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A multi-objective approach for robust structural topology optimization
Nathana, Palar P.S.a, Zuhal L.R.a
a Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Bandung, West Java, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.This paper presents a robust multi-objective approach for structural topology optimization. The performance and the stability are two conflicting objectives, hence, it is sometimes preferable to treat them as multiple objectives to gain important insight regarding various optimal topologies. The optimization problem is formulated as a simultaneous minimization of expectation and standard deviation of compliance in the presence of uncertainties in loading magnitude, boundary conditions, or material properties. It is imperative to obtain a good representation of the Pareto front with as few finite element simulations as possible. This paper proposes the combination of topology optimization with Polynomial Chaos Expansion (PCE) and Pareto Optimal Tracing (POT) to seek diverse Pareto optimal solutions efficiently. A benchmark test case with two and three random loads is used to test efficacy of the proposed method and the results are compared to the deterministic optimization. We studied the impact of the PCE to reduce the number of realizations and POT to reduce the number of iterations on the estimation of the Pareto front with various combinations of weights assigned to the expectation and the standard deviation. Results show that robust topology optimization with POT and PCE yielded better and diverse topologies than the conventional approach, which restarts the search from the uniform distributed density as the baseline design.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]