INVESTIGATING PREDICTIVE METAMODELING FOR ADDITIVE MANUFACTURING

Additive manufacturing (AM) is a new and disruptive technology that comes with a set of unique challenges. One of them is the lack of understanding of the complex relationships between the numerous physical phenomena occurring in these processes. Metamodels can be used to provide a simplified mathematical framework for capturing the behavior of such complex systems. At the same time, they offer a reusable and composable paradigm to study, analyze, diagnose, forecast, and design AM parts and process plans. Training a metamodel requires a large number of experiments and even more so in AM due to the various process parameters involved. To address this challenge, this work analyzes and prescribes metamodeling techniques to select optimal sample points, construct and update metamodels, and test them for specific and isolated physical phenomena. A simplified case study of two different laser welding process experiments is presented to illustrate the potential use of these concepts. We conclude with a discussion on potential future directions, such as data and model integration while also accounting for sources of uncertainty.