A Digital Design Agent for Ground Vehicles

The design of transportation vehicles, whether passenger or commercial, typically involves a lengthy process from concept to prototype and eventual manufacture. To improve competitiveness, original equipment manufacturers are continually exploring ways to shorten the design process. The application of digital tools such as computer-aided-design and computer-aided-engineering, as well as model-based computer simulation enable team members to virtually design and evaluate ideas within realistic operating environments. Recent advances in machine learning (ML)/artificial intelligence (AI) can be integrated into this paradigm to shorten the initial design sequence through the creation of digital agents. A digital agent can intelligently explore the design space to identify promising component features which can be collectively assessed within a virtual vehicle simulation. In this paper, the framework for a python-based ML/AI virtual agent will be presented and applied to a vehicle suspension within an off-road ground vehicle. A case study investigates four suspension designs and their corresponding component feature characteristics with attention focused on ride quality, weight, and maximum force. Representative results are presented and discussed to offer insight into the methodology and briefly contrast it with traditional optimization approaches. The path forward for the inclusion of multiple design agents within a “collaborative digital design bowl” are presented in the conclusion.