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Generalizing Machine Intelligence Techniques for Automotive Body Frame Design

Ramnath, Satchit
http://orcid.org/0000-0002-2509-0626
http://rave.ohiolink.edu/etdc/view?acc_num=osu1658369343417838

Abstract Details

2022, Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering.
Thin-walled frames are prevalent in automotive body structures as they provide lower vehicle weight to meet strength and stiffness requirements for different types of loading cases. The inner and outer styling surfaces drive the shape of the design domain, which constrains structural configurations, and the size and shape of parts. With an increasing demand for a shorter development and production time and cost of automotive production and to stay competitive in the field, automotive manufacturers are using advanced computation techniques like Topology Optimization (TO) to create new and better performing designs. Although automotive structural engineers have been using thin-walled frames for years, they are keen on getting additional improvements through TO. However, current technology is not capable of producing thin-walled shapes. The research presented here aims to bridge the gap between the topology optimized automotive body structures and the traditional manufacturing processes by automatically post-processing the results to create hollow cross-sections for the body components to reside within a given space. The inner and outer styling surfaces control the space boundaries that constrain the structural configurations and the size of parts. Topology optimization is performed on an FEA model that represents the design domain using voxel elements, and the results typically mesh/triangulated surfaces stored as *.stl files. The objective is to develop a systematic approach for converting organic, solid, and monolithic shapes, obtained from TO, into parameterized CAD models for thin-walled structures produced by sheet metal stamping. The methods developed can perform concurrent shape and size optimization of the cross-section of thin-walled components, as opposed to the traditional method where the shape of the cross-section is obtained from an existing, which is then optimized for size. The area and moment of inertias of the cross-section are the two metrics used to match the thin-walled cross-section to the cross-section of the solid TO results. An initial set of cross-sections for thin-walled structures are generated using a feature-based method, which is further optimized to match the cross-sectional properties. The optimization process is referred to as “morphing”, which is done using a genetic algorithm. All the cross-sections generated must adhere to multiple constraints such as boundary constraints, manufacturing constraints, and specified bounds. The boundary constraints are imposed by an existing design space. In the absence of a design space, two methods have been developed to synthesize a new design geometry. The manufacturing constraints are based on the stamping process used to manufacture the components. These constraints are guided by multiple sets of rules such as topology rules, geometry rules, and manufacturing rules. A thin-walled component is generated from multiple optimized cross-sections lofted together. The representation and generation of cross-sections, and the corresponding thin-walled components, are generalized using features and parameters so that the methods can be used for both automotive and non-automotive applications. It is also necessary to generate and evolve the design of the joints along with their adjacent hollow components. A formal approach to generating conceptual designs in CAD will provide the designer with a starter design that can be used to further improve the model and prepare for analysis. The generation of conceptual joint designs is governed by various rules on manufacturing, joining technology, materials, etc. A formal method for integrating joint design with thin-walled components has also been evaluated and discussed.
Jami Shah (Advisor)
Ali Nassiri (Committee Member)
Farhang Pourboghrat (Committee Member)
Shawn Midlam-Mohler (Committee Member)
242 p.
Mechanical Engineering
computer aided engineering; structural optimization; genetic algorithm; design automation; thin-walled components; topology optimization

Recommended Citations

Citations

  • Ramnath, S. (2022). Generalizing Machine Intelligence Techniques for Automotive Body Frame Design [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1658369343417838

    APA Style (7th edition)

  • Ramnath, Satchit. Generalizing Machine Intelligence Techniques for Automotive Body Frame Design. 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1658369343417838.

    MLA Style (8th edition)

  • Ramnath, Satchit. "Generalizing Machine Intelligence Techniques for Automotive Body Frame Design." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1658369343417838

    Chicago Manual of Style (17th edition)

osu1658369343417838
185
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This open access ETD is published by The Ohio State University and OhioLINK.