Blondheim, David J., Jr., authorAnderson, Charles, advisorSimske, Steve, committee memberRadford, Donald, committee memberKirby, Michael, committee member2022-01-072022-01-072021https://hdl.handle.net/10217/234279Die casting is a highly complex manufacturing system used to produce near net shape castings. Although the process has existed for more than hundred years, a systems engineering approach to define the process and the data die casting can generate each cycle has not been completed. Industry and academia have instead focused on a narrow scope of data deemed to be the critical parameters within die castings. With this narrow focus, most of the published research on machine learning within die casting has limited success and applicability in a production foundry. This work will investigate the die casting process from a systems engineering perspective and show meaningful ways of applying machine learning. The die casting process meets the definition of a complex system both in technical definition and in the way that humans interact within the system. From the technical definition, the die casting system is a network structure that is adaptive and can self-organize. Die casting also has nonlinear components that make it dependent on initial conditions. An example of this complexity is seen in the stochastic nature of porosity formation, even when all key parameters are held constant. Die casting is also highly complex due to the human interactions. In manufacturing environments, human's complete visual inspection of castings to label quality results. Poor performance creates misclassification and data space overlap issues that further complicate supervised machine learning algorithms. The best way to control a complex system is to create feedback within that system. For die casting, this feedback system will come from Industry 4.0 connections. A systems engineering approach will define the critical process and then create groups of data in a data framework. This data framework will show the data volume is several orders of magnitude larger than what is currently being used within the industry. With an understanding of the complexity of die cast and a framework of available data, the challenge becomes identifying appropriate applications of machine learning in die casting. The argument is made, and four case studies show, unsupervised machine learning provides value by automatically monitoring the data that can be obtained and identifying anomalies within the die cast manufacturing system. This process control improvement thereby removes the noise from the system, allowing one to gain knowledge about the die casting process. In the end, the die casting industry can better understand and utilize the data it generates with machine learning.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.defect classificationmachine learningunsupervised machine learningdie castingcritical error thresholdporosityText