PROCESS PARAMETER OPTIMIZATION IN THE MACHINING OF FIBER-REINFORCED POLYMERS: A REVIEW OF METHODOLOGIES FROM TAGUCHI TO NEURAL NETWORKS

Authors

DOI:

https://doi.org/10.20998/2078-7405.2026.104.12

Keywords:

composite machining, process parameter optimization, artificial intelligence

Abstract

The widespread adoption of Fiber Reinforced Polymers (FRPs), such as CFRP and GFRP, in weight-critical industries has necessitated highly precise secondary machining operations. However, the heterogeneous and anisotropic nature of these composites makes them susceptible to severe machining-induced defects, including delamination, matrix smearing, and rapid tool wear. To mitigate these issues, selecting and controlling optimal machining parameters (cutting speed, feed rate, and depth of cut) is critical. This paper comprehensively reviews the evolution of process optimization strategies in composite machining. It begins by examining established traditional statistical methods, including the Taguchi Method, Analysis of Variance (ANOVA), and Response Surface Methodology (RSM), which offer robust, data-efficient frameworks for linear process control. Subsequently, the paper explores the paradigm shift toward Artificial Intelligence (AI) and machine learning techniques, specifically Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Fuzzy Logic systems. These data-driven approaches successfully overcome the limitations of traditional models by capturing complex, non-linear thermo-mechanical dynamics and resolving multi-objective conflicts. Ultimately, this review highlights that the future of zero-defect composite manufacturing lies in integrating these methodologies into intelligent hybrid models that bridge the gap between experimental efficiency and advanced predictive accuracy.

Author Biographies

Valizada Mikayil, University of Miskolc, Hungary

PhD. Student (Mechanical Engineering), University of Miskolc, Department of Production Engineering, Miskolc - Egyetemváros, Hungary

Sztankovics István, University of Miskolc, Hungary

Associate Professor, Deputy Director of the Institute of Manufacturing Science, University of Miskolc, Department of Production Engineering, Miskolc - Egyetemváros, Hungary

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Published

2026-05-15

Issue

No. 104 (2026): Cutting and Tools in Technological Systems

Section

Mechanical processing of materials, the theory of cutting materials, mathematical and computer simulation of machining p

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