Determination of Stress Concentration Factor (Kt) for a Crankshaft Under Bending Loading: An Artificial Neural Networks Approach
Öz
Crankshafts are used in especially engines. Crankshafts are usually effected bending and torsional stress. These loading situations are important for design of engine and its parts. Crankshaft design requires design experience and engineering calculations. When the engineering calculation is performed, stress concentration factor is put into effect. These factors are usually obtained from Stress concentration factor Charts. Reading the real stress concentration factor from charts can be resulted in getting from false values. This study is an update work of old studies. Using the new computer techniques stress concentration factor values were converted into numerical values. Stress concentration factor values were collected in a database. Artificial Neural Network (ANN) Model was improved using the database. ANN model is gave to us time economy and high accuracy of obtaining the stress concentration values.
Anahtar Kelimeler
Stress Concentration Factor (SCF) Crankshaft Design Artificial Neural Network (ANN)
Kaynakça
- REFERENCES [1] Arai J,” The bending stress concentration factor of solid crankshaft”, Bulletin of JSME, 8, 322, (1965).
- [2] Peterson R. E, “Fatigue of shafts having keyways”, Proc. ASTM. 32.2, 413, (1932).
- [3] Peterson R. E, “Methods of correlating data from fatigue tests of stress concentration specimens”, Stephen Timoshenko Anniversary Volume, Macmillan, New York. 179, (1938).
- [4] Staul G, “Der Einfluβ der form auf di Spannungen in Kurbelwellen”, Konstruction, 10, 2, (1958).
- [5] Pfender M, Amedick E. and Sonntag, “Einfluβ der Formgebung auf die soannungsverteilung in kurbelkropfungen”, M.T.Z., 27, 225, (1966).
- [6] Fessler H. and Sood, V. K, “Stress distribution in some diesel engine crankshaft, Trans”, ASME Diesel and engine Power Division conference, Toronto, Paper 71, 1, (1971).
- [7] Baragetti S, “Design Criteria for High Power Engines Crankshafts the Open”, Mechanical Engineering Journal, 9, 271-281, (2015).
- [8] Bargis E. Garro A. And Vullo V, “Crankshaft design and evaluation -Part 1 -Critical analysis and experimental evaluation of current methods, Part 2 -A modern design method: modal analysis -Part 3 -Modern design method: direct integration”, In: The international conference on reliability, stress analysis and failure prevention, Century 2 emerging technology conferences, San Francisco, California, (1980).
- [9] Chien W.Y.; Pan J.; Close D. and Ho S. “Fatigue analysis of crankshaft sections under bending with consideration of residual stresses”, Int. J. Fatigue, 27, 1-19, (2005).
- [10] Choi K.S. and Pan J. “Simulations of stress distributions in crankshaft sections under fillet rolling and bending fatigue tests”, Int. J. Fatigue, 31, 544-557, (2009).
- [11] Ozkan M. T.; Toktas I, “Determination of the stress concentration factor (Kt) in a rectangular plate with a hole under tensile stress using different methods”, Materials Testing, 58(10), 839-847, (2016).
- [12] Ozkan M. T, “Surface roughness during the turning process of a 50CrV4 (SAE6150) steel and ANN based modeling”, Materials Testing, 57(10), 889-896, (2015).
- [13] Ozkan M. T.; Ulas H. B.; Bilgin M, “Experimental Design and Artificial Neural Network Model For Turning 50crv4 (Sae 6150) Alloy Using Coated Carbide/Cermet Cuting Tools”, Materiali In Tehnologije / Materials and Technology, 48(2), 227-236, (2014).
- [14] Ulas H. B, Ozkan M. T., Malkoc Y., ”Vibration prediction in drilling processes with HSS and carbide drill bit by means of artificial neural networks”, Neural Computing and Applications, 31(9): 5547–5562, (2019)
- [15] Ozkan M. T,” Experimental and artificial neural network study of heat formation values of drilling&boring operations on Al 7075 T6 workpiece”, Indian Journal of Engineering & Materials Science, 20(4), 259-268, (2014).
- [16] Ozkan M. T, “Notch sensitivity factor calculationin the design of shafts using artificial neural network system”, Education Science and Technology Part A: Energy Science and Research, 30(1), 621-630, (2012).
- [17] Pilkey, W. D, “Formulas for Stress, Strain, and Structural Matrices”, 2nd ed.,Wiley, New York,. (2005).
Determination of Stress Concentration Factor (Kt) for a Crankshaft Under Bending Loading: An Artificial Neural Networks Approach
Öz
Crankshafts are used in especially engines. Crankshafts are usually effected bending and torsional stress. These loading situations are important for design of engine and its parts. Crankshaft design requires design experience and engineering calculations. When the engineering calculation is performed, stress concentration factor is put into effect. These factors are usually obtained from Stress concentration factor Charts. Reading the real stress concentration factor from charts can be resulted in getting from false values. This study is an update work of old studies. Using the new computer techniques stress concentration factor values were converted into numerical values. Stress concentration factor values were collected in a database. Artificial Neural Network (ANN) Model was improved using the database. ANN model is gave to us time economy and high accuracy of obtaining the stress concentration values.
Anahtar Kelimeler
Stress concentration factor (SCF) crankshaft design artificial neural network (ANN)
Kaynakça
- REFERENCES [1] Arai J,” The bending stress concentration factor of solid crankshaft”, Bulletin of JSME, 8, 322, (1965).
- [2] Peterson R. E, “Fatigue of shafts having keyways”, Proc. ASTM. 32.2, 413, (1932).
- [3] Peterson R. E, “Methods of correlating data from fatigue tests of stress concentration specimens”, Stephen Timoshenko Anniversary Volume, Macmillan, New York. 179, (1938).
- [4] Staul G, “Der Einfluβ der form auf di Spannungen in Kurbelwellen”, Konstruction, 10, 2, (1958).
- [5] Pfender M, Amedick E. and Sonntag, “Einfluβ der Formgebung auf die soannungsverteilung in kurbelkropfungen”, M.T.Z., 27, 225, (1966).
- [6] Fessler H. and Sood, V. K, “Stress distribution in some diesel engine crankshaft, Trans”, ASME Diesel and engine Power Division conference, Toronto, Paper 71, 1, (1971).
- [7] Baragetti S, “Design Criteria for High Power Engines Crankshafts the Open”, Mechanical Engineering Journal, 9, 271-281, (2015).
- [8] Bargis E. Garro A. And Vullo V, “Crankshaft design and evaluation -Part 1 -Critical analysis and experimental evaluation of current methods, Part 2 -A modern design method: modal analysis -Part 3 -Modern design method: direct integration”, In: The international conference on reliability, stress analysis and failure prevention, Century 2 emerging technology conferences, San Francisco, California, (1980).
- [9] Chien W.Y.; Pan J.; Close D. and Ho S. “Fatigue analysis of crankshaft sections under bending with consideration of residual stresses”, Int. J. Fatigue, 27, 1-19, (2005).
- [10] Choi K.S. and Pan J. “Simulations of stress distributions in crankshaft sections under fillet rolling and bending fatigue tests”, Int. J. Fatigue, 31, 544-557, (2009).
- [11] Ozkan M. T.; Toktas I, “Determination of the stress concentration factor (Kt) in a rectangular plate with a hole under tensile stress using different methods”, Materials Testing, 58(10), 839-847, (2016).
- [12] Ozkan M. T, “Surface roughness during the turning process of a 50CrV4 (SAE6150) steel and ANN based modeling”, Materials Testing, 57(10), 889-896, (2015).
- [13] Ozkan M. T.; Ulas H. B.; Bilgin M, “Experimental Design and Artificial Neural Network Model For Turning 50crv4 (Sae 6150) Alloy Using Coated Carbide/Cermet Cuting Tools”, Materiali In Tehnologije / Materials and Technology, 48(2), 227-236, (2014).
- [14] Ulas H. B, Ozkan M. T., Malkoc Y., ”Vibration prediction in drilling processes with HSS and carbide drill bit by means of artificial neural networks”, Neural Computing and Applications, 31(9): 5547–5562, (2019)
- [15] Ozkan M. T,” Experimental and artificial neural network study of heat formation values of drilling&boring operations on Al 7075 T6 workpiece”, Indian Journal of Engineering & Materials Science, 20(4), 259-268, (2014).
- [16] Ozkan M. T, “Notch sensitivity factor calculationin the design of shafts using artificial neural network system”, Education Science and Technology Part A: Energy Science and Research, 30(1), 621-630, (2012).
- [17] Pilkey, W. D, “Formulas for Stress, Strain, and Structural Matrices”, 2nd ed.,Wiley, New York,. (2005).
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Eylül 2020 |
| Gönderilme Tarihi | 1 Şubat 2020 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 23 Sayı: 3 |
Kaynak Göster
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