scholarly journals Disassembly Sequence Planning for Intelligent Manufacturing Using Social Engineering Optimizer

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 663
Author(s):  
Cheng Zhang ◽  
Amir Mohammad Fathollahi-Fard ◽  
Jianyong Li ◽  
Guangdong Tian ◽  
Tongzhu Zhang
Keyword(s):  
Fitness Function ◽  
Evaluation Criteria ◽  
Social Engineering ◽  
Optimization Methods ◽  
Intelligent Manufacturing ◽  
Constraint Matrix ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Product Disassembly

Product disassembly and recycling are important issues in green design. Disassembly sequence planning (DSP) is an important problem in the product disassembly process. The core idea is to generate the best or approximately optimal disassembly sequence to reduce disassembly costs and time. According to the characteristics of the DSP problem, a new algorithm to solve the DSP problem is proposed. Firstly, a disassembly hybrid graph is introduced, and a disassembly constraint matrix is established. Secondly, the disassembling time, replacement frequency of disassembly tool and replacement frequency of disassembly direction are taken as evaluation criteria to establish the product fitness function. Then, an improved social engineering optimizer (SEO) method is proposed. In order to enable the algorithm to solve the problem of disassembly sequence planning, a swap operator and swap sequence are introduced, and steps of the social engineering optimizer are redefined. Finally, taking a worm reducer as an example, the proposed algorithm is used to generate the disassembly sequence, and the influence of the parameters on the optimization results is analyzed. Compared with several heuristic intelligent optimization methods, the effectiveness of the proposed method is verified.

scholarly journals Disassembly Sequence Planning (DSP) Applied to a Gear Box: Comparison between Two Literature Studies

2020 ◽  
Vol 10 (13) ◽  
pp. 4591 ◽  
Author(s):  
Leonardo Frizziero ◽  
Alfredo Liverani
Keyword(s):  
Three Dimensional ◽  
Evaluation Criteria ◽  
Three Dimensional Model ◽  
Specific Component ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
New Methods ◽  
Disassembly Sequence ◽  
Mechanical Products

This work aims to analyze the characteristics and importance that design techniques for disassembly assume in the modern design phase of a mechanism. To this end, the study begins by considering a three-dimensional model of a gear motor, taken from the components of which the overall drawings are arranged and from the relief of those not available. Once the mechanism has been digitally reconstructed, the activity focuses on the study of the optimal disassembly sequence by comparing different methodologies, according to two evaluation criteria—minimizing the time taken and minimizing the number of tool changes necessary to complete the sequence. The main results of the work are (1) defining a standard methodology to improve disassembly sequence planning, (2) finding the best disassembly sequence for the specific component among the literature and eventually new methods, and (3) offering to the industrial world a way to optimize maintenance operations in mechanical products. Referring to the limitation of the present works, it can be affirmed that the results are limited to the literature explored and to the case study examined.

Adaptive Disassembly Sequence Planning for VR Maintenance Training via Deep Reinforcement Learning

2021 ◽  
Author(s):  
Haoyang Mao ◽  
Zhenyu Liu ◽  
Chan Qiu
Keyword(s):  
Reinforcement Learning ◽  
Fitness Function ◽  
Training System ◽  
Credit Assignment ◽  
Disassembly Sequence Planning ◽  
Rapid Repair ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Markov Decision ◽  
Maintenance Work

Abstract Given the great inconvenience caused by the randomness of the fault to the maintenance work, it is necessary to perform on-site and efficient disassembly planning for the faulty parts and present them in combination with virtual reality (VR) technology to achieve rapid repair. As a promising method in solving dynamistic and stochastic problems, deep reinforcement learning (DRL) is adopted in this paper for the solution of adaptive disassembly sequence planning (DSP) in the VR maintenance training system, in which sequences can be generated dynamically based on user inputs. Disassembly Petri net is established to describe and model the disassembly process, and then the DSP problem is defined as a Markov decision process (MDP) that can be solved by the deep Q-network (DQN). For handling the temporal credit assignment with sparse rewards, the long-term return in DQN is replaced with the fitness function of the genetic algorithm (GA). Meanwhile, the update method of gradient descent in DQN is adopted to speed up the iteration of the population in GA. A case study has been conducted to prove that the proposed method can provide better solutions for DSP problems in terms of VR maintenance training.

A genetic algorithm for product disassembly sequence planning

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2720-2726 ◽  
Author(s):  
Wang Hui ◽  
Xiang Dong ◽  
Duan Guanghong
Keyword(s):  
Genetic Algorithm ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Product Disassembly

A Hybrid Disassembly Sequence Planning Approach for Maintenance

2002 ◽  
Vol 2 (1) ◽  
pp. 28-37 ◽  
Author(s):  
J. R. Li , ◽  
S. B. Tor , and ◽  
L. P. Khoo
Keyword(s):  
Fitness Function ◽  
Hybrid Approach ◽  
Search Space ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Constraint Graph ◽  
Planning Approach ◽  
Disassembly Sequence

This paper describes a hybrid approach to handle disassembly sequence planning for maintenance. The product under maintenance is first modeled using a novel hybrid graph known as Disassembly Constraint Graph (DCG) which embodies complete disassembly information and can be used to prune the search space of disassembly sequences. Subsequently, a novel Tabu-enhanced GA engine is invoked to generate the near optimal disassembly sequences. A case study was used to illustrate the effectiveness of the proposed approach. The details of the DCG, the TS-enhanced GA engine and the fitness function used are presented in this paper.

Sign in / Sign up

Export Citation Format

Share Document