New works on Heaviside functions part2(Machine Learning 2024)
- Topology optimization of stiff structures under self-weight for given volume using a smooth Heaviside function(arXiv)
Author : Prabhat Kumar
Abstract : This paper presents a density-based topology optimization approach to design structures under self-weight load. Such loads change their magnitude and/or location as the topology optimization advances and pose several unique challenges, e.g., non-monotonous behavior of compliance objective, parasitic effects of the low-stiffness elements, and unconstrained nature of the problems. The modified SIMP material scheme is employed with the three-field density representation technique (original, filtered, and projected design fields) to achieve optimized solutions~close~to~0–1. A novel mass density interpolation strategy is proposed using a smooth Heaviside function, which provides a continuous transition between solid and void states of elements and facilitates tuning of the non-monotonous behavior of the objective. A constraint that implicitly imposes a lower bound on the permitted volume is conceptualized using the maximum permitted mass and the current mass of the evolving design. Sensitivities of the objective and self-weight are evaluated using the adjoint-variable method. Compliance of the domain is minimized to achieve the optimized designs using the Method of Moving Asymptotes. The Efficacy and robustness of the presented approach are demonstrated by designing various 2D and 3D structures involving self-weight. The proposed approach maintains the constrained nature of the optimization problems and provides smooth and rapid objective convergence.
