Design decisions have a large impact early in the design process. Optimization methods can help engineers improve their early decision making, however, design problems are often ill-posed for optimization at this early stage. This thesis develops engineering methods to use optimization during embodiment design, despite these difficulties. One common difficulty in designing mechanical systems is in handling the effects that design changes in one subsystem have on another. This is made more difficult in early engineering design, when design information is preliminary. Increased efforts have been made to use numerical optimization methods in early engineering design – because of the large impact early decisions have on subsequent development activities. One step toward executing meaningful optimizations in early design is the development of an optimization framework to be used when conditions are expected to change as the design progresses and new information is gained. This thesis presents a design framework that considers such change by subjecting the parametric updating of CAD models to optimization criteria specific to the problem at hand. Under the proposed framework, a part or subassembly is parametrically modeled in CAD; when changes are made to the subsystems that interact with the part or subassembly, it is then updated subject to design objectives and constraints. In this way, the updated part or subassembly satisfies system and subsystem level optimization criteria, reducing the need for the designer to react to design changes manually. It is used to reduce the weight of a Formula SAE suspension rocker by 18%, demonstrating the utility of this framework. Next, we develop methods to help engineers by giving them options and helping them explore during configuration generation. The design of multiple-bend, progressive-die-formed springs typically comprises four steps: (i) functional specification, (ii) configuration generation, (iii) configuration selection, and (iv) detailed shape and size optimization. Configuration generation fundamentally affects the success or failure of the design effort. This presents an important problem: by not generating potentially optimal configurations for further development in detailed design, the designer may unknowingly set the design on track for sub-optimal performance. In response, a method is developed that improves configuration generation. Specifically, an optimization-based spring configuration generator – without which, the generation would typically be based solely on designer creativity, experience, and knowledge. The proposed approach allows the designer to explore numerous optimization-generated spring configurations, which feasibly satisfy the functional specifications. The feasibility study is carried out before a final configuration is chosen for detailed development. Thus streamlining the designer's efforts to develop a design that avoids sub-optimality. We use the feasibleconfiguration generator to identify twenty-two electrical contact spring configurations. All twenty-two of the configurations satisfy the design's functional specifications. Two important concepts that improve decision making in early design were chosen. First, is the concept of a paremetric CAD based framework. Second is the concept of generating iso-performing design solutions. A numerical computer-based application is explained that takes advantage of these two ideas. A genetic algorithm topology optimization framework with the ability to converge to iso-performing solutions was integrated with CATIA V5. This application is demonstrated on a Formula SAE frame where it develops a pareto frontier of designs, expands upon one compromise design by producing iso-performing solutions, and automatically produces designs with the same performance after a parametric suspension change.



College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering



Date Submitted


Document Type





optimization, design, conceptual design, embodiment design, iso-performance, framework, CAD, CAE, CATIA, FEA, decision making, parametric, Formula SAE, FSAE, genetic algorithm