Title: Dry CNC Milling of Glass Fiber Reinforced Polymer: Statistical Modelling and Multi-Response Optimization of Productivity and Surface Quality
Authors: Chuku, I. E., Bani, S. L., Jinyemiema, T. K., Nwosu, H. U.
Volume: 10
Issue: 4
Pages: 147-156
Publication Date: 2026/04/28
Abstract:
Glass Fiber Reinforced Polymer (GFRP) is widely used in aerospace, automotive, and oil-and-gas industries due to its high strength-to-weight ratio and corrosion resistance. Though, its heterogeneous and abrasive nature poses significant challenges during machining, including rapid tool wear and poor surface quality. This study through an experiment investigates the dry CNC milling of GFRP with the aim of improving productivity and surface integrity through statistical modeling and multi-response optimization. A full factorial experimental design comprising spindle speed, feed rate, and depth of cut at three levels each was adopted, resulting in 27 experimental runs with three replications. Material removal rate (MRR) and surface roughness (Ra) were selected as performance indicators. Analysis of variance (ANOVA) and multiple linear regression were used to identify significant factors and develop predictive models. Results reveal that all three machining parameters and their interactions significantly influence MRR and surface roughness (p < 0.05). The developed regression models demonstrated excellent predictive accuracy with coefficients of determination exceeding 99%. An optimal cutting condition of 1700 rpm spindle speed, 60 mm/min feed rate, and 0.60 mm depth of cut yielded a high MRR of 196.79 mm³/min and a low surface roughness of 1.025 µm. The findings offer reliable guidelines for efficient dry milling of GFRP and contribute to sustainable and cost-effective composite machining practices.