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Machine learning based Topology Optimization

Wihrén, Lina LU (2023) In TFHF-5000 FHLM01 20231
Solid Mechanics
Department of Construction Sciences
Abstract
This thesis aims to find a design suggestion for the chassis of a new micromobility vehicle developed by the company LEVTEK SWEDEN AB by using a multiple load, compliance based topology optimization. For this purpose, 9 static load cases are suggested, 4 of which have been derived from dynamic scenarios using an equivalent static load inspired approach based on free-body diagrams. The results from the topology optimization is presented as a design suggestion, but further post-processing is needed. Additionally, the extent of which machine learning could be applied for speeding up of the topology optimization
was explored, and it was concluded to be feasible on a 2D cross section of the deck given the state-of-the-art and available... (More)
This thesis aims to find a design suggestion for the chassis of a new micromobility vehicle developed by the company LEVTEK SWEDEN AB by using a multiple load, compliance based topology optimization. For this purpose, 9 static load cases are suggested, 4 of which have been derived from dynamic scenarios using an equivalent static load inspired approach based on free-body diagrams. The results from the topology optimization is presented as a design suggestion, but further post-processing is needed. Additionally, the extent of which machine learning could be applied for speeding up of the topology optimization
was explored, and it was concluded to be feasible on a 2D cross section of the deck given the state-of-the-art and available resources. For this purpose a convolutional neural network proposed by Sosnovik & Oseledets (2017) was used, which demonstrated strong potential for learning a specific design domain, and it was investigated if the close-to-optimal solutions found by the network could be used as an initial guess for further topology optimization. It is concluded that transferability and consistency needs to be further investigated for this deep-learning approach. (Less)
Please use this url to cite or link to this publication:
author
Wihrén, Lina LU
supervisor
organization
course
FHLM01 20231
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Machine learning, Topology Optimization, Artificial neural networks, Micromobility vehicles
publication/series
TFHF-5000
report number
TFHF-5255
language
English
id
9134942
date added to LUP
2023-08-28 12:33:00
date last changed
2023-08-28 12:33:00
@misc{9134942,
  abstract     = {{This thesis aims to find a design suggestion for the chassis of a new micromobility vehicle developed by the company LEVTEK SWEDEN AB by using a multiple load, compliance based topology optimization. For this purpose, 9 static load cases are suggested, 4 of which have been derived from dynamic scenarios using an equivalent static load inspired approach based on free-body diagrams. The results from the topology optimization is presented as a design suggestion, but further post-processing is needed. Additionally, the extent of which machine learning could be applied for speeding up of the topology optimization
was explored, and it was concluded to be feasible on a 2D cross section of the deck given the state-of-the-art and available resources. For this purpose a convolutional neural network proposed by Sosnovik & Oseledets (2017) was used, which demonstrated strong potential for learning a specific design domain, and it was investigated if the close-to-optimal solutions found by the network could be used as an initial guess for further topology optimization. It is concluded that transferability and consistency needs to be further investigated for this deep-learning approach.}},
  author       = {{Wihrén, Lina}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{TFHF-5000}},
  title        = {{Machine learning based Topology Optimization}},
  year         = {{2023}},
}