| Abstract Scope |
Additive Manufacturing (AM) is an inherently complex process, where materials science and manufacturing concerns are intimately linked. The performance of the manufactured part depends on the properties of the material, the design of the part, the reliability of the manufacturing equipment, and details of build parameters used (e.g., orientation and laser power). In such a high-dimensional, complex manufacturing space, determining optimized composition, build parameters, and qualification procedures can be a daunting, time consuming, and costly process. Artificial Intelligence (AI) methods in materials since are particularly useful where the challenge is high-dimensional and data-rich, mechanistic models are unavailable, and projects have tight time and resource constraints. Therefore, AI for AM is a highly promising proposition to guide the gathering of experimental data, provide statistical quantitative process-structure-property, and produce prediction uncertainties for accelerated qualification workflows. This talk will describe Citrine’s experience and detail case studies in utilizing AI for AM applications. |