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2025 | OriginalPaper | Chapter

Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System

Authors : Chezta Ahmad Muzakky, Arif Wahjudi, B. O. P. Soepangkat

Published in: Smart Innovation in Mechanical Engineering

Publisher: Springer Nature Singapore

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Abstract

This chapter presents a groundbreaking approach to predicting cutting forces in the face milling process of JIS SKD 11 tool steel material using a fuzzy inference system (FIS) and cryogenic cooling. The study addresses the critical issue of heat generation during machining, which can degrade dimensional accuracy and tool life. By employing cryogenic cooling with liquid nitrogen, the research mitigates environmental and health concerns associated with conventional cutting fluids. The experimental setup involves a Hartfort S-Plus 10 CNC machine and a Kistler 9272 dynamometer, with machining parameters including coolant flow rate, cutting speed, feeding speed, and axial depth of cut. The results are analyzed using ANOVA to determine significant parameters, leading to the development of Sugeno and Mamdani type-1 FIS models. The genetic algorithm is employed to optimize FIS parameters, with the Sugeno type-1 FIS demonstrating superior performance. The chapter concludes with a detailed comparison of predicted and observed cutting forces, showcasing the efficacy of the proposed method. The findings offer valuable insights into enhancing machining processes, reducing environmental impact, and improving tool life and dimensional accuracy.

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Metadata
Title
Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System
Authors
Chezta Ahmad Muzakky
Arif Wahjudi
B. O. P. Soepangkat
Copyright Year
2025
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-7898-0_42

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