Active Part Filtering for Additive Manufacturing Candidate Identification

Cornerstone Research Group Inc. (CRG) proposes to develop an additive manufacturing candidate identification tool which automatically processes technical data packages, applies part filtering/assessment methods to score the part against a list of additive manufacturing criteria, and categorize parts according to its suitability for additive manufacturing. This tool will feature a continuously tuned machine learning prediction component to produce assessment metrics directly from technical data package information. As the tool is used, prediction capability will grow as expert judgements are incorporated until fully automated assessments are possible with suitable accuracy. CRGÆs research institution partner, Penn State University, will formulate the core filtering and assessment methodology for CRGÆs implementation and automation via machine learning application. During Phase I, CRG and Penn State will prove feasibility of the approach by characterizing performance of the active learning for part assessment using a limited number of relevant part geometrical and manufacturing information.