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<p>The purpose of exploring the application of computer-aided design technology in industrial product design is to realize the diversification of industrial product design and meet the personalized customization needs of customers. Starting from computer-aided technology, this paper illustrates the optimization of CAD design by using a genetic algorithm and constructs a bipolar progressive interactive genetic algorithm using bipolar progressive ranking and fuzzy fitness jointly. Genetic algorithm genotypes in CAD process product design are defined, and a comparative evaluation experimental analysis of industrial product design is performed with the AR-IGA algorithm. Regarding the number of iterations, the average number of iterations for the AR-IGA algorithm to complete the color-matching design of process products among ten testers was 9.34, which was 36.42% lower than that of the TIGA algorithm. The average evaluation elapsed time of the AR-IGA algorithm was 246.9s, which was 24.12% less than the average evaluation elapsed time of the TIGA algorithm. This shows that computer-aided technology optimized by genetic algorithms can effectively improve the efficiency of industrial product design and satisfy more customers to realize personalized design simultaneously.</p>
</abstract>

The purpose of exploring the application of computer-aided design technology in industrial product design is to realize the diversification of industrial product design and meet the personalized customization needs of customers. Starting from computer-aided technology, this paper illustrates the optimization of CAD design by using a genetic algorithm and constructs a bipolar progressive interactive genetic algorithm using bipolar progressive ranking and fuzzy fitness jointly. Genetic algorithm genotypes in CAD process product design are defined, and a comparative evaluation experimental analysis of industrial product design is performed with the AR-IGA algorithm. Regarding the number of iterations, the average number of iterations for the AR-IGA algorithm to complete the color-matching design of process products among ten testers was 9.34, which was 36.42% lower than that of the TIGA algorithm. The average evaluation elapsed time of the AR-IGA algorithm was 246.9s, which was 24.12% less than the average evaluation elapsed time of the TIGA algorithm. This shows that computer-aided technology optimized by genetic algorithms can effectively improve the efficiency of industrial product design and satisfy more customers to realize personalized design simultaneously.

Analysis of the application of computer-aided design in industrial product design based on

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The purpose of exploring the application of computer-aided design technology in industrial product design is to realize the diversification of industrial...

Analysis of the application of computer-aided design in industrial product design based on

Publicado en línea: 23 sept 2023

Páginas: -

Recibido: 15 dic 2022

Aceptado: 03 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00361

Palabras clave
Computer-aided technology, Genetic algorithm, AR-IGA algorithm, Industrial product design, CAD design

© 2023 Yun Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the application of computer-aided design in industrial product design based on

Publicado en línea: 23 sept 2023

Páginas: -

Recibido: 15 dic 2022

Aceptado: 03 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00361

Palabras claveComputer-aided technology, Genetic algorithm, AR-IGA algorithm, Industrial product design, CAD design

© 2023 Yun Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Computer-aided technology, Genetic algorithm, AR-IGA algorithm, Industrial product design, CAD design