Single-point incremental forming (SPIF) is highly flexible and successful in
sheet metal forming, particularly in the fabrication of complex components
without requiring molds and dies. Nevertheless, the accurate definition and
definition of the best forming angle in SPIF is still challenging, especially in
limited experimental conditions. This specific research focuses on regression
modeling using machine learning algorithms in finding the best possible forming
angles in SPIF, using a limited number of experimental trials. Some of the most
significant factors in SPIF, namely wall angles, tools, and experimental
conditions, become the inputs in regression models. Based on research, five
individual regression models were made and compared using the leave-one-out
cross-validation (LOOCV) technique for accurate assessment and evaluation
despite the limited experimental conditions. Hyperparameter tuning was conducted
using GridSearchCV, while extra optimizations were implemented in the bigger
models, specifically in the ensembled models. Based on research findings,
boosted models, particularly the use of XGBoost and CatBoost, were shown to
outperform other regression models in modeling the SPIF process due to their
prowess in modeling nonlinear interactions among other process parameters
despite limited experimental conditions, which emphasizes the robustness and
flexibility offered by machine learning techniques in modeling any process,
notably in SPIF process modeling.