Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling

Machine Learning Models in Drilling of Different Types of Glass-Fiber-Reinforced Polymer Composites

The aim of the research presented in this paper was to simulate the relationship between selected technological drilling parameters (cutting speed, vc, and feed per tooth, fz) and cutting forces and the delamination in machining of a new glass-fiber-reinforced polymer (GFRP) composite. Four different types of new materials were manufactured with the use of a specially designed pressing device and differed in the fiber type (plain and twill woven materials) and weight fraction (wf) ratio, but they had the same number of layers and the same stacking sequence. A vertical machining center Avia VMC800HS was used for drilling holes with a two-edge carbide diamond coated drill. Measurements of the cutting force Fz in the drilling process conducted with variable technological parameters were carried out on a special test stand, 9257B, from Kistler. The new ink penetration method, involving covering the drilled hole surface with a colored liquid that spreads over the inner surface of the hole showing damage, was used to determine the delamination area. The cause-and-effect relationship between the drilling parameters was simulated with the use of five machine learning (ML) regression models (Linear Regression; Decision Tree Regressor; Decision Tree Regressor with Ada Boost; XGBRF Regressor; Gradient Boosting Regressor). Gradient Boosting Regressor results showed that the feed per tooth had the greatest impact on delamination-the higher the feed was, the greater the delamination became. Push-out delamination factors had higher values for materials that were made of plain woven fibers. The lowest amplitude of the cutting force component Fz was obtained for the lowest tested feed per tooth of 0.04 mm for all tested materials, with the lowest values obtained for the materials with twill fibers.

Analysis of Vibration, Deflection Angle and Surface Roughness in Water-Jet Cutting of AZ91D Magnesium Alloy and Simulation of Selected Surface Roughness Parameters Using ANN

The use of magnesium alloys in various industries and commerce is increasing due to their properties such as high strength and casting properties, high vibration damping capability, good shielding of electromagnetic radiation and high machinability. Conventional machining methods can, however, pose a risk of ignition. AWJM is a safe alternative to conventional machining, but the deflection and vibration of the water jet can affect surface quality. Therefore, the aim of this study was to investigate the effects of selected AWJM parameters on the surface quality and vibration of machined magnesium alloys. Jet deflection angle, surface roughness parameters and vibration during AWJM were investigated. The findings showed that higher skewness occurred at a lower abrasive flow rate, while higher average values of the Sku roughness parameter were obtained at m_a = 8 g/s in the range of 60-140 mm/min. It was also observed that higher vibration values occurred at m_a = 8 g/s. The input parameters for creating an artificial neural network (ANN) model used in this study were the cutting speed v_f and the mass flow rate m_a. The results of this study provided valuable insights into ways of ensuring a safe and efficient machining environment for magnesium alloys. The use of ANN modeling for predicting the vibration and surface roughness of AZ91D magnesium alloy after water-jet cutting could be an effective tool for optimizing AWJM parameters.