Data-Driven Optimization of Wire Arc Directed Energy Deposition Manufacturing Conditions for Improved Bead Shape Prediction

Price, Stephen

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Data-Driven Optimization of Wire Arc Directed Energy Deposition Manufacturing Conditions for Improved Bead Shape Prediction

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Traditional manufacturing of large parts requires a costly process of developing, certifying, storing, and maintaining physical casts and molds. Wire Arc Directed Energy Deposition (Wire Arc DED), which uses an electric arc to 3D print metal layers, offers a potential improvement to this process by replacing physical molds with digital CAD models. Additionally, Wire Arc DED has a higher deposition rate, increased material utilization, and improved energy efficiency over traditional manufacturing techniques. As a new technology, it could benefit from an optimized parameter selection process, enabling quicker and more efficient manufacturing. We propose to use a data-driven machine learning approach to train a model capable of predicting the bead shape (width and height) of a printed layer using Wire Arc DED. Specifically, through a novel design of experiment (DOE) approach, expansive data collection, feature engineering, and extensive evaluation of distinct model architectures, we aim to advance the state-of-the-art performance and generalizability in predicting the bead shape of printed layers using Wire Arc DED.

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