Residual stresses and distortion are common serious defects in wire-arc additive manufacturing. Commercial thermomechanical models are often used to understand how these defects form. However, no clear mitigation strategy has evolved from previous research. Identification of the hierarchy of variables that influence residual stresses will help to uncover practical means of mitigating this difficulty. Here we use multiple machine learning algorithms and a mechanistic model to rank separately both easy to measure process parameters as well as thermomechanical variables that affect the evolution of stresses. We analyze 243 sets of residual stress data for three alloys using random forest and neural network algorithms to uncover the relative influences of the variables. Both these algorithms predict residual stresses with 97 % accuracy. More important, both algorithms provide the same hierarchical influence of process variables on stresses. The substrate preheat temperature is the most influential variable among the process variables. Among the thermomechanical variables, the following variables are the most influential in decreasing order of importance: the gap between the solidus and preheat temperatures, the product of elastic modulus and the coefficient of thermal expansion, molten pool volume, substrate rigidity, and heat input.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Materials Science(all)
- Engineering (miscellaneous)
- Industrial and Manufacturing Engineering