). Second, the subject of data warehouse is established and some samples are extracted as train objects. Third, alternative predication algorithms are chosen to train selected samples. Finally, selected predication algorithm with extension is used to forecast other customers. For the imbalance and nonlinear of customer churn, an extended support vector machine (ESVM) is proposed by introducing parameters to tell the impact of churner, non-churner and nonlinear. Artificial neural network (ANN), decision tree, SVM and ESVM are considered as alternative predication algorithms to forecast customer churn with the innovative framework. Result shows that ESVM performs best among them in the aspect of accuracy, hit rate, coverage rate, lift coefficient and treatment time. This novel ESVM can process large scale and imbalanced data effectively based on the framework. [ABSTRACT FROM AUTHOR]

Abstract: For the Bernstein left quasi-interpolants , we prove that for some This is a strong converse inequality of type B. [Copyright &y& Elsevier]

In this paper we give direct, inverse, and equivalence approximation theorems with unified Ditzian-Totik modulus [image omitted] (0 ≤ λ ≤ 1) for Meyer-Konig and Zeller operators. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we give the strong converse inequality of type B for Meyer–König and Zeller operators. [Copyright &y& Elsevier]

Abstract: In this paper, we give direct, inverse and equivalence approximation theorems for the Bézier type of Meyer–König and Zeller operator with unified Ditzian–Totik modulus (). [Copyright &y& Elsevier]

Abstract: For Baskakov–Bzier operators we give the direct, inverse and equivalence approximation theorems with unified Ditzian–Totik modulus . [Copyright &y& Elsevier]

Abstract: In this work we give the estimates of the moments for the Meyer–König and Zeller operators which can be used for studying the approximation properties of the operators. [Copyright &y& Elsevier]

Abstract: Recently, the quasi-interpolants of some classical operators were introduced. Mache and Müller gave Baskakov quasi-interpolants and obtained approximation equivalence theorem with In this paper, we extend the above result with modulus (0 ≤ λ ≤ 1) which unified classical modulus and Ditzian-Totik modulus. [Copyright &y& Elsevier]

We consider the pointwise weighted approximation by Bernstein operators. The related weight functions are Jacobi weights $w(x)\backslash eg\; x^a\{(1-x)\}^b$ (

We give a strong converse inequality of type B in terms of unified K-functional Kλα( f,t2)(0&les;λ&les;1, 0<α<2) for Baskakov operators. [Copyright &y& Elsevier]

Recently some classical operator quasi-interploants were introduced to obtain much faster convergence. P. Mache and D. H. Mache gave Bernstein quasi-interpolants and obtained approximation equivalence theorem with . In this article we extend above result with modulus which unified classical modulus and Ditzian-Totik modulus. [ABSTRACT FROM AUTHOR]

Presents an equivalent theorem for linear combinations of Gamma operators. Ditzian-Totik moduli of smoothness; Theorems, lemmas and proofs.

For linear combinations of Bernstein–Kantorovich operators Kn, r(f, x), we give an equivalent theorem with ω2rϕλ(f, t). The theorem unites the corresponding results of classical and Ditzian–Totik moduli of smoothness. [Copyright &y& Elsevier]

E. Van Wickeren (1986, Constr. Approx.2, 331–337) shows some Stechkin–Marchaud-type inequalities in connection with Bernstein polynomials. In this paper, we introduce ω2ϕλ(f, t)α, β, and give the Stechkin–Marchaud-type inequalities for Baskakov polynomials. [Copyright &y& Elsevier]

The increasing number of large-scale end-of-life products, such as vehicles and refrigerators have paid a negative impact on the ecological environment and led to the waste of non-renewable resources. The two-sided disassembly line is playing an important role in the recycling of end-of-life products with high efficiency. However, the complex execution constraints in the disassembly process have been ignored, and the additional energy consumption caused by hazardous tasks is not fully considered. Therefore, we introduce the two-sided disassembly line balancing problem with complex execution constraints and establish an improved mixed-integer programming model to optimize the weighted length, workload smoothness index, and total energy consumption simultaneously. A dual-individual simulated annealing algorithm with multi-objective is developed, in which two different neighbor structure strategies and a two-point mapping information exchange mechanism are designed to enrich the diversity of Pareto-optimal solutions. Six testing benchmark problems of different scales are designed, then the algorithm is evaluated with the single-objective and multi-objective simulated annealing and the famous NSGA-II. The results indicate that the proposed model and algorithm can satisfy the complex execution constraints for task assignment, effectively improve the smoothness index, and reduce the energy consumption in the disassembly system. [ABSTRACT FROM AUTHOR]

Turning operation is a fundamental machining method that is indispensable in aviation industry and automotive sectors, what is more, round insert is an important type of cutter due to its relatively massive engaged edge. Modeling of cutting force is important to forecast the machining process, what is more, to predict tool wear and surface quality of components. The cutting force distribution, often studied in milling and drilling operation, is necessary to be discussed in turning operation with round shape insert due to its complex and unique cutting geometry. In this paper, an analytical force prediction model for turning process is proposed based on the discretization of cutting edge and detailed geometric analysis with the consideration of edge effect and size effect, and applied to the force distribution of round insert. Firstly, a novel way is developed to divide the uncut chip area of round insert into many increments and the local parameters of each increment can be calculated by the classical oblique cutting theory. Secondly, shear flow stress of the workpiece at the primary shear zone is determined based on unequal division shear zone model and modified with the influence of size effect. The cutting force of each increment is predicted based on the oblique cutting theory with the consideration of edge effect. Then, an in-depth discussion of the distributions of local parameters is presented for each increment along the cutting edge of round insert, which indicates the characteristics of round insert. Finally, series of cutting experiments are performed on Inconel 718 and Ti6Al4V to verify the effectiveness of the developed model. The predicted results show good agreement with the measured ones, which proves the correctness and accuracy of the proposed analytical model. [ABSTRACT FROM AUTHOR]

The die model characterized with high design efficiency and strong correlation plays an important role in simulation and machining. Because of the complexity of structure and the changing shape of flow channel, current design tools lack in the level of automation so that design of an extrusion die is a nontrivial task. In this paper, an integral design method is proposed to improve the design efficiency and model correlation. The computer-aided design (CAD) system for plastic profile extrusion die is designed and implemented by integrating Visual Studio 2010 (VS2010) and Unigraphics NX (UG). The closed loop recognition algorithm (CLR), multi closed loops intelligent recognition (MCLR) algorithm, and multi closed loops matching (MCLM) algorithm based on feature recognition are proposed to realize automatic generation of ruled surface in flow channel. The integral design and automatic generation methods of die set are proposed to facilitate efficient formation of different structures and process holes. The particle swarm optimization (PSO) algorithm is put forward to obtain best position and specification for the interference parts. Three modules (integral sketches, integral flow channel, and integral die set) are developed, which focus on integrating the proposed automation design methods into their framework. A case application on die design is used to illustrate the proposed method. The results show that the developed CAD system not only improves the design efficiency of extrusion die but also enhances the design quality. [ABSTRACT FROM AUTHOR]

The complex structure of plastic profile calibrating die and the various types of holes make it difficult to meet the requirements for efficient and rapid assembly of the components of calibrating die. To solve this problem, this paper presented an automatic assembly CAD system, which was based on Siemens PLM NX platform, Visual Studio tools, and object-oriented programming technology. In this system, the relevance expression between components and main assembly was established. The recognition method of closed-loop sketch and attributed adjacency graph was used. Components were one-click unitized assembled. Based on those key technologies, the automatic assembly CAD system has been developed successfully, and it has been applied in a large mold enterprise, which improves the quality and efficiency of calibrating die design. [ABSTRACT FROM AUTHOR]

Abstract. The purpose of this paper is to derive the pointwise estimate for three general exponential-type operators by using new Ditzian-Totik modulus. [ABSTRACT FROM AUTHOR]

Domain adaptation (DA) approaches have been extensively applied to the diagnosis of rotating machinery faults under different working conditions. However, most DA-based methods perform poorly in practical situations since they generally only consider the global distribution or subdomain distribution of the source and target domains. Thus, we propose a novel Deep Convolution Multi-Adversarial adaptation network with Correlation Alignment (DCMACA). DCMACA consists of an improved deep convolutional feature extractor, a domain adaptation module, and a label classifier. The improved deep convolutional feature extractor comprises ordinary convolutional layers, depthwise convolution layers, Squeeze and Excitation modules, skip connection operations, an average pooling layer, and a fully connected layer. The domain adaptation module introduces multiple domain discriminators and Coral distance to align the subdomain distribution and global distribution of features extracted by the feature extractor, respectively. The softmax function is employed as the label classifier. Based on DCMACA, we presented a new approach for identifying faults in rotating machinery under different operating conditions. First, the original vibration signals are converted into the time-frequency maps of size 64 × 64 via the continuous wavelet transform and bilinear interpolation technologies. Subsequently, the time-frequency maps are input to DCMACA to complete the extraction of transferable features and fault identification. The proposed DCMACA fault identification approach was evaluated through two experiments, where it achieved an average accuracy of 98.84% in 18 migration diagnostic tasks. The comprehensive results reveal that the presented approach can realize higher diagnostic accuracies, robustness, and superior generalization capability compared to the existing mainstream DA approaches. [ABSTRACT FROM AUTHOR]

The degradation state of a bearing can be monitored effectively by using a wear indicator (WI). A WI curve having smoothness and monotonicity can lay a good foundation for predicting the remaining useful life of the bearing. Most traditional models for bearing WI construction, such as time–frequency indicators and signal decomposition, are complicated; for example, some WI construction models need several models to select from, and their fusion depends on the manual experience of engineers. For example, single and mixed traditional time–frequency indicators, such as the root mean square (RMS), Kurtosis, multiple time–frequency domain fusion. However, the mentioned-above time–frequency domain indicators are difficult to adaptively reflect the operating status of the equipment when the operating conditions of the mechanical system change. Some signal decomposition models are combined with other models and rely on manual experience to extract WI, such as the selection of effective intrinsic mode function components function, parameter setting of empirical mode decomposition and ensemble empirical mode decomposition model, etc. Deep learning models, such as stacked auto encoder (SAE) and convolution neural network, have been widely used in bearing health monitoring and WI construction, because its powerful learning and feature extraction capabilities of multiple hidden layer structures. But these deep learning models are designed to use output labels. Particularly when the data volume is large, it requires manpower, material resources, and experienced engineers to label the data, or it is difficult to label and distinguish the categories of data samples. Therefore, to solve these problems and eliminate the need for manual labor, such as labeling data and selecting models for fusion, we propose using SAE without an output label layer to extract WI from original signals directly. However, an extracted WI curve without good monotonicity (Mon) will result in a poor remaining useful life prediction accuracy. To improve the monotonicity of the extracted WI and reduce the complexity of the WI construction model, we propose an unsupervised enhanced SAE without an output layer, named SINSAE, by adding a sine function of an average value which is calculated form start time to current at each hidden layer to eliminate concussion. Moreover, to demonstrate that our proposed model is better than other models, such as the RMS, Kurtosis, multiple time–frequency domain fusion, SAE, SAE without an output layer, and signal decomposition models, the Mon indicator in this study is used to compare the monotonicity of the extracted WI. Lastly, the results of our experiments using different bearing datasets and various working conditions show that the smoothness and monotonicity of the WI curve extracted by the SINSAE is better than that of other models. Moreover, compared to the traditional commonly used single and multiple time–frequency domain indicators, supervised deep learning and basic unsupervised deep models, the unsupervised SINSAE model can increase the Mon indicators from [0.1, 0.8], [0.02, 0.1], [0.1, 0.8] to above 0.9, respectively. [ABSTRACT FROM AUTHOR]

Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality control of the manufacturing process of large equipment were proposed. In that model, the processing surface was the minimum information granularity to meet the production characteristics of large equipment and to improve the flexibility of the product gene model. Then, a similarity evaluation rule and an optimization method of the weights of elements based on particle swarm optimization (PSO) were addressed to filter the available knowledge of product gene from the product gene knowledge base. Aiming at the characteristic of many-to-many between quality defects and quality influence factors in some cases, a fuzzy comprehensive evaluation (FCE) method was developed for the further localization of diagnosis knowledge. Finally, an experiment of bearing spacer was applied to illustrate the proposed quality diagnosis approach. In the experiment, the data from the target gene and knowledge genes were described reasonably. On this basis, available knowledge genes could be accurately filtered with the proposed similarity rule and the method of filtration, where the PSO was proved to be effective. The diagnosis results of the experiment show that multiple factors lead to the defects that were verified. Therefore, the proposed quality defects diagnosis method is an effective way for quality control. [ABSTRACT FROM AUTHOR]

Abstract The sharp increase of heat generated when processing difficult-to-machine materials is the main reason of short tool life and poor surface integrity. Temperature modeling plays a significant role in the mechanism of metal cutting, which can guide the optimization of cutting parameters. This paper proposes a modified three-dimensional analytical model of temperature prediction for oblique turning Inconel 718 with round carbide insert. The discretization of heat sources is indispensable to analyze the temperature rise due to the complicated geometry of uncut chip area. In this study, primary heat source (shear band) and secondary heat source (tool-chip interface) that divided into many increments through different discretization methods are considered to analyze thermal effects. For each increment of secondary heat source, the local contact length is calculated through a modified function considering global chip flow angle in oblique cutting. The local heat liberation intensities of primary and secondary heat sources are determined through analytical force prediction model. The variation of heat partition fraction with respect to immersion angle is given to study the characteristics of secondary heat source. Finally, a modified three-dimensional expression of temperature rises when turning with round insert considering semi-infinite medium is proposed based on classical thermal theory. The temperature distributions in chip, tool and workpiece can be determined through the proposed model. The results calculated through predictive model show good agreement with that given by finite element method (FEM) and experiments, which validates the correctness of the proposed model. [ABSTRACT FROM AUTHOR]

The characteristics of decentralization, diversification, and dynamics of outsourcing resources put forward higher requirements for the supplier selection in manufacturing enterprises. For building materials equipment manufacturing enterprises (BMEM), due to the complex product structure, the demand for outsourcing resources is very large. The traditional method is difficult to meet the needs of current outsourcer evaluation and selection. In order to solve the problem, this paper establishes an outsourcing supplier selection model based on the quality, price, delivery time, reliability, and availability of outsourced suppliers. And then, a hybrid supplier selection algorithm based on Analytic Hierarchy Process (AHP) and Particle Swarm Optimization (PSO) is designed in this paper. Finally, the model and algorithm are verified and analyzed in BMEM. The paper also compares the algorithm with genetic algorithm (GA) and ant colony optimization (ACO). The experimental results show that the model can effectively improve the effectiveness and reliability of outsourcing supplier selection. [ABSTRACT FROM AUTHOR]

As a common type of ceramic inserts, the round inserts with high hardness and super high wear resistance are now widely used in high efficiency machining of some key components of aviation industry and nuclear industry. Cutting forces have an important effect on the surface quality of key components and tool life of inserts in machining process. However, the lack of understanding on the complex geometry and detailed description of uncut chip thickness (UCT) restrict the cutting force prediction of round inserts. In this paper, an analytical model for cutting force prediction of round inserts based on the precise cutting geometry is presented. First, a new geometric method for the cutting edge discretization of ceramic round insert in high speed turning is proposed. Different from the previous methods, the discretization of cutting edge in this research is performed on the rake face rather than the reference plane, which makes the prediction more accurate for the reason that the projection of tool edge is ellipse on the reference plane. Second, the shear flow stress in the primary deformed area, used to estimate the cutting force coefficients, is calculated based on the unequal division shear zone model. Then, a new prediction model considering shear flow stress and chamfer length is developed in edge force coefficients forecasting and is verified by the simulation of finite elements method (FEM). Finally, a series of cutting experiments are implemented to verify the feasibility of the proposed analytical model for cutting force prediction of round inserts. It indicates that the forecast results are in good agreement with the measured cutting forces, which proves the correctness and accuracy of the model. [ABSTRACT FROM AUTHOR]

Abstract: Most existing methods for 3D model retrieval focus on the global shape description and matching. However, partial shape description and retrieval may be used more frequently in the fields of product design and manufacture. In order to resolve the problem that the retrieval efficiency for complex model descriptors is low, a CAD model retrieval method based on local surface region decomposition is presented in this paper. First, according to the salient geometric features of the mechanical part, the surface boundary of a solid model is divided into local convex, concave and planar regions. Then, we give a kind of region codes that describe the surface regions and their links in the CAD model. Finally, the model retrievals are realized based on the similarity measurement between two models’ region codes. Experimental results have shown that this approach is able to support partial retrieval of CAD models. [Copyright &y& Elsevier]

• The energy-oriented activity splitting and recombining proactively. • Dynamic fuzzy clustering strategy for the time-varying resource consumption level. • Metaheuristic solution for project scheduling with different granularity levels. Energy-cost-aware scheduling in manufacturing process has recently been considered as an effective way to use energy. In addition, applying time-of-use is regarded as one of the optimal uses of energy which the governments extend to control energy consumption. Complex product system (CoPS) has more complex and huge amounts of multiple manufacturing activities with higher energy sensibility in project scheduling than commodity products. This paper presents a new variant of resource-constrained project scheduling problem (RCPSP), which is named energy-cost-aware resource-constrained project scheduling problem with activity splitting and recombining (eRCPSP-AS&R) for CoPS. In the proposed model, a dynamic activity splitting and recombining strategy for CoPS is used by considering energy consumption. Also, a bi-objective (project delay and energy consumption cost) mathematical model is established. The solution framework of eRCPSP-AS&R model consists of two parts: activity splitting and recombining, project scheduling. The dynamic strategy of activity splitting and recombining is used to perform energy-cost-aware based on hybrid intuitionistic fuzzy information entropy, TOPSIS and fuzzy clustering method. Moreover, an improved NSGA-II algorithm is employed to solve the Pareto-optimal solutions for the proposed bi-objective optimization problem. Series of computational experiments are conducted and verified the overall performance of proposed eRCPSP-AS&R model and approaches. [ABSTRACT FROM AUTHOR]

• A novel way is proposed for daily urban water demand forecasting. • Both data pre-processing and optimal parameter selection techniques are used. • The results show that the proposed model is superior to some existing models. • The proposed model can be a promising model to forecast urban water consumptions. A reliable and accurate urban water demand forecasting plays a significant role in building intelligent water supplying system and smart city. Due to the high frequency noise and complicated relationships in water demand series, forecasting the urban water demand is not an easy task. In order to improve the model's abilities in handling the complex patterns and catching the peaks in time series, we propose a hybrid long short-term memory model combining with discrete wavelet transform (DWT) and principal component analysis (PCA) pre-processing techniques for water demand forecasting, i.e. , DWT-PCA-LSTM. First, the outliers of water demand series are identified and smoothed by 3σ criterion and weighted average method, respectively. Then, the noise component of water demand series is eliminated by DWT method and the principal components (PCs) among influencing factors of water demand are selected by PCA method. In addition, two LSTM networks are built to yield the daily urban water demand predictions using the results of DWT and PCA techniques. At last, the superiorities of the proposed model are demonstrated by comparing with the other benchmark predictive models. The water demand from 2016 to 2020 of a waterworks located in Suzhou, China is used for the experiment. The predictive performance of the experiments are evaluated by the mean absolute percentage error (MAPE), mean absolute percentage errors of peaks (pMAPE), explain variance score (EVS) and correlation coefficient (R). The results show that the DWT-PCA-LSTM model outperforms the other models and has satisfactory performance both in catching the peaks and the average prediction accuracy. [ABSTRACT FROM AUTHOR]