Application Of Convolutional Neural Network in Industrial Product Design

Abstract

The term "industrial design" describes the process of creating products using design principles for mass production. Design is the pre-production mental exercise that defines the form and attributes of an object. The development of novel primary commodities is increasingly dependent on comprehensive industry evaluation. We present a proposal for industrial product design (IPD) that makes use of convolutional neural networks (CNN). Incentives may encourage firms to develop new technologies, but too many of them might stifle innovation, according to data from China's electronic manufacturing sector. As a result, we collected the Chinese manufacturing data set. As a preliminary step in processing the collected data, normalization might be employed. Another approach that has been proposed is the CNN method. These are compared to more conventional approaches on a number of metrics, such as precision, accuracy, recall, implementation cost, and energy consumption. Compared to more conventional methods, the suggested methodology appears to have a lower implementation cost of 70%.