| Abstract Scope |
The prevalence of using machine learning models as research and technology innovation methods in the field of additive manufacturing is growing exponentially, as measured by submissions to the journal Additive Manufacturing. Collectively, researchers are incorporating such modeling approaches into nearly every aspect of AM spanning the process-structure-property design space, ranging from discovery to optimization to qualification problems and challenges for materials, machines, parts, systems, and more. However, the variability in documenting and benchmarking such approaches and their impacts is currently huge, resulting in great inconsistency and lack of comparability or adoptability of much of the work. In this presentation, the fundamental requirements of statistical modeling will be reviewed. Case studies and experiential learning will be used to motivate a proposed standardized framework for documenting machine learning methodologies. A list of priorities for creating and curating benchmark datasets that will enable greater technological advancement and understanding will be suggested. |