Determination of Stress Concentration Factor for a Stepped Bar Under Bending Loading: An Artificial Neural Networks Approach
Keywords:
Stress Concentration Factor (SCF), Artificial Neural Network (ANN), bending loading
Abstract
Even today, charts and formulas derived from experimental determinations are used to obtain the stress concentration factor.
Stress concentration factors from charts can be converted into numerical values using computational techniques. Stress concentration factor values were collected in a database and an Artificial Neural Network (ANN) model can be developed for improving the database. ANN model provides accuracy in obtaining the stress concentration factors. Using the static stress concentration factor for a stepped bar under a bending load we can quantify the impact of notches in fatigue.
Downloads
Download data is not yet available.
References
[1]. Boger Z., Guterman H., Knowledge extraction from artificial neural network models, IEEE Systems, Man, and Cybernetics Conference, Orlando, FL, USA, 1997.
[2]. Budynas-Nisbett, Shigley's Mechanical Engineering Design, 8th Ed.
[3]. Giannini F., Laveglia V., Rossi A., Zanca D., Zugarini A., Neural Networks for Beginners A fast implementation in Matlab, Torch, TensorFlow, March 17, 2017.
[4]. Simon Haykin, Neural Networks and Learning Machine, Third Edition, Pearson Prentice Hall, 2009.
[5]. Saurabh Karsoliya, Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture, International Journal of Engineering Trends and Technology – vol. 3, issue 6, 2012.
[6]. Abhishek K., Kishore D. J., Prakashkumar Kavalur, Akshay N H., ANN modeling for prediction of cutting force component during orthogonal turning, International Research Journal of Engineering and Technology (IRJET), vol. 5, issue 6, June, 2018.
[7]. Souâd Makhfi, Raphaël Velasco, Malek Habak, Kamel Haddouche, Pascal Vantomme, An Optimized ANN Approach for Cutting Forces Prediction in AISI 52100 Bearing Steel Hard Turning, p-ISSN: 2163-2669, e-ISSN: 2163-2677, 3(1), p. 24-32, doi: 10.5923/j.scit.20130301.03, 2013.
[8]. Krzysztof L. Molski, Stress concentration at load-carrying fillet welded cruciform joints subjected to tensile and bending loads, Acta mechanica et automatica, vol. 13, no. 4, DOI 10.2478/ama-2019-0033, 2019.
[9]. Neuber H., Theory of Stress Concentration for Shear-Strained Prismatical Bodies with Arbitrary Nonlinear Stress-Strain Law, ASME Journal of Applied Mechanics, 28, 1961.
[10]. Robert Norton, Machine Design – An integrated Approach, 6th Edition, Pearson Publisher, 2014.
[11]. Foram S. Panchal, Mahesh Panchal, Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network, International Journal of Computer Science and Mobile Computing, vol. 3, issue 11, p. 455-464, November 2014.
[12]. Peterson R. E., Stress concentration factors, John Willey and Sons, 1975.
[13]. George Sines, Waisman J. L., Metal Fatigue, McGraw-Hill Book Company, 1959.
[14]. Alan J. Thomas, Miltos Petridis, Simon D. Walters, Saeed Malekshahi Gheytassi, Robert E. Morgan, On Predicting the Optimal Number of Hidden Nodes, International Conference on Computational Science and Computational Intelligence, 2015.
[15]. Alan J. Thomas, Simon D. Walters, Saeed Malekshahi Gheytassi, Robert E. Morgan, Miltos Petridis, On the Optimal Node Ratio between Hidden Layers: A Probabilistic Study, International Journal of Machine Learning and Computing, vol. 6, no. 5, October 2016.
[16]. Ihsan Toktas, Murat Tolga Ozkan, Fulya Erdemir, Nurullah Yuksel, Determination of Stress Concentration Factor (Kt) for a Crankshaft under Bending Loading: An Artificial Neural Networks Approach, Politeknik Dergisi, Journal of Polytechnic, ISSN: 1302-0900 (PRINT), ISSN: 2147-9429 (ONLINE).
[17]. Pilkey Walter D., Pilkey Deborah F., Peterson's Stress Concentration Factors, 3rd Edition.
[18]. ***, Matlab, The MathWorks, Inc., Natick, Massachusetts, United States, 2018a.
[19]. ***, https://materion.com/-/media/files/alloy/newsletters/technical-tidbits/issue-no-60---effectof-stress-concentration-on-fatigue-life.pdf.
[20]. ***, https://users.wpi.edu/~cfurlong/me3320/lect14/Lect14.pdf.
[21]. ***, https://research.iaun.ac.ir/pd/jjfesharaki/pdfs/UploadFile_9038.pdf.
[2]. Budynas-Nisbett, Shigley's Mechanical Engineering Design, 8th Ed.
[3]. Giannini F., Laveglia V., Rossi A., Zanca D., Zugarini A., Neural Networks for Beginners A fast implementation in Matlab, Torch, TensorFlow, March 17, 2017.
[4]. Simon Haykin, Neural Networks and Learning Machine, Third Edition, Pearson Prentice Hall, 2009.
[5]. Saurabh Karsoliya, Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture, International Journal of Engineering Trends and Technology – vol. 3, issue 6, 2012.
[6]. Abhishek K., Kishore D. J., Prakashkumar Kavalur, Akshay N H., ANN modeling for prediction of cutting force component during orthogonal turning, International Research Journal of Engineering and Technology (IRJET), vol. 5, issue 6, June, 2018.
[7]. Souâd Makhfi, Raphaël Velasco, Malek Habak, Kamel Haddouche, Pascal Vantomme, An Optimized ANN Approach for Cutting Forces Prediction in AISI 52100 Bearing Steel Hard Turning, p-ISSN: 2163-2669, e-ISSN: 2163-2677, 3(1), p. 24-32, doi: 10.5923/j.scit.20130301.03, 2013.
[8]. Krzysztof L. Molski, Stress concentration at load-carrying fillet welded cruciform joints subjected to tensile and bending loads, Acta mechanica et automatica, vol. 13, no. 4, DOI 10.2478/ama-2019-0033, 2019.
[9]. Neuber H., Theory of Stress Concentration for Shear-Strained Prismatical Bodies with Arbitrary Nonlinear Stress-Strain Law, ASME Journal of Applied Mechanics, 28, 1961.
[10]. Robert Norton, Machine Design – An integrated Approach, 6th Edition, Pearson Publisher, 2014.
[11]. Foram S. Panchal, Mahesh Panchal, Review on Methods of Selecting Number of Hidden Nodes in Artificial Neural Network, International Journal of Computer Science and Mobile Computing, vol. 3, issue 11, p. 455-464, November 2014.
[12]. Peterson R. E., Stress concentration factors, John Willey and Sons, 1975.
[13]. George Sines, Waisman J. L., Metal Fatigue, McGraw-Hill Book Company, 1959.
[14]. Alan J. Thomas, Miltos Petridis, Simon D. Walters, Saeed Malekshahi Gheytassi, Robert E. Morgan, On Predicting the Optimal Number of Hidden Nodes, International Conference on Computational Science and Computational Intelligence, 2015.
[15]. Alan J. Thomas, Simon D. Walters, Saeed Malekshahi Gheytassi, Robert E. Morgan, Miltos Petridis, On the Optimal Node Ratio between Hidden Layers: A Probabilistic Study, International Journal of Machine Learning and Computing, vol. 6, no. 5, October 2016.
[16]. Ihsan Toktas, Murat Tolga Ozkan, Fulya Erdemir, Nurullah Yuksel, Determination of Stress Concentration Factor (Kt) for a Crankshaft under Bending Loading: An Artificial Neural Networks Approach, Politeknik Dergisi, Journal of Polytechnic, ISSN: 1302-0900 (PRINT), ISSN: 2147-9429 (ONLINE).
[17]. Pilkey Walter D., Pilkey Deborah F., Peterson's Stress Concentration Factors, 3rd Edition.
[18]. ***, Matlab, The MathWorks, Inc., Natick, Massachusetts, United States, 2018a.
[19]. ***, https://materion.com/-/media/files/alloy/newsletters/technical-tidbits/issue-no-60---effectof-stress-concentration-on-fatigue-life.pdf.
[20]. ***, https://users.wpi.edu/~cfurlong/me3320/lect14/Lect14.pdf.
[21]. ***, https://research.iaun.ac.ir/pd/jjfesharaki/pdfs/UploadFile_9038.pdf.
Published
2023-09-15
How to Cite
1.
BOAZU D. Determination of Stress Concentration Factor for a Stepped Bar Under Bending Loading: An Artificial Neural Networks Approach. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Sep.2023 [cited 30Nov.2024];46(3):29-8. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/6248
Issue
Section
Articles
