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Author ORCID Identifier

https://orcid.org/0000-0001-2345-6789

Date Available

9-1-2028

Year of Publication

2026

Document Type

Thesis

Degree Name

Master of Science in Mechanical Engineering (MSME)

College

Engineering

Department/School/Program

Mechanical Engineering

Faculty

Fazleena Badurdeen

Jonathan Wenk

Abstract

Metal Additive Manufacturing (AM) offers design flexibility, material efficiency, and reduced energy consumption. However, reliable and rapid part qualification, the process of demonstrating whether an entity fulfills specified requirements, remains a critical barrier to widespread adoption. This study builds upon the Digital Product Passport (DPP) concept to introduce a Digital Manufacturing Passport (DMP) framework tailored to facilitate metal AM qualification. Effective qualification requires traceable linkages between process parameters, microstructure, and final part properties. While these relationships are well understood and standardized in conventional manufacturing technologies, in AM they are often empirical, process- or system-specific, and under-documented. Moreover, metal AM processes are sensitive to variations in thermal history, feedstock condition, and process parameters which can significantly influence microstructure evolution, residual stresses, defect formation, and mechanical properties of the finished component, reducing confidence in repeatability and product quality. The proposed DMP aggregates process parameters, in-situ monitoring data, post-process characterization results, and material properties within a unified digital framework. Machine learning and physics-based models are incorporated to support predictive assessment of part performance. The framework is demonstrated through a prototype DMP for an aluminum flange manufactured using Additive Friction Stir Deposition (AFSD), illustrating structured data capture and qualification-oriented traceability in metal AM.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.438

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Archival

Available for download on Friday, September 01, 2028

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