Predicting Geometric Accuracy in Turning: Comparative Modelling Using ANFIS and Linear Regression

Authors

  • Viktor Gergely Ráczi Óbuda University, Bánki Donát Faculty of Mechanical and Safety Engineering, Budapest, Hungary
  • Balázs Mikó Óbuda University, Bánki Donát Faculty of Mechanical and Safety Engineering, Budapest
  • Edit Laufer Óbuda University, Bánki Donát Faculty of Mechanical and Safety Engineering, Budapest, Hungary

Keywords:

ANFIS, turning, geometric accuracy, Taguchi method, predictive modelling

Abstract

Reliable prediction of geometric deviations in high-precision machining is crucial for optimizing parameters and reducing costs. This research focuses on predictive modeling of deviations during finish turning of blind holes. Using Taguchi L9 design data, the performance of ANFIS and linear regression was compared based on cutting speed, feed rate, and depth . Results confirm ANFIS superiority, showing lower prediction errors and higher coefficients of determination (R2) for both diameter and roundness errors. The hybrid model effectively handles nonlinear relationships, supporting manufacturing optimization.

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Published

2026-07-06

Issue

Vol. 8 No. 1 (2026): Bánki Reports Spring (2026)

Section

SzaFARI különszám

License

Copyright (c) 2026 Viktor Gergely Ráczi, Balázs Mikó, Edit Laufer

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.