  The maximum stress minimization problem is among the most important topics
for structural design. The conventional gradient-based topology optimization
methods require transforming the original problem into a pseudo-problem by
relaxation techniques. Since their parameters significantly influence
optimization, accurately solving the maximum stress minimization problem
without using relaxation techniques is expected to achieve extreme performance.
This paper focuses on this challenge and investigates whether designs with more
avoided stress concentrations can be obtained by solving the original maximum
stress minimization problem without relaxation techniques, compared to the
solutions obtained by gradient-based topology optimization. We employ
data-driven multifidelity topology design (MFTD), a gradient-free topology
optimization based on evolutionary algorithms. The basic framework involves
generating candidate solutions by solving a low-fidelity optimization problem,
evaluating these solutions through high-fidelity forward analysis, and
iteratively updating them using a deep generative model without sensitivity
analysis. In this study, data-driven MFTD incorporates the optimized designs
obtained by solving a gradient-based topology optimization problem with the
p-norm stress measure in the initial solutions and solves the original maximum
stress minimization problem based on a high-fidelity analysis with a
body-fitted mesh. We demonstrate the effectiveness of our proposed approach
through the benchmark of L-bracket. As a result of solving the original maximum
stress minimization problem with data-driven MFTD, a volume reduction of up to
22.6% was achieved under the same maximum stress value, compared to the initial
solution.
