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.

Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes

“…So it is certainly applicable to detecting surface quality changes on a flat surface as well. We take the data from the flat surface polishing experiment published in Jin et al (2020) and test the proposed method on it. For the details on that polishing experiment, please refer to Jin et al (2020).…”

“…Recent works (Wang et al, 2014;Liu et al, 2017;Bukkapatnam et al, 2018;Jin et al, 2020) showed people's awareness of a number of critical limitations of using average metric such as Sa or median Sa for the finishing process of precise manufacturing. Jin et al (2020) provided specific examples where a set of similar media Sa values correspond to surfaces with rather different roughness features. Jin et al (2020) further proposed a new decision making criterion for polishing by modeling the surface roughness at each stage through a Gaussian process (GP).…”

“…Jin et al (2020) provided specific examples where a set of similar media Sa values correspond to surfaces with rather different roughness features. Jin et al (2020) further proposed a new decision making criterion for polishing by modeling the surface roughness at each stage through a Gaussian process (GP). The surface of the polished object is treated like a landscape, so that its analogy to spatial statistics is invoked, explaining why the GP model is used.…”

“…The surface of the polished object is treated like a landscape, so that its analogy to spatial statistics is invoked, explaining why the GP model is used. Jin et al (2020) demonstrated that the scale parameter in the resulting GP model is sensitive to changes in the surface roughness and consequently devised a decision procedure based on that. While Jin et al (2020) took an important step forward in introducing sophisticated statistical modeling to decision making in polishing, there are still two limitations in their method.…”

“…The tool also steadily degrades during the process due to loading, glazing and other issues (Rao et al, 2015). The decisions of when to change the polishing tool or to stop polishing altogether are crucial, demanding sound and consistent guidance (Jin et al, 2020;Bukkapatnam et al, 2018).…”