Multi-objective Optimization-Driven Design: Generative Design Approach for Manufacturing of a Train Bogie

Abstract

In recent years, advances in additive manufacturing (AM) technologies, particularly in Direct Energy Deposition (DED) as Wire Arc Additive Manufacturing (WAAM) and Laser Wire Directed Energy Deposition (LW-DED), have enabled engineers to produce large complex structures. Consequently, these developments drive a paradigm shift in how we conceptualize and manufacture complex components, pushing the limits of what is possible in engineering and design. As a result, there is a growing need for optimized structures considering the material constraints and advantages. This study uses a multi-objective optimization-driven engineering design approach to optimize the shape and materials of a train bogie as a case study. From the material perspective, different materials were selected to benefit from the high-strength steel (HSL) and low-carbon steel (LCS) and achieve weight reduction while considering other structural constraints. We generated multiple prospective designs using the generative design feature from Autodesk Fusion 360. Subsequently, safety factors are collected from these models using AbaqusCAE and the fatigue data with fe-safe. Moreover, a multi-objective optimization approach is applied to find the best possible design based on the resulting safety factor, weight, fatigue life, and maximum displacement of the proposed part. Finally, the collected results are compared, and a set of optimum designs is presented based on different criteria detailed in the study.