Proceedings of the 6th Mechanical Engineering, Science and Technology International conference (MEST 2022)

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Multi-objective Optimization of Turning Process Steel SKD 11 Using BPNN-Artificial Bee Colony (ABC) Method

Authors
Mazwan1, *, M. Khoirul Effendi1, Bobby O. P. Soepangkat1, Satrio Darma Utama1, Ridhani Anita Fajardini1
1Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember Surabaya, 60111, Surabaya, East Java, Indonesia
*Corresponding author. Email: mazwan.polbeng@gmail.com
Corresponding Author
Mazwan
Available Online 19 April 2023.
DOI
10.2991/978-94-6463-134-0_49How to use a DOI?
Keywords
BPNN-ABC; Turning; Surface Roughness; Cutting Force; Feeding Force; Tool’s Life
Abstract

In the turning process, the force is a constraint factor that must be considered because a large cutting force will generally result in a high surface roughness value. Therefore, selecting suitable parameters for the turning process is necessary to minimize surface roughness (SR), cutting force (Fc), and feeding force (Ff) and increase the tool’s life (TL). This study uses a combination method of backpropagation neural network (BPNN) and artificial bee colony (ABC) to obtain the level of turning process parameters that produce maximum TL and minimum SR, Fc, and Ff. The material utilized in the turning process is SKD 11. This study used the L9 orthogonal array from the Taguchi experimentation design. The optimum parameters of optimization results using the ABC method are cutting speed 288.910 (m/min), depth of cut 0.5 (mm), feed rate 0.094 (mm/rev), and tool nose radius 0.931 (mm). Furthermore, the results of response prediction with BPNN compared to the average confirmation experiment produced errors below 5% which means that the BPNN-ABC method succeeded in optimizing and predicting multi-objective responses in this study.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 6th Mechanical Engineering, Science and Technology International conference (MEST 2022)
Series
Advances in Engineering Research
Publication Date
19 April 2023
ISBN
978-94-6463-134-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-134-0_49How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mazwan
AU  - M. Khoirul Effendi
AU  - Bobby O. P. Soepangkat
AU  - Satrio Darma Utama
AU  - Ridhani Anita Fajardini
PY  - 2023
DA  - 2023/04/19
TI  - Multi-objective Optimization of Turning Process Steel SKD 11 Using BPNN-Artificial Bee Colony (ABC) Method
BT  - Proceedings of the 6th Mechanical Engineering, Science and Technology International conference (MEST 2022)
PB  - Atlantis Press
SP  - 520
EP  - 529
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-134-0_49
DO  - 10.2991/978-94-6463-134-0_49
ID  - 2023
ER  -