Machine learning (ML) techniques are increasingly being applied to establish
correlations between input parameters and key process responses in the wire arc
additive manufacturing (WAAM) process. Despite their potential, there remains
limited understanding of how to develop an integrated ML framework that
simultaneously considers both the dataset characteristics and the modeling
approach to ensure accurate and reliable predictions. The present study
addresses this gap by developing an integrated ML framework to predict the
deposition behavior of Inconel 625 in WAAM. To capture nonlinear system
behavior, three ML methods, namely artificial neural network (ANN), support
vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS), were
developed and systematically evaluated for predictive modeling and process
optimization, considering deposited geometry, area, and efficiency as the key
output characteristics. The input parameters, i.e., voltage, wire feed rate,
torch travel speed, and shielding gas flow rate, were identified as critical
factors influencing the deposition process. The datasets were preprocessed to
remove noise and analyzed to extract relevant features that captured the
intrinsic physical behavior of the process. Performances of the ML models were
evaluated using a separate test dataset, and predictions were assessed through
mean absolute percentage deviation (MAPD). Results demonstrated that integrated
ML framework could accurately represent intricate interdependencies among
process parameters on deposition outcomes, providing a robust method of
predictive modeling and parametric process optimization for Inconel 625
deposition by WAAM process. The ANN model demonstrated satisfactory performance
for forward modeling with MAPD values of 12.24, 14.87, and 11.91 for deposition
geometry, deposition area, and deposition efficiency, respectively. For inverse
modeling, the ANN accurately predicted key inputs from outputs, with MAPD values
of 1.39, 18.91, 12.25, and 19.36 for voltage, wire feed rate, torch speed, and
shielding gas flow rate, respectively. Bidirectional predictive modeling keeps
to set operating conditions to achieve desired depositions and process
automations.
