Faculty Advisor or Committee Member

Diran Apelian, Advisor

Faculty Advisor or Committee Member

Richard D. Sisson, Jr., Department Head

Faculty Advisor or Committee Member

Jamal Yagoobi, Committee Member

Faculty Advisor or Committee Member

Nikolaos Gatsonis, Committee Member

Faculty Advisor or Committee Member

Randy Paffenroth, Committee Member

Faculty Advisor or Committee Member

Joseph Dallarosa, Committee Member

Identifier

etd-042517-104852

Abstract

Manufacturing in modern society has taken on a different role than in previous generations. Today’s manufacturing processes involve many different physical phenomenon working in concert to produce the best possible material properties. It is the role of the materials engineer to evaluate, develop, and optimize applications for the successful commercialization of any potential materials. Laser-assisted cold spray (LACS) is a solid state manufacturing process relying on the impact of supersonic particles onto a laser heated surface to create coatings and near net structures. A process such as this that involves thermodynamics, fluid dynamics, heat transfer, diffusion, localized melting, deformation, and recrystallization is the perfect target for developing a data science framework for enabling rapid application development with the purpose of commercializing such a complex technology in a much shorter timescale than was previously possible. A general framework for such an approach will be discussed, followed by the execution of the framework for LACS. Results from the development of such a materials engineering model will be discussed as they relate to the methods used, the effectiveness of the final fitted model, and the application of such a model to solving modern materials engineering challenges.

Publisher

Worcester Polytechnic Institute

Degree Name

PhD

Department

Materials Science & Engineering

Project Type

PhD report

Date Accepted

2017-04-25

Accessibility

Unrestricted

Subjects

cold spray, laser, Machine Learning, manufacturing, powder metallurgy

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