A Gaussian Process Model-Guided Surface Polishing Process in Additive Manufacturing
Abstract
Polishing of additively manufactured products is a multi-stage process, and a different combination of polishing pad and process parameters is employed at each stage. Pad change decisions and endpoint determination currently rely on practitioners’ experience and subjective visual inspection of surface quality. An automated and objective decision process is more desired for delivering consistency and reducing variability. Toward that objective, a model-guided decision-making scheme is developed in this article for the polishing process of a titanium alloy workpiece. The model used is a series of Gaussian process models, each established for a polishing stage at which surface data are gathered. The series of Gaussian process models appear capable of capturing surface changes and variation over the polishing process, resulting in a decision protocol informed by the correlation characteristics over the sample surface. It is found that low correlations reveal the existence of extreme roughness that may be deemed surface defects. Making judicious use of the change pattern in surface correlation provides insights enabling timely actions. Physical polishing of titanium alloy samples and a simulation of this process are used together to demonstrate the merit of the proposed method.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 20, 2019
- Source ID
- 10.1115/1.4045334
Entities
People
- Andrew Gaynor
- Ashif Iquebal
- Satish Bukkapatnam
- Shilan Jin
- Yu Ding
Organizations
- National Science Foundation of Sri Lanka
- Texas A&M University
- United States Army Research Laboratory