Abstract: Traditional design requires designers to envisage a product operating environment in order to identify customer needs. Analyzing product usage context by collecting actual product operating data during the product in use empowers new opportunities for the projection of requirement specifications and understanding of use case scenarios. This paper proposes a data-driven inverse design optimization approach to provide decision support to product personalization design. A closed-loop decision-making framework is formulated by integrating forward design and inverse problem solving within a coherent framework of data-driven analysis. An application to the transmission system personalization design of wheel loaders is presented to demonstrate how personalized product usage contexts are identified through inverse analysis of product operating data under different operating conditions. A particle swarm optimization (PSO) algorithm incorporated with Simulink simulation is developed to solve the multi-objective optimization of power performance and fuel economy for wheel loaders.

Link: https://www.sciencedirect.com/science/article/pii/S2452414X21000133?via%3Dihub