Exploring Data-Driven Methods to Enhance Usability of Design Optimization

Sammanfattning: Developing high-performing products at a low cost while keeping development time down is increasingly important in today’s competitive market. The current state presents a need for efficient product development processes. One of the challenges is knowledge often being limited in early stages where the cost of making changes is still relatively low. As the process progresses more knowledge is gained to better support decisions; however the cost of making changes increases, limiting the design freedom. To increase knowledge while retaining design freedom, several computer-based tools are available to both generate and evaluate designs in order to make iterations faster and more accurate.Design Optimization (DO) can be utilized to explore the design space and find optimal designs. A Computer-Aided Design (CAD) model is often required as input to analysis tools evaluating the designs. By utilizing Design Automation (DA) several tasks involved in creation and modification of CAD models can be automated. For this reason, DA is sometimes considered an enabler for DO although its use is far wider, covering several aspects of the design process mainly focusing on automating repetitive and routine tasks.Machine Learning and other data-driven methods are becoming increasingly viable in the context of DO and DA. This thesis explores the use of data-driven methods to enhance the usability of DO in different ways such as a faster process, new use-cases, or a more integrated and automated process.  Literature in the area is reviewed, identifying applications, trends and challenges. Furthermore, two support tools are developed, incorporating data-driven methods tied to an industrial case. The applications focus on parameterizing geometry and predicting design performance respectively. Potential benefits, limitations, and challenges are discussed based on the literature review and insights from the two support tools. The focus of the thesis is mainly on how data-driven methods can facilitate automation and integration in the design process, specifically for complex products requiring significant engineering efforts.