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

Chezta Ahmad Muzakky; Arif Wahjudi; B. O.P. Soepangkat

doi:10.1007/978-981-97-7898-0_42

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

Chezta Ahmad Muzakky^*
, Arif Wahjudi
, B. O.P. Soepangkat

^*Corresponding author for this work

Laboratory of Production Engineering

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This research aims to predict and create a model of cutting force as a response to multiple parameters in the face milling process of JIS SKD 11 tool steel material. The face milling parameters are coolant flow rate (Q), cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa). The flow rate (Q) for cryogenic cooling has two levels. Cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa) each have three levels. Using the Taguchi method, it has an L18 mixed-orthogonal array as the experiment design. Using ANOVA, the parameters are qualified to only those significant toward cutting force. The prediction uses Sugeno type-1 and Mamdani type-1 fuzzy inference systems (FIS). The tuning of FIS is conducted using genetic algorithm. The tuned parameters were rules-only and rules-and-output. The membership functions are set to be Gaussian The results are shown in surface plots and root mean square error (RMSE). The result shows errors are less than 5%, which tells the cutting force has been successfully predicted.

Original language	English
Title of host publication	Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023
Editors	Abdel El Kharbachi, Ika Dewi Wijayanti, Putu Suwarta, Ivan Tolj
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	375-384
Number of pages	10
ISBN (Print)	9789819778973
DOIs	https://doi.org/10.1007/978-981-97-7898-0_42
Publication status	Published - 2025
Event	6th International Conference on Mechanical Engineering, ICOME 2023 - Bali, Indonesia Duration: 30 Aug 2023 → 31 Aug 2023

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	6th International Conference on Mechanical Engineering, ICOME 2023
Country/Territory	Indonesia
City	Bali
Period	30/08/23 → 31/08/23

Keywords

Cutting force
FIS
Face milling
Prediction

Access to Document

10.1007/978-981-97-7898-0_42

Cite this

Muzakky, C. A., Wahjudi, A., & Soepangkat, B. O. P. (2025). Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System. In A. El Kharbachi, I. D. Wijayanti, P. Suwarta, & I. Tolj (Eds.), Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023 (pp. 375-384). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-7898-0_42

Muzakky, Chezta Ahmad ; Wahjudi, Arif ; Soepangkat, B. O.P. / Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System. Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023. editor / Abdel El Kharbachi ; Ika Dewi Wijayanti ; Putu Suwarta ; Ivan Tolj. Springer Science and Business Media Deutschland GmbH, 2025. pp. 375-384 (Lecture Notes in Mechanical Engineering).

@inproceedings{0757bcc1d47c41e6b3d98451866d5e8d,

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",

abstract = "This research aims to predict and create a model of cutting force as a response to multiple parameters in the face milling process of JIS SKD 11 tool steel material. The face milling parameters are coolant flow rate (Q), cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa). The flow rate (Q) for cryogenic cooling has two levels. Cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa) each have three levels. Using the Taguchi method, it has an L18 mixed-orthogonal array as the experiment design. Using ANOVA, the parameters are qualified to only those significant toward cutting force. The prediction uses Sugeno type-1 and Mamdani type-1 fuzzy inference systems (FIS). The tuning of FIS is conducted using genetic algorithm. The tuned parameters were rules-only and rules-and-output. The membership functions are set to be Gaussian The results are shown in surface plots and root mean square error (RMSE). The result shows errors are less than 5\%, which tells the cutting force has been successfully predicted.",

keywords = "Cutting force, FIS, Face milling, Prediction",

author = "Muzakky, \{Chezta Ahmad\} and Arif Wahjudi and Soepangkat, \{B. O.P.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 6th International Conference on Mechanical Engineering, ICOME 2023 ; Conference date: 30-08-2023 Through 31-08-2023",

year = "2025",

doi = "10.1007/978-981-97-7898-0\_42",

language = "English",

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series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "375--384",

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booktitle = "Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023",

address = "Germany",

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Muzakky, CA, Wahjudi, A & Soepangkat, BOP 2025, Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System. in A El Kharbachi, ID Wijayanti, P Suwarta & I Tolj (eds), Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 375-384, 6th International Conference on Mechanical Engineering, ICOME 2023, Bali, Indonesia, 30/08/23. https://doi.org/10.1007/978-981-97-7898-0_42

Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System. / Muzakky, Chezta Ahmad; Wahjudi, Arif; Soepangkat, B. O.P.
Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023. ed. / Abdel El Kharbachi; Ika Dewi Wijayanti; Putu Suwarta; Ivan Tolj. Springer Science and Business Media Deutschland GmbH, 2025. p. 375-384 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System

AU - Muzakky, Chezta Ahmad

AU - Wahjudi, Arif

AU - Soepangkat, B. O.P.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - This research aims to predict and create a model of cutting force as a response to multiple parameters in the face milling process of JIS SKD 11 tool steel material. The face milling parameters are coolant flow rate (Q), cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa). The flow rate (Q) for cryogenic cooling has two levels. Cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa) each have three levels. Using the Taguchi method, it has an L18 mixed-orthogonal array as the experiment design. Using ANOVA, the parameters are qualified to only those significant toward cutting force. The prediction uses Sugeno type-1 and Mamdani type-1 fuzzy inference systems (FIS). The tuning of FIS is conducted using genetic algorithm. The tuned parameters were rules-only and rules-and-output. The membership functions are set to be Gaussian The results are shown in surface plots and root mean square error (RMSE). The result shows errors are less than 5%, which tells the cutting force has been successfully predicted.

AB - This research aims to predict and create a model of cutting force as a response to multiple parameters in the face milling process of JIS SKD 11 tool steel material. The face milling parameters are coolant flow rate (Q), cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa). The flow rate (Q) for cryogenic cooling has two levels. Cutting speed (Vc), feeding speed (Vf), and axial depth of cut (Aa) each have three levels. Using the Taguchi method, it has an L18 mixed-orthogonal array as the experiment design. Using ANOVA, the parameters are qualified to only those significant toward cutting force. The prediction uses Sugeno type-1 and Mamdani type-1 fuzzy inference systems (FIS). The tuning of FIS is conducted using genetic algorithm. The tuned parameters were rules-only and rules-and-output. The membership functions are set to be Gaussian The results are shown in surface plots and root mean square error (RMSE). The result shows errors are less than 5%, which tells the cutting force has been successfully predicted.

KW - Cutting force

KW - FIS

KW - Face milling

KW - Prediction

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DO - 10.1007/978-981-97-7898-0_42

M3 - Conference contribution

AN - SCOPUS:105001333738

SN - 9789819778973

T3 - Lecture Notes in Mechanical Engineering

SP - 375

EP - 384

BT - Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023

A2 - El Kharbachi, Abdel

A2 - Wijayanti, Ika Dewi

A2 - Suwarta, Putu

A2 - Tolj, Ivan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 6th International Conference on Mechanical Engineering, ICOME 2023

Y2 - 30 August 2023 through 31 August 2023

ER -

Muzakky CA, Wahjudi A, Soepangkat BOP. Prediction of the Cutting Force in the Face Milling Process on JIS SKD 11 Tool Steel Material with Cryogenic Cooling Using Fuzzy Inference System. In El Kharbachi A, Wijayanti ID, Suwarta P, Tolj I, editors, Smart Innovation in Mechanical Engineering - Select Proceedings of ICOME 2023. Springer Science and Business Media Deutschland GmbH. 2025. p. 375-384. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-981-97-7898-0_42

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

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