Abstract
In Industry 4.0, data are sensed and merged to drive intelligent systems. This research focuses on the optimization of selective assembly of complex mechanical products (CMPs) under intelligent system environment conditions. For the batch assembly of CMPs, it is difficult to obtain the best combinations of components from combinations for simultaneous optimization of success rate and multiple assembly quality. Hence, the Taguchi quality loss function was used to quantitatively evaluate each assembly quality and the assembly success rate is combined to establish a many-objective optimization model. The crossover and mutation operators were improved to enhance the ability of NSGA-III to obtain high-quality solution set and jump out of a local optimal solution, and the Pareto optimal solution set was obtained accordingly. Finally, considering the production mode of Human–Machine Intelligent System interaction, the optimal compromise solution is obtained by using fuzzy theory, entropy theory and the VIKOR method. The results show that this work has obvious advantages in improving the quality of batch selective assembly of CMPs and assembly success rate and gives a sorting selection strategy for non-dominated selective assembly schemes while taking into account the group benefit and individual regret.
Highlights
Academic Editor: Arkadiusz GolaIn the manufacturing environment of Industry 4.0, it becomes easy to accurately collect various data during production
The Pareto optimal solution set obtained by the non-dominated sorting genetic algorithm (NSGA)-III-I algorithm solving the selective assembly high-dimensional many-objective optimization model is a selective assembly high-dimensional many-objective optimization model is a non-dominated solution set, which includes a large number of mutually non-dominated non-dominated solution set, which includes a large number of mutually non-dominated solutions
In order to verify the effectiveness of the NSGA-III-I algorithm in solving manyobjective selective assembly optimization problems, a comparative simulation experiment was designed for the NSGA-III-I algorithm, interchangeable assembly (IA), NSGA-II, and NSGA-III
Summary
Academic Editor: Arkadiusz GolaIn the manufacturing environment of Industry 4.0, it becomes easy to accurately collect various data during production. Relying on a large amount of data, intelligent systems can optimize and make decisions in the production process. Most mechanical products are manufactured by the processing and assembly of components [1]. Under the condition of high requirements for product matching accuracy, due to the limitation of component processing capacity and manufacturing cost, it is unrealistic and uneconomical to completely rely on improving the accuracy of the processing process to meet and improve the product matching accuracy [2]. The demand of producers is to reduce manufacturing costs as much as possible while ensuring quality to obtain maximum product competitiveness. Selective assembly is one of the feasible methods to achieve high-precision assembly and reduce costs by using data and intelligent systems
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