Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences

Abstract

An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS) in combination with the Genetic Algorithm (GA) to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1) Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2) Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques.

Intelligent robot of inclined assembly sequence planning in Industrial 4.0

“…Interference analysis is a common method of calculating the assembly direction of parts [5]. The final pattern of assembly sequence is the optimal assembly sequence, and although the possible assembly sequence allows the assembly operation to be performed without any interference, some assembly sequences still have many repetitive motions during the assembly process and many literature studies Find out the best assembly sequence by different algorithms and different objective functions [1,9] 3 Research methods…”

“…In order to generate the best assembly sequence, several heuristic algorithms are proposed to solve the problem. The three most important ones are Genetic Algorithm (GA) [1] , Particle Swarm Optimization (PSO) [3] and ACO [9] . However, the input data of these algorithms such as the contact and interference between parts must be input manually.…”

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

“…They used the precedence graph to check the feasibility of the sequence. Later on HA was developed by Shan et al., 29 Ning and Gu, 30 Zhou et al., 31 Zhang et al., 32 Gunji et al., 33 Yanfeng Xing and Yansong Wang 34 and many others. In these, Zhang et al.…”

A penalty-guided fractal search algorithm for reliability–redundancy allocation problems with cold-standby strategy

“…A convenient sequence of assembly operations in manufacturing aspects of industry increases the product reliability. Gunji et al 49 developed an intelligent strategy to obtain an optimal assembly in order to enhance the product quality.…”