Target Disassembly Sequencing and Scheme Evaluation for CNC Machine Tools Using Improved Multiobjective Ant Colony Algorithm and Fuzzy Integral

Feng, Yixiong; Zhou, MengChu; Tian, Guangdong; Li, Zhiwu; Zhang, Zhifeng; Zhang, Qin; Tan, Jianrong

doi:10.1109/tsmc.2018.2847448

Cited by 100 publications

(34 citation statements)

References 59 publications

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“…The proposed method is applied to the conceptual design of precision fixture, which proves the feasibility and effectiveness of the method. Feng et al 82 proposed an improved multi-objective ant colony algorithm to help decision makers choose the best plan and sequence when performing a product disassembly process. To reduce the negative impact of the ambiguity of uncertain evaluation data on the decision of product design schemes, Song et al 83 proposed a product design scheme evaluation model based on a combination of rough numbers, AHP, and TOPSIS.…”

Section: Multi-attribute Decision-making For Design Alternativesmentioning

confidence: 99%

Data-driven product design toward intelligent manufacturing: A review

Feng

Zhao

Zheng

et al. 2020

International Journal of Advanced Robotic Systems

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With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.

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Section: Multi-attribute Decision-making For Design Alternativesmentioning

confidence: 99%

Data-driven product design toward intelligent manufacturing: A review

Feng

Zhao

Zheng

et al. 2020

International Journal of Advanced Robotic Systems

Self Cite

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“…Porta et al studied an energy harvesting-aware sensor-mission assignment distributed algorithm [ 24 ]. Chen and Feng et al proposed a hop-based mobile charging policy to minimize the number of mobile chargers in a large-scale WRSN [ 25 , 26 ]. Because these mobile chargers are often deployed on the automated guided vehicles (AGVs), researchers paid attention to reducing the number of mobile chargers in the case of ensuring the coverage.…”

Section: Related Workmentioning

confidence: 99%

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

Feng

Wang

Zheng

et al. 2018

Sensors

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Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

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“…In literature, different kinds of intelligent algorithms have been successfully applied to solve DSP problems. Compared with traversal search and its improved methods such as Dijkstra's algorithm [9], branch-and-bound algorithm [25]- [27], swarm intelligence and evolutionary algorithms [28] such as simplified swarm optimization algorithm (SSO) [9], [29]- [31], genetic algorithms (GAs) [15], [32]- [37], Simplified teaching-learningbased optimization (STLBO) [38], [39], artificial bee colony algorithms [40], [41], ant colony algorithms [42]- [44], and scatter search [45] can better solve the DSP with large scale components.…”

Section: Introductionmentioning

confidence: 99%

A Disassembly Sequence Planning Method With Team-Based Genetic Algorithm for Equipment Maintenance in Hydropower Station

Cui

et al. 2020

IEEE Access

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Disassembly sequence planning (DSP) is an important part of equipment maintenance in a hydropower station. In this paper, the generation of an excellent disassembly sequence (DS) for equipment is studied. Firstly, according to the characteristics of hydropower equipment, a combination node type is added to the directed graph analysis model, and the distance factor of components in space is added to the evaluation function of DS. Secondly, a DSP strategy including the grouping and minimization of the node's scope is adopted to reduce computational complexity. Thirdly, a novel team-based genetic algorithm (TBGA) combining teams, fast feasible solution generator (FFSG), precedence preservative crossover (PPX) mechanism, multi-point heuristic mutation (MHM) mechanism, and forward-and-backward optimization operator (FBOO) is designed for DSP. The proposed TBGA maintains global search capabilities through teams and enhances local search capabilities through individuals. In the evolutionary process, teams, MHM, and FBOO have good complementarity to improve the comprehensive performance of the algorithm. Finally, four experiments are conducted and the performance of TBGA is tested based on the comparison of a well-known genetic algorithm, simplified teaching-learning-based optimization, and simplified swarm optimization algorithm. The results show that the proposed method can get better search results in limited iterations and require only about 25% time of other algorithms.

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Target Disassembly Sequencing and Scheme Evaluation for CNC Machine Tools Using Improved Multiobjective Ant Colony Algorithm and Fuzzy Integral

Cited by 100 publications

References 59 publications

Data-driven product design toward intelligent manufacturing: A review

Data-driven product design toward intelligent manufacturing: A review

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

A Disassembly Sequence Planning Method With Team-Based Genetic Algorithm for Equipment Maintenance in Hydropower Station

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