Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm

M. Khoirul Effendi; Bobby O.P. Soepangkat; Dinny Harnany; Rachmadi Norcahyo

doi:10.1007/978-981-19-0867-5_39

Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm

M. Khoirul Effendi^*
, Bobby O.P. Soepangkat
, Dinny Harnany
, Rachmadi Norcahyo

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember
Gadjah Mada University

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

Abstract

An integrated approach has been applied to predict and optimize multi-performance characteristics, namely, cutting force (CF) and surface roughness (SR), in the end-milling process of glass fiber-reinforced polymer (GFRP) composites. The experiments were performed by varying spindle speed, feeding speed, and depth of cut. The quality characteristics of cutting force and surface roughness were the smaller, the better. Full factorial design 3 × 3 × 3 was used as the design of experiments. Backpropagation neural network (BPNN) was used to model the end-milling experiment and also to determine the objective function. This objective function will be modified into a fitness function optimized by using a differential evolution algorithm (DEA) to find the combination of drilling parameters’ levels that yield minimum cutting force and surface roughness simultaneously. As a result, the minimum cutting force can reduce the energy consumption, and the end-milling process can be performed with higher energy efficiency. Based on BPNN-DEA, the depth of cut of 2 mm, the spindle speed of 4797.5 rpm, and the feeding speed of 579.7 mm/min can simultaneously minimize the cutting force and surface roughness in the end milling of GFRP.

Original language	English
Title of host publication	Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021
Editors	Ivan Tolj, M.V. Reddy, Achmad Syaifudin
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	325-333
Number of pages	9
ISBN (Print)	9789811908668
DOIs	https://doi.org/10.1007/978-981-19-0867-5_39
Publication status	Published - 2023
Event	5th International Conference on Mechanical Engineering, ICOME 2021 - Virtual, Online Duration: 25 Aug 2021 → 26 Aug 2021

Publication series

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

Conference

Conference	5th International Conference on Mechanical Engineering, ICOME 2021
City	Virtual, Online
Period	25/08/21 → 26/08/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Backpropagation neural network
Differential evolution algorithm
End milling
Energy consumption
Glass fiber-reinforced polymer

Access to Document

10.1007/978-981-19-0867-5_39

Cite this

Effendi, M. K., Soepangkat, B. O. P., Harnany, D., & Norcahyo, R. (2023). Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm. In I. Tolj, M. V. Reddy, & A. Syaifudin (Eds.), Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021 (pp. 325-333). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-0867-5_39

Effendi, M. Khoirul ; Soepangkat, Bobby O.P. ; Harnany, Dinny et al. / Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm. Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021. editor / Ivan Tolj ; M.V. Reddy ; Achmad Syaifudin. Springer Science and Business Media Deutschland GmbH, 2023. pp. 325-333 (Lecture Notes in Mechanical Engineering).

@inproceedings{9dd48e1862cd4030a7ad7eee7674ebc2,

title = "Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm",

abstract = "An integrated approach has been applied to predict and optimize multi-performance characteristics, namely, cutting force (CF) and surface roughness (SR), in the end-milling process of glass fiber-reinforced polymer (GFRP) composites. The experiments were performed by varying spindle speed, feeding speed, and depth of cut. The quality characteristics of cutting force and surface roughness were the smaller, the better. Full factorial design 3 × 3 × 3 was used as the design of experiments. Backpropagation neural network (BPNN) was used to model the end-milling experiment and also to determine the objective function. This objective function will be modified into a fitness function optimized by using a differential evolution algorithm (DEA) to find the combination of drilling parameters{\textquoteright} levels that yield minimum cutting force and surface roughness simultaneously. As a result, the minimum cutting force can reduce the energy consumption, and the end-milling process can be performed with higher energy efficiency. Based on BPNN-DEA, the depth of cut of 2 mm, the spindle speed of 4797.5 rpm, and the feeding speed of 579.7 mm/min can simultaneously minimize the cutting force and surface roughness in the end milling of GFRP.",

keywords = "Backpropagation neural network, Differential evolution algorithm, End milling, Energy consumption, Glass fiber-reinforced polymer",

author = "Effendi, \{M. Khoirul\} and Soepangkat, \{Bobby O.P.\} and Dinny Harnany and Rachmadi Norcahyo",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th International Conference on Mechanical Engineering, ICOME 2021 ; Conference date: 25-08-2021 Through 26-08-2021",

year = "2023",

doi = "10.1007/978-981-19-0867-5\_39",

language = "English",

isbn = "9789811908668",

series = "Lecture Notes in Mechanical Engineering",

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

pages = "325--333",

editor = "Ivan Tolj and M.V. Reddy and Achmad Syaifudin",

booktitle = "Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021",

address = "Germany",

}

Effendi, MK, Soepangkat, BOP, Harnany, D & Norcahyo, R 2023, Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm. in I Tolj, MV Reddy & A Syaifudin (eds), Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 325-333, 5th International Conference on Mechanical Engineering, ICOME 2021, Virtual, Online, 25/08/21. https://doi.org/10.1007/978-981-19-0867-5_39

Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm. / Effendi, M. Khoirul; Soepangkat, Bobby O.P.; Harnany, Dinny et al.
Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021. ed. / Ivan Tolj; M.V. Reddy; Achmad Syaifudin. Springer Science and Business Media Deutschland GmbH, 2023. p. 325-333 (Lecture Notes in Mechanical Engineering).

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

TY - GEN

T1 - Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm

AU - Effendi, M. Khoirul

AU - Soepangkat, Bobby O.P.

AU - Harnany, Dinny

AU - Norcahyo, Rachmadi

PY - 2023

Y1 - 2023

N2 - An integrated approach has been applied to predict and optimize multi-performance characteristics, namely, cutting force (CF) and surface roughness (SR), in the end-milling process of glass fiber-reinforced polymer (GFRP) composites. The experiments were performed by varying spindle speed, feeding speed, and depth of cut. The quality characteristics of cutting force and surface roughness were the smaller, the better. Full factorial design 3 × 3 × 3 was used as the design of experiments. Backpropagation neural network (BPNN) was used to model the end-milling experiment and also to determine the objective function. This objective function will be modified into a fitness function optimized by using a differential evolution algorithm (DEA) to find the combination of drilling parameters’ levels that yield minimum cutting force and surface roughness simultaneously. As a result, the minimum cutting force can reduce the energy consumption, and the end-milling process can be performed with higher energy efficiency. Based on BPNN-DEA, the depth of cut of 2 mm, the spindle speed of 4797.5 rpm, and the feeding speed of 579.7 mm/min can simultaneously minimize the cutting force and surface roughness in the end milling of GFRP.

AB - An integrated approach has been applied to predict and optimize multi-performance characteristics, namely, cutting force (CF) and surface roughness (SR), in the end-milling process of glass fiber-reinforced polymer (GFRP) composites. The experiments were performed by varying spindle speed, feeding speed, and depth of cut. The quality characteristics of cutting force and surface roughness were the smaller, the better. Full factorial design 3 × 3 × 3 was used as the design of experiments. Backpropagation neural network (BPNN) was used to model the end-milling experiment and also to determine the objective function. This objective function will be modified into a fitness function optimized by using a differential evolution algorithm (DEA) to find the combination of drilling parameters’ levels that yield minimum cutting force and surface roughness simultaneously. As a result, the minimum cutting force can reduce the energy consumption, and the end-milling process can be performed with higher energy efficiency. Based on BPNN-DEA, the depth of cut of 2 mm, the spindle speed of 4797.5 rpm, and the feeding speed of 579.7 mm/min can simultaneously minimize the cutting force and surface roughness in the end milling of GFRP.

KW - Backpropagation neural network

KW - Differential evolution algorithm

KW - End milling

KW - Energy consumption

KW - Glass fiber-reinforced polymer

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DO - 10.1007/978-981-19-0867-5_39

M3 - Conference contribution

AN - SCOPUS:85137092136

SN - 9789811908668

T3 - Lecture Notes in Mechanical Engineering

SP - 325

EP - 333

BT - Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021

A2 - Tolj, Ivan

A2 - Reddy, M.V.

A2 - Syaifudin, Achmad

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Mechanical Engineering, ICOME 2021

Y2 - 25 August 2021 through 26 August 2021

ER -

Effendi MK, Soepangkat BOP, Harnany D, Norcahyo R. Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm. In Tolj I, Reddy MV, Syaifudin A, editors, Recent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021. Springer Science and Business Media Deutschland GmbH. 2023. p. 325-333. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-981-19-0867-5_39

Multi-performance Optimization in End Milling of GFRP Composites Using Backpropagation Neural Network and Differential Evolution Algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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