Work

Practical Multivariate Equivalency Testing for Additively Manufactured Parts: Comparing Independent and Dependent Cases

Year: 2026

Type: article

Abstract: Additive manufacturing (AM) requalification and change-control workflows often require evidence that a candidate machine, parameter set, scanner subsystem, facility, or measurement workflow remains comparable to a stable reference process after a change, but fabrication and testing costs limit exhaustive multifeature studies. The aim of this study was to address this engineering design problem by developing a practical multifeature equivalency screening framework for AM settings in which prior engineering evidence already suggests that the candidate process should be comparable to the reference process. Building on prior work focused on the univariate problem, the proposed framework uses reference-defined percentile bins, feature-wise distributional tests, and family-wise error-rate control to screen for evidence of non-equivalency across multiple measured attributes. A direct joint-binning approach was first shown to become sample-intensive as dimensionality increases, after which an independent feature-wise method and an exploratory dependent bivariate extension were developed. Simulation-based power analyses quantified the trade-offs among power, detectable effect size, distributional resolution, feature count, and the combined costs of fabrication and measurement. In a laser-based powder bed fusion validation study with 40 observations per process and three corner-deviation features, the expected-equivalent AconityMIDI+ candidate satisfied all feature-wise equivalency criteria (V˜=0.207–0.214 CI+=0.276). These results support multivariate equivalency as a requalification screening tool for AM process comparability and change control, while confirming that it should not be interpreted as proof of physical-process identity or as a replacement for first-time formal qualification. Core procedures are implemented in the open-source R package MultivariateEquivalency.

Source: Journal of Manufacturing and Materials Processing

DOI: 10.3390/jmmp10070229

Authors: Colin M. Lynch, Rene Villalobos, Brenda Leticia Valadez Mesta, Cesar Gomez Guillen, Jorge Mireles, Ryan B. Wicker

Institutions: Arizona State University, The University of Texas at El Paso

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: University of Texas at El Paso, Air Force Research Laboratory