Abstract:
Numerical and Artificial Neural Network (ANN) approaches are now frequently
employed for modeling and optimizing the performance of industrial systems. Optimal
machining parameters are of major relevance in production contexts, as machining
operations efficiency is critical to market competitiveness. The optimal machining
parameters (i.e., spindle speed, drill diameter, and feed rate) for drilling operations will be
researched in this project in order to minimize the delamination factor. Regression
modeling and Response Surface Methodology (RSM) was previously used to explore the
effects of specified parameters on process variables (i.e., delamination factors). The data
acquired during the machining operation is utilized to create machine learning (ML)-based
surrogate models that test, assess, and optimize different input machining parameters. To
predict different output reactions of bio-composites drilling, several ML approaches such
as polynomial regression (PR), random forest (RF) regression, gradient boosted (GB) trees,
and adaptive boosting (AB) based regression are utilized. The ML results will then be
compared to the experimental and RSM results.
Keywords: Artificial Neural Networks; Bio-Composites; Drilling; Machine Learning;
Optimization;
