A Data-driven Decision-making Approach for Complex Product Design Based on Deep Learning

Traditional complex product design methods rely too much on the designer's experience and lack methodology, so they are susceptible to subjective factors. It is easy to overlook some critical influencing factors. The big data generated in the design process contains much knowledge and provides a new perspective for decision-making. This paper proposes a data-driven decision-making approach for complex product design based on deep neural network. Correlation analysis is used to find the critical dimensions of big data that affect decision-making. The big data generated in the complex product design process is analyzed through the deep neural network, and the value of design variables can be predicted. Finally, an experiment was conducted with a complex aerospace product, which proved the validity and accuracy of the approach proposed in this paper.