Hybrid Manufacturing

Hybrid Manufacturing Combining Wire-Arc Directed Energy Deposition and Machining

Wire-Arc Directed Energy Deposition (DED) has emerged as a transformative force in metal additive manufacturing, presenting high deposition rates and manufacturing efficiency. Despite its advantages, challenges such as geometric inaccuracies, microstructural inconsistencies, and residual stresses have impeded widespread adoption. This research area seeks to overcome these hurdles through innovative methodologies. Our focus spans recent studies addressing bead geometry prediction, interlayer machining interventions, and the influence of high strain-rate machining on microstructural dynamics within the realm of Wire-Arc DED. 

Initiating our exploration, bead geometry prediction becomes a key aspect, particularly in corner structures, employing the integration of Machine Learning (ML). Traditional Wire-Arc DED studies often overlook the intricacies of multi-layer structures and corner features, concentrating on single-layer straight bead geometry. Leveraging ML techniques, our Multilayer Perceptron (MLP) model, trained on various mild steel welding beads, successfully predicts excess material at corners. This leads to the creation of an inverse algorithm, suggesting welding parameters for smooth bead shapes, thus advancing the accuracy of WAAM.  

Simultaneously, our research delves into interlayer machining interventions, a hybrid approach that significantly improves geometry, surface quality, microstructure, and mechanical properties of metal structures produced in Wire-Arc DED. Highlighting the importance of machining interventions, this study underscores its potential to enhance the overall quality and performance of additively manufactured metal components.  

Further extending our research, we explore microstructural dynamics in thin-walled structures through Hybrid Wire-Arc DED, incorporating high strain-rate machining interventions. This facet provides crucial insights into the intricate interactions among deposition, solidification, thermal cycling, and machining strategies, showcasing machining as a promising strategy for achieving consistent microstructural properties and precise geometry in Wire-Arc DED.  

In conclusion, these collective efforts aim to propel the capabilities of WAAM, contributing to ongoing endeavors to enhance its accuracy, efficiency, and applicability in modern manufacturing. 

Improvement of variation in transverse cross section width (left) and line roughness measurements (right) by incorporating Machining with Wire-Arc DED 
Refinement of grain size (top) and improvement of microhardness values (bottom) by incorporating Machining with Wire-Arc DED 
Prediction of bead height and bead width with varying material deposition rate and robot traverse speed using Machine Learning algorithm 

Publications

Asif Rashid, Ji Ho Jeon, Denis Boing, Shreyes Melkote “Effect of Machining-Induced Deformation and Grain Refinement on Microstructure Evolution in Hybrid Wire Arc Directed Energy Deposition”. In review, CIRP Annals Manufacturing Technology, submitted January 2024. 

Asif Rashid, Akshar Kota, Denis Boing, Shreyes Melkote “Effect of Interlayer Machining Interventions on the Geometric and Mechanical Properties of Wire Arc Directed Energy Deposition Parts”. In review, Journal of Manufacturing Science and Engineering, submitted January 2024. 

Akshar Kota, Shohom Bose-Bandyopadhyay, Asif Rashid, Shreyes Melkote “Influence of Milling Interventions on the Geometry of Wall-Shaped Structures in Hybrid Wire-Arc Directed Energy Deposition”. In review, Proceedings of the 52nd North American Manufacturing Research Conference (NAMRC), submitted November 2023. 

Asif Rashid, Akshar Kota, Shreyes Melkote “Evolution of Microstructure and Mechanical Property Enhancement in Wire-Arc Directed Energy Deposition with Interlayer Machining”. In review, Proceedings of the 52nd North American Manufacturing Research Conference (NAMRC), submitted November 2023. 

Marwin Gihr, Asif Rashid, Shreyes Melkote “Bead geometry prediction and optimization for corner structures in Wire Arc Additive Manufacturing using Machine Learning”. In review, Additive Manufacturing, submitted April 2023. 

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