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Materials Science and Technology Volume 28, 2012 - Issue 8
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Original Article

Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel

İ AsilturkSelçuk University, Technology Faculty, Konya42075, TURKEYCorrespondence[email protected]
,
H KahramanliSelçuk University, Technology Faculty, Konya42075, TURKEY
&
H El MounayriPurdue School of Engineering and Technology, IUPUI, 723 W. Michigan Street, Indianapolis, 46202, IN, USA
Pages 980-986 | Received 09 Mar 2012, Accepted 30 Mar 2012, Published online: 12 Nov 2013

Citations (13)

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Arunkumar K & Murugarajan A. (2022) Influence of AWJM process parameters on the surface quality of chicken feather fiber reinforced composite. Materials and Manufacturing Processes 37:2, pages 221-229.
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Articles from other publishers (12)

Chong Peng, Zhongyuan Che, T.W. Liao & Zhongwen Zhang. (2023) Prediction using multi-objective slime mould algorithm optimized support vector regression model. Applied Soft Computing, pages 110580.
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Mehdi Safari, Amir Hossein Rabiee & Jalal Joudaki. (2023) Developing a Support Vector Regression (SVR) Model for Prediction of Main and Lateral Bending Angles in Laser Tube Bending Process. Materials 16:8, pages 3251.
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T. Mohanraj. (2021) Application of AI techniques for modeling the performance measures in milling of 7075-T6 hybrid aluminum metal matrix composites. International Journal of Modeling, Simulation, and Scientific Computing 13:04.
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Hind Abdulla, Maher Maalouf, Imad Barsoum & Heungjo An. (2022) Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L. Applied Sciences 12:9, pages 4252.
Crossref
Mintu Pal & Sibsankar Dasmahapatra. (2022) Estimation of cutting forces and tool tip temperature in turning operation with help of artificial neural network. Materials Today: Proceedings 66, pages 1623-1632.
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Rao M. C. Karthik, Rashmi L. Malghan, Fuat Kara, Arunkumar Shettigar, Shrikantha S. Rao & Mervin A. Herbert. (2021) Influence of Support Vector Regression (SVR) on Cryogenic Face Milling. Advances in Materials Science and Engineering 2021, pages 1-18.
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Longhua Xu, Chuanzhen Huang, Jiahui Niu, Chengwu Li, Jun Wang, Hanlian Liu & Xiaodan Wang. (2021) An improved case-based reasoning method and its application to predict machining performance. Soft Computing 25:7, pages 5683-5697.
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JongCheol Pyo, Hongtao Duan, Mayzonee Ligaray, Minjeong Kim, Sangsoo Baek, Yong Sung Kwon, Hyuk Lee, Taegu Kang, Kyunghyun Kim, YoonKyung Cha & Kyung Hwa Cho. (2020) An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery. Remote Sensing 12:7, pages 1073.
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Adel Taha Abbas, Danil Yurievich Pimenov, Ivan Nikolaevich Erdakov, Tadeusz Mikolajczyk, Mahmoud Sayed Soliman & Magdy Mostafa El Rayes. (2019) Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel. The International Journal of Advanced Manufacturing Technology 105:5-6, pages 2151-2165.
Crossref
Kang He, Zhuanzhe Zhao, Minping Jia & Conghu Liu. (2018) Dynamic Bayesian Network-Based Approach by Integrating Sensor Deployment for Machining Process Monitoring. IEEE Access 6, pages 33362-33375.
Crossref
A. T. Abbas, D. Yu. Pimenov, I. N. Erdakov, T. Mikolajczyk, E. A. El Danaf & M. A. Taha. (2017) Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method. The International Journal of Advanced Manufacturing Technology 93:5-8, pages 2375-2392.
Crossref
Junkai Wang, Fei Qiao, Fu Zhao & John W. Sutherland. (2016) A Data-Driven Model for Energy Consumption in the Sintering Process. Journal of Manufacturing Science and Engineering 138:10.
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