Masters Theses

Date of Award

8-2025

Degree Type

Thesis

Degree Name

Master of Science

Major

Mechanical Engineering

Major Professor

Bradley H. Jared

Committee Members

Subhadeep Chakraborty, Tony L. Schmitz

Abstract

With recent expansion and popularity of the Additive Manufacturing (AM) industry, Wire-Arc Additive Manufacturing (WAAM) processes utilize tried and tested welding equipment in conjunction with robotics to create metal parts for a variety of customers and applications. As with many other AM processes, process monitoring and product quality are among the areas with the most sought-after improvements as the manufacturing technology is being deployed. In particular, for WAAM, this can be a difficult task, as the process area, commonly referred to as a weld cell, can be uninhabitable for sensitive and expensive monitoring technology due to the high heat, spatter, and debris that occur during welding. Because of this, it is difficult to detect defects during the manufacturing of parts. Due to the high input energy process of welding, many heating and cooling cycles between layers of material can result in heat-induced stress within the part. This can cause morphological warping that results in a nonconforming part, which costs manufacturing partners time and money. The ability to detect these defects within a weld cell with equipment that is economically suitable for the hazardous environment can speed up manufacturing and result in fewer defect parts. The research described in this work seeks to develop and demonstrate a cost-effective solution to obtain morphological data from within a weld cell using a commercially available stereo depth camera integrated with code to capture and process the data. One key feature of the process described in this work is that it leverages the robotics already used for the WAAM process to produce higher quality results faster than other common methods.

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