Investigations on motorbike frame material and comparative analysis using generative design and topology optimization

Abstract

The frame is one of the most important load-bearing structures of a motorbike. Since various components are mounted on the frame, hence rigorous analysis is required based on different design techniques. Among the different techniques for design, generative design and topology optimization are used in the present work. Generative design is a design exploration process that uses machine learning and cloud computing and generates a large number of designs starting from restricted design data. The generative design domain includes geometric feasibility, manufacturability, and cost analysis, which provides the opportunity to achieve design objectives for optimized models. Topology optimization is a mathematical methodology that re-arranges the material distribution within the design domain, for different loads, boundary conditions, and constraints on functional and optimized models. A comparative study based on the mechanical properties of two different designs of motorbike frames created using generative design and topology optimization is proposed. Both of these processes are aimed towards light-weighting the structural models to maximize the performance of the intended design. The practical use of the generative design and topology optimization methodology is demonstrated using a generic CAD-based design of a trellis frame structure and the generative workspace in Autodesk FUSION 360. Comparative analysis was carried out between the conventional Trellis frame, Generative Frame and Topology Optimized frame structures. Distribution of induced stresses within the frame, nodal displacement and equivalent strain are investigated in the present work. Structural mass is decreased by 26.63% in the case of topology optimization and 34.29 % for generative design.