Optimization and CAE Automation

Based topology optimization of BIW structures

Objective

• Maximize stiffness of components for regular working conditions
• Maximize energy absorption in exceptional loading conditions, for instance in crash events

Results

• It was demonstrated that the method yields a good trade-off result in terms of stiffness and crash energy absorption for all load cases
• These findings encourage a systematic application of the method in the industrial vehicle design process

Publications/Thesis:

Aulig, N, Ramnath, S, Nutwell, E, Horner, K. “Design Domain Dependent Preferences for Multi-Disciplinary Body-in-White Concept Optimization”. 15^th International LS-Dyna Users Conference 2018.

Ramnath, S, Nutwell, E, Aulig, N, Horner, K. “Detail Design Evaluation of Extruded Sections on a Body-in-White Concept Model”. 15^th International LS-Dyna Users Conference 2018.

Vehicle design concept optimization using load case preference patterns

Motivation

• Perform Design of Experiments on Load Case Preferences.

Objective

• Create User Interface for Industrial Development Applications for Non-Expert Users.
• Enables Quick Studies on Different Concept Designs.

Results

• Successfully demonstrated integration and application within a commercial software package.
• Enabled the use of the software readily accessible to the industry. 

Publications/Thesis:
S. Ramnath, N. Aulig, M. Bujny, S. Menzel, I. Gandikota, K. Horner, “Load Case Preference Patterns based on Parameterized Pareto-Optimal Vehicle Design Concept Optimization” in 12th European LS-DYNA Conference, 2019.

Developing machine intelligence techniques for automotive body frame engineering design

Research questions

• How to generate manufacturable car body frames using solid meshes of monolithic structures computed by topology optimization?

• Is it feasible to fit hollow fabricated component sections between inner and outer styling surfaces to match inertia properties of solid sections resulting from topology optimization?
• How to generalize the methodology to any type of structure?

Methods developed

• Generalization: Determine design space which must account for voids that come from TO.
  • Machine learning for edge detection of TO results for design domain creation.
  • Automated 3D vectorization for creating curves/lines from 2D edges for design domain creation.
• Generalization: Separate solid and potential beam segments.
• Cross-section generation:
  • Graph grammar-based rules to evolve cross-sections.
  • Genetic algorithm to optimize cross-sections with manufacturing constraints to meet target fitness function.

Generating large data-sets for AI systems

Motivation

• Using AI Systems to generate better designs, either obtained from a combination of existing ones, or provide insight into completely new design concepts exceeding performance.

Objective

• Automate CAD data generation.
• Automate FEA simulation to generate performance data.
• Produce over 100k data sets for AI systems.

Result

• Published Automotive Engineering Research Dataset for researchers to use in training machine learning algorithms specific to automotive design and performance (OSU-Honda automobile hood dataset (CarHoods10k) https://doi.org/10.5061/dryad.2fqz612pt)

Publications/Thesis:
Ramnath, S, Haghighi, P, Kim, JH, Detwiler, D, Berry, M, Shah, JJ, Aulig, N, Wollstadt, P, & Menzel, S. "Automatically Generating 60,000 CAD Variants for Big Data Applications." Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1: 39th Computers and Information in Engineering Conference. Anaheim, California, USA. August 18–21, 2019. V001T02A006. ASME. https://doi.org/10.1115/DETC2019-97378

Ramnath, S, Haghighi, P, Ma, J, Shah, JJ, & Detwiler, D. "Design Science Meets Data Science: Curating Large Design Datasets for Engineered Artifacts." Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 9: 40th Computers and Information in Engineering Conference (CIE). Virtual, Online. August 17–19, 2020. V009T09A043. ASME. https://doi.org/10.1115/DETC2020-22377

Ramnath, S, Ma, J, Shah, JJ, & Detwiler, D. "Intelligent Design Prediction Aided by Non-Uniform Parametric Study and Machine Learning in Feature Based Product Development." Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 41st Computers and Information in Engineering Conference (CIE). Virtual, Online. August 17–19, 2021. V002T02A025. ASME. https://doi.org/10.1115/DETC2021-67923

P. Wollstadt, M. Bujny, S. Ramnath, J. J. Shah, D. Detwiler and S. Menzel, "CarHoods10k: An Industry-grade Data Set for Representation Learning and Design Optimization in Engineering Applications," in IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2022.3147013
Ramnath, Satchit et al. (2022), OSU-Honda automobile hood dataset (CarHoods10k), Dryad, Dataset, https://doi.org/10.5061/dryad.2fqz612pt

Current team members
Jami Shah, Professor, Mechanical and Aerospace Engineering 
Alex Adrian, Graduate Student, Mechanical and Aerospace Engineering
Abhishek Bolar, Graduate Student, Mechanical and Aerospace Engineering

Past Members:
Graduate Students:
Konrad Witek, Mechanical and Aerospace Engineering
Ang Li, Mechanical and Aerospace Engineering
Jiachen Ma, Mechanical and Aerospace Engineering
Yiming Jiang, Mechanical and Aerospace Engineering

Undergraduate Students:
Nolan LaMarche, Mechanical and Aerospace Engineering
Alex Borchers, Mechanical and Aerospace Engineering