Construction and Application of Product Styling Process Design Optimization Model Based on Artificial Neural Network

Abstract

In this study, Artificial Neural Networks as an emulator of brain functionality are introduced in the Product Styling Process Design Optimization Model, PSPDOM, to optimize design since ANNs learn and adapt to new data for optimization. The model offers optimal styling solutions with various design parameters and consumer preferences toward high-quality and quantitative product development. Data gathering, parameter selection, model designing, and validation in diverse product categories are a part of it. Key outcomes of the result show high value added to enhancement in consumer satisfaction, reduced cost of production, and lessened time-to-market. Thus, PSPDOM is proven useful in augmenting competitiveness and market viability. The paper further develops design engineering and AI with new insights into ANNs and places a firm framework for their real application. Cooperation between experts and data scientists enhances innovation and future work is recommended to further enhance the capabilities of ANN.