Intelligent Algorithm-Driven Product Design Process Optimization: Intelligent Transformation of Product Design Processes

Abstract

In this study, the Petri net model is initially employed to establish a framework for the product design process, subsequently enhanced by the integration of an ant colony algorithm to augment the intelligent capabilities of product design. Further refinement is achieved through the incorporation of a genetic algorithm, which optimizes the model by aligning the product design challenges more closely with real-world scenarios. This paper introduces a bespoke ant colony optimization algorithm tailored for discrete variable-oriented product design, focusing on optimizing the size and structural topology of products. A test enterprise is selected to demonstrate the efficacy of this approach. The analysis is conducted using a real case analysis method, assessing impacts on product manufacturing costs and input-output efficiency. Results from the implementation show a substantial reduction in the overall cost of intelligent product design by 15.819%. Following the adoption of smart product design strategies in 2017, there was a notable reduction in capital investment, amounting to 267,345,000 yuan, while maintaining the initial annual production volume. Over the period from 2016 to 2020, the employment of design engineers decreased by an average of 3,815 daily without compromising performance levels. The application of intelligent product process design not only achieved optimal input-output efficiency but also underscored the success of intelligent transformation in the product design process.