Heat input control and deep learning-based indirect measure of process and deposition stability in Wire Arc Additive Manufacturing
Abstract
A process qualification-oriented data-driven framework for Wire Arc Additive Manufacturing (WAAM) integrating qualification data, process monitoring and feedback control, is presented. A proportional control strategy regulating heat input by varying the Contact Tip–to–Workpiece Distance (CTWD) is developed to enhance process stability, ensure consistent layer geometry and maintain the qualified heat-input conditions for process qualification. To assess the control strategy stability, deep learning-based CTWD soft sensing from high-frequency welding signals is combined with an uncertainty-aware process quality index. The framework is validated on Invar 36 alloy, but it supports extension to other alloys and arc welding-based additive processes.
Fields of Study & Subjects
Additive Manufacturing Materials and Processes
High Entropy Alloys Studies
Welding Techniques and Residual Stresses
Publication details
- Journal / source
- CIRP Annals
- Publisher
- Elsevier BV
- Volume
- 75
- Issue
- 1
- Pages
- 203–207
- Publication date
- 1 January 2026
- Language
- EN