Abstract : Over a wide range of modern engineering design, numerical optimization plays crucial roles in diverse decision-making processes. This minisymposium aims to bring together recent advances in various aspects of structural and engineering optimization. The topics of interest include, but are not limited to
- new advances in structural optimization methods,
- surrogate modeling and digital twins for engineering optimization,
- multi-scale and microstructral topology optimization,
- machine learning and data-driven approaches to optimization.
[04641] Topology optimization reducing the dynamic instability of squeal noise
Format : Talk at Waseda University
Author(s) :
SolJi Han (Hanyang University)
GilHo Yoon (hanyang university)
Abstract : This study focuses on topology optimization considering the dynamic instability of squeal noise. In this study, the instability value caused by the frictional force is analysed through the eigenvalue analysis, and the sensitivity is calculated by left and right eigenvectors. With the present development, it is possible to optimize a structure that effectively reduces the instability value. To verify this study, several optimization examples are considered.
[03974] Structural simulation and optimization to improve the quality of metal additive manufacturing
Format : Talk at Waseda University
Author(s) :
Akihiro Takezawa (Waseda University)
Abstract : Reduction of the residual warpage generated through fabrication is an emerging issue in metal laser powder bed fusion additive manufacturing (AM). Regarding the minimization of the residual warpage of the lattice infill structures, simultaneous optimization of the laser hatching orientation and lattice density distribution is conducted in this study to confirm their synergetic effect.
[03594] Machine-learning assisted topology optimization with structural gene inheritance
Format : Talk at Waseda University
Author(s) :
Weisheng Zhang (Dalian University of Technology)
Sung-Kie Youn (KAIST)
Xu Guo (Dalian University of Technology)
Abstract : A machine-learning assisted topology optimization approach is proposed for structural design with structural gene inheritance. This work establishes a novel framework to systematically integrate structural topology optimization with subjective human design preferences. To embed the structural gene into the design, neural style transfer technique is adopted to measure and generate the prior knowledge from a reference image with the concerned structural gene (such as biological characteristic, artistic flavor and manufacturing requirement, etc.). By using different convolutional layers in the VGG-19 model-based CNN, both the style and content of the structural gene can be constructed from low to high levels of abstraction. The measured knowledge can then be integrated into pixel-based topology optimization as a formal similarity constraint. Both 2D and 3D problems are solved to illustrate the effectiveness of the proposed approach where the inheritance of the structural gene can be achieved in a systematic manner.
[03346] Numerical and experimental investigation on process parameters optimization in rapid heat cycle molding
Format : Talk at Waseda University
Author(s) :
Satoshi Kitayama (Kanazawa University)
Yusuke Yamazaki (Sodick Co. Ltd.)
Yoshikazu Kubo (Sodick Co. Ltd.)
Shuji Aiba (Sodick Co. Ltd.)
Abstract : Weldline that is forms when two or more melt fronts meet is one of the major defects in plastic injection molding (PIM), and it is important to reduce the wedline as much as possible. Rapid heat cycle molding (RHCM) is one of the effective PIMs for weldline reduction, but the process parameters are determined by the trial and error method. This paper optimizes the process parameters in RHCM by CAE and design optimization technique. The experiment is also conducted to examine the validity of the proposed approach.
[01219] Acoustic metamaterials design with non-gradient material-field series-expansion topology optimization
Format : Talk at Waseda University
Author(s) :
Xiaopeng Zhang (Dalian Dalian University of TechnologyUniversity of Technology)
Abstract : Designing bandgap acoustic metamaterial has important application potential but is also challenging. This study proposes a systematic topology optimization method of acoustic metamaterial to open single and multiple low-frequency bandgaps. To describe the complicated topologies of the multi-material acoustic metamaterial with a lower number of design variables, the material-field series expansion (MFSE) technique is adopted. With the interpolated scheme and the multi-material field description model, a clear three-material topology can be determined by two independent material-field functions with only 100 independent design variables. This greatly reduces the design variables for the topological description of the microstructure, enabling the problem to be solved using non-gradient optimization algorithms. The self-adaptive strategy based sequential Kriging optimization algorithm is then introduced to solve the optimization problems. Numerical examples prove the proposed topology optimization method can effectively provide the acoustic metamaterial designs with ultra-wide low-frequency bandgaps.
[04675] Multiscale topology optimization to maximize dissipated energy
Format : Talk at Waseda University
Author(s) :
Takashi Yamamoto (Kogakuin University)
Abstract : In this study, a multiscale topology optimization method for micro structure is proposed utilizing the homogenization method based on the asymptotic expansion. Energy dissipated in macroscopic component is maximized at a prescribed frequency. Design sensitivities of the homogenized macroscopic properties are calculated by applying the adjoint variable method in the frame work of the homogenization method. Adjoint variable method is hierarchically applied to obtain design sensitivities of the macroscopic dissipated energy.
[03135] Multiscale topology optimization of fiber reinforced composite using homogenization design method.
Format : Talk at Waseda University
Author(s) :
Jaewook Lee (Gwangju Institute of Science and Technology (GIST))
Abstract : This presentation shows topology optimization of fiber reinforced composite with spatially-varying fiber structure. The numerical homogenization of the microscale unit-cell is performed at various fiber sizes. Then, the effective elasticity tensor is represented as the function of fiber size and orientations, together with the density. Topology optimization is carried out at macroscale, and the microscale composite structure is restored using the de-homogenization method. Both 2D and 3D design examples will be provided.
