Modelling, prediction, and optimization of properties of low-carbon steel produced by wire arc additive manufacturing using machine learning
Modelling, prediction, optimization, wire arc additive manufacturing, machine learning - Jun Cheng
During the wire arc additive manufacturing (WAAM) process, the high heat accumulation and the complex thermal evolution significantly impact the properties of the steel deposit. The high experimental costs and extensive simulation computation times do not allow us to efficiently figure out the complex interaction of process parameters and mechanical properties. Thus, our target is to develop a prediction/optimization hybrid model for the process parameters-properties relationship to determine the optimal WAAM process parameter settings.
- Promotors: Wim De Waele & Dibakor Boruah
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