<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this article, some complex parameters of the product and design processes, how to match and optimise the sub-parts of related industrial products and how to improve the quality of the corresponding products and the competitiveness of the product in the international market are discussed in this article. We also build an algorithm based on the particle swarm and XGBoost algorithms, combined with the intelligent computing of the Internet of Things (IoT). We transform some uncertain factors in the process of the industrial product design process through the fuzzy matrix, select the optimal design through the optimised intelligent computing of the IoT scheme and compare the influence of the scheme before and after optimisation on production efficiency. The results show that the method proposed in this article can reduce the time-consumption of optimal solution selection by 42.85%–52.94%. In addition, selecting the optimal solution for each field in a targeted manner can increase the overall production efficiency of the product by about 5%, reaching between 93.6% and 96.5%, which may save raw materials and create more economic value.</p>
</abstract>

In this article, some complex parameters of the product and design processes, how to match and optimise the sub-parts of related industrial products and how to improve the quality of the corresponding products and the competitiveness of the product in the international market are discussed in this article. We also build an algorithm based on the particle swarm and XGBoost algorithms, combined with the intelligent computing of the Internet of Things (IoT). We transform some uncertain factors in the process of the industrial product design process through the fuzzy matrix, select the optimal design through the optimised intelligent computing of the IoT scheme and compare the influence of the scheme before and after optimisation on production efficiency. The results show that the method proposed in this article can reduce the time-consumption of optimal solution selection by 42.85%–52.94%. In addition, selecting the optimal solution for each field in a targeted manner can increase the overall production efficiency of the product by about 5%, reaching between 93.6% and 96.5%, which may save raw materials and create more economic value.

Research on product process design and optimisation model based on IoT intelligent computing

Applied Mathematics and Nonlinear Sciences

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

In this article, some complex parameters of the product and design processes, how to match and optimise the sub-parts of related industrial products and how to...

Research on product process design and optimisation model based on IoT intelligent computing

Publicado en línea: 23 dic 2022

Páginas: 653 - 664

Recibido: 07 jun 2022

Aceptado: 16 ago 2022

DOI: https://doi.org/10.2478/amns.2021.2.00264

Palabras clave
Internet of Things, Intelligent Computing, Industrial Design, Optimisation Models

© 2023 Mingyan Yang, published by Sciendo

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

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Research on product process design and optimisation model based on IoT intelligent computing

Publicado en línea: 23 dic 2022

Páginas: 653 - 664

Recibido: 07 jun 2022

Aceptado: 16 ago 2022

DOI: https://doi.org/10.2478/amns.2021.2.00264

Palabras claveInternet of Things, Intelligent Computing, Industrial Design, Optimisation Models

© 2023 Mingyan Yang, published by Sciendo

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

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Palabras clave
Internet of Things, Intelligent Computing, Industrial Design, Optimisation Models