A Novel Lightweight Multi-Objective Optimization Design System for Vehicle Chassis Frames Based on ANFIS-SHAMODE-IWOA Model

• Published in: IEEE access Vol. 12; pp. 42214 - 42232
• Main Authors: Minqing, Zhao, Hongxi, Wang, Weihuan, Chen, Shanping, Ning
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; ANFIS model; Chassis truss; Design improvements; Design optimization; Design parameters; Efficiency; Global optimization; Heuristic methods; Indicators; Intelligent manufacturing systems; Lightweight; Machine learning; Mathematical models; multi-objective optimization; Multiple objective analysis; Optimization; Product design; Product development; Reliability analysis; Reliability engineering; Search problems; SHAMODE algorithm; Sociology; Spirals; Statistics; Trussed structures; Trusses; Vehicle dynamics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3379149

In the process of daily product development and design, optimizing the shape and external dimensions of the vehicle chassis truss structure while considering both weight and reliability indicators often involves interdisciplinary collaboration and inefficient communication, leading to repetitive mechanical labor and low efficiency. This is widely regarded as a difficult and challenging task. In order to improve the design quality of the chassis truss structure and enhance work efficiency, this paper proposes a novel lightweight multi-objective optimization design system for vehicle chassis trusses based on the ANFIS-SHAMODE-IWOA model. Firstly, an adaptive spiral search strategy is introduced based on the multi-objective hybrid metaheuristic algorithm (SHAMODE) which is based on successful history. Then, a new SHAMODE-IWOA algorithm is proposed. In order to estimate the reliability level of the chassis truss structure under different design parameter combinations, a new ANFIS-SHAMODE-IWOA model is constructed by using the proposed SHAMODE-IWOA algorithm to learn the ANFIS model. Finally, in order to obtain the optimal design parameter combination, multi-objective optimization based on minimum design quality and optimal reliability measurement functions is studied using the SHAMODE-IWOA algorithm. Experimental results show that SHAMODE-IWOA has leading global optimization capability on CEC2017 test set benchmark functions. Compared with other intelligent models, the ANFIS-SHAMODE-IWOA model has better performance in reliability coefficient estimation. At the same time, the SHAMODE-IWOA algorithm obtains a more optimal design parameter combination for the chassis truss, with improvements of 9.5%, 12.5%, and 15% in Mass, Bending, and Torsion indicators, respectively. Ultimately, the proposed ANFIS-SHAMODE-IWOA multi-objective optimization design system, as a novel intelligent model, can be used to evaluate the reliability of chassis truss structures, improve development and design efficiency, and obtain the best design parameter combination, which is beneficial to improving the level of green intelligent manufacturing design.