SUMMARY

Wire arc additive manufacturing (WAAM) is a relatively recent field of additive manufacturing that achieves layer by layer deposition of a part geometry with commercially available welders and a motion platform. Due to the complexity of the welding process, process monitoring, and control schemes are a focus of the WAAM literature. These studies focus on monitoring to determine, predict, or mitigate defects from the welding process. Despite these efforts, the contact tip and associated wear state is not considered when examining defects. This is likely due to the small size of components produced. Additionally, there is not a standardized method for determining when to replace the contact tip. The contact tip, a key component in gas metal arc welding, positions the wire and serves as the electrical contact surface between the wire electrode and the welding power supply. The contact tip is a consumable component that degrades and it is replaced regularly in manual welding and automated welding. This work seeks to better understand the degradation of the contact tip with respect to WAAM for a 316L wire electrode as well as explore methods of monitoring the contact tip state from in-situ process data. This thesis characterized the wear of the contact tip in terms of material loss and material contamination for a set of tips worn to discrete levels as measured by the amount of wire fed or arc time. Additionally, machine learning models were developed to predict the relative increase in the contact tip bore from arc-based process data.

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