Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing. (June 2016)

• Record Type: Journal Article
• Authors: Ding, Donghong; Pan, Zengxi; Cuiuri, Dominic; Li, Huijun; van Duin, Stephen; Larkin, Nathan
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 39(2016)
• Page Start: 32
• Page End: 42
• Publication Date: 2016-06
• Subjects: Bead model -- Arc welding -- Additive manufacturing -- Medial axis transformation (MAT) -- Path planning; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2015.12.004
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Wire and arc additive manufacturing (WAAM) is a promising alternative to traditional subtractive methods for fabricating large aerospace metal components that feature high buy-to-fly ratios. This study focuses on the development of an automated manufacturing system in order to free the operator from intervening in the analysis of the CAD model, planning the deposition path, and then manually setting the welding process parameters. Firstly, the relationship between single bead geometry and welding process parameters is established through an artificial neural network (ANN) model. Then, the adaptive medial axis transformation (MAT) algorithm for void-free deposition with high geometrical accuracy is introduced. The adaptive MAT path is implemented by using the single bead ANN model together with a previously developed multi-bead overlapping model. Finally, the adaptive MAT path planning strategy and the established bead models are tested through experimental deposition of two metal components. The results show that the developed bead model and adaptive MAT-based path are capable of producing depositions with high quality (void-free) and geometrical accuracy through automated selection of process variables for the WAAM process. Highlights: This study developed an automated wire arc additive manufacturing system. An artificial neural network (ANN) bead model is established. An innovative path planning strategy is introduced. Practical implementation of an automated deposition system is reported. Experiments are conducted through automated selection of process variables.