Artificial Intelligence Model Used for Optimizing Abrasive Water Jet Machining Parameters to Minimize Delamination in Carbon Fiber-Reinforced Polymer

Abstract

This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts.

Keywords

• abrasive water jet cutting
• piercing
• composite materials
• CFRP
• artificial intelligence
• machine learning