Industrial Product Design based on Convolutional Neural Network Model

Abstract

Industrial design refers to the practice of applying design principles to produce goods for large-scale manufacturing. Design is the imaginative process that comes before a thing is made to determine and define its shape and characteristics. Thorough industry evaluation is becoming more important to the creation of innovative primary commodities. In this paper, we proposed the use of Convolutional neural networks (CNN) in the design of industrial product design (IPD). Data from China's electronic industrial sector shows that incentives may boost technological advancement in businesses, but an excess of them may limit progress. Subsequently, the manufacturing set of data from china was gathered. Normalization may be used as a preprocessing step for the gathered data. In addition, the CNN method has been suggested. Traditional methods are contrasted with these in terms of several different parameters, including accuracy, precision, recall, implementation cost, and energy utilization. The implementation cost of the proposed model is 70% which seems to be less than the other traditional approaches.