Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis

By Jun Xiong, Guangjun Zhang, J. Hu, Lin Wu. Journal of Intelligent Manufacturing, 2012

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Wire-feed additive manufacturing of metal components: technologies, developments and future interests (2015, background): “networks or combinations of these two techniques [90].”
Building blocks for a digital twin of additive manufacturing (2017, methodology): “Previous research on predicting single layer cladding geometry includes the use of parabolic and sinusoidal curves fitting [20], numerical algorithms [21], in-situ sensing [22], neural networks [20,23] and ANOVA techniques [24].”
A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM) (2015, methodology): “Much research has been directed towards developing a correlation between welding parameters and weld bead geometry by using regression analysis [5], artificial neural networks, or combinations of these two techniques [21].”
Wire and arc additive manufacturing: Opportunities and challenges to control the quality and accuracy of manufactured parts (2021, background): “, inter-layer temperature and cooling rate) and therefore difficulties in controlling dimensional accuracy, particularly for large parts [11,104,110].”
Numerical analysis of heat transfer and fluid flow in multilayer deposition of PAW-based wire and arc additive manufacturing (2018, methodology): “Bead modelling work using empirical models or artificial neural networks can establish the relationship between deposition parameters and bead shape [8-10].”