Author ORCID Identifier

https://orcid.org/0009-0002-6482-6680

Semester

Summer

Date of Graduation

2023

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Imtiaz Ahmed

Committee Co-Chair

Imtiaz Ahmed

Committee Member

Zichao Liu

Committee Member

Omar Al-Shebeeb

Abstract

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, man, and machine hours. So, each experiment is valuable and it's critical to determine the optimal experiment location to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This thesis presents a multi-objective Bayesian optimization framework to find out the optimum processing conditions for a manufacturing setup. It uses an acquisition function to collect data points sequentially and iteratively update its understanding of the underlying design space utilizing a Gaussian Process-based surrogate model.

In manufacturing processes, the focus is often on obtaining a rough understanding of the design space using minimal experimentation, rather than finding the optimal parameters. This falls under the category of "approximating the underlying function" rather than design optimization. This approach can provide material scientists or manufacturing engineers with a comprehensive view of the entire design space, increasing the likelihood of making discoveries or making robust decisions. However, a precise and reliable prediction model is necessary for a good approximation. To meet this requirement, this thesis proposes an epsilon-greedy sequential prediction framework that is distinct from the optimization framework. The data acquisition strategy has been refined to balance exploration and exploitation, and a threshold has been established to determine when to switch between the two. The performance of this proposed optimization and prediction framework is evaluated using real-life datasets against the traditional design of experiments. The proposed frameworks have generated effective optimization and prediction results using fewer experiments.

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