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Экспериментальное исследование шламовой эрозии чугуна Ni-Hard (Нихард) и прогнозирование износа материалов с использованием искусственной нейронной сети (ИНС)

Мохит Д. Маквана, Б. М. Сутариа

Аннотация


Исследована износостойкость легированного чугуна Ni-Hard 4 (Нихард) при шламовой эрозии. Предпринята попытка прогнозировать эрозионный износ материалов с помощью искусственной нейронной сети (ИНС) на основе результатов экспериментального исследования износа шламового бака в различных условиях эксплуатации. Предложена модель ИНС для прогнозирования эрозионного износа материалов и показана ее высокая точность. Модель позволит выбирать материалы, удовлетворяющие заданным эксплуатационным характеристикам, без проведения длительных испытаний в различных условиях эксплуатации.

Ключевые слова


легированный чугун Нихард (Ni-Hard); трибология; шламовая эрозия; износ; искусственная нейронная сеть, прогнозирование

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Литература


Wilson K. C., Addie G. R., Sellgren A., Clift R. Centrifugal Pumps / In: Slurry Transport Using Centrifugal Pumps. Sprin¬ger: Boston, MA, 2006. P. 190 – 226.

Al-Bukhaiti M. A., Ahmed S. M., Badran F. M. F., Ema¬ra K. M. Effect of impingement angle on slurry erosion beha¬vi¬our and mechanisms of 1017 steel and high-chromium white cast iron // Wear. 2007. V. 262, Is. 9 – 10. P. 1187 – 1198.

Haviez L., Rosario T., Mohamad El Youssef et al. Semi-physical neural network model for fretting wear estimation // JIFS. 2015. V. 28, Is. 4. P. 1745 – 1753.

Li D., Lv R., Si G., You Y. Hybrid neural network-based prediction model for tribological properties of polyamide6-based friction materials // Polym. Compos. 2017. V. 38, Is. 8. P. 1705 – 1711.

Velten K., Reinicke R., Friedrich K. Wear volume prediction with artificial neural networks // Tribol. Int. 2000. V. 33, Is. 10. P. 731 – 736.

Jones Steven P., Ralph Jansen, Robert L. Fusaro. Preliminary investigation of neural network techniques to predict tribolo¬gical properties // Tribol. Trans. 1997. V. 40, Is. 2. P. 312 – 320.

Frangu L., Minodora R. Artificial neural networks applications in Tribology — a survey / In: 2001 NATO Advanced Study Institute on Neural Networks for Instrumentation, Measurement, and Related Industrial Applications: Study Cases, 2001. P. 35 – 42.

Kolodziejczyk T., Toscano R., Fouvry S., Morales-Espejel G. Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction // Wear. 2010. V. 268, Is. 1 – 2. P. 309 – 315.

Anand Kumar S., Ganesh Sundara Raman S., Sankara Na¬ra¬yanan T. S. N., Gnanamoorthy R. Prediction of fretting wear behavior of surface mechanical attrition treated Ti – 6Al – 4V using artificial neural network // Mater. Des. 2013. V. 49. P. 992 – 999.

Quiza R., Figueira L., Paulo Davim J. Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel // Int. J. Adv. Manuf. Technol. 2008. V. 37, Is. 7. P. 641 – 648.

Zhu J., Shi Y., Feng X. et al. Prediction on tribological properties of carbon fiber and TiO2 synergistic reinforced poly¬tetra¬fluoroethylene composites with artificial neural networks // Mater. Des. 2009. V. 30, Is. 4. P. 1042 – 1049.

Gyurova L. A., Friedrich K. Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites // Tribol. Int. 2011. V. 44, Is. 5. P. 603 – 609.

Saravanan S. D., Senthilkumar M. Prediction of tribological behaviour of rice husk ash reinforced aluminum alloy matrix com¬posites using artificial neural network // Russ. J. Non-Ferr. Met+. 2015. V. 56, Is. 1. P. 97 – 106.

El-Sonbaty I. A., Khashaba U. A., Selmy A. I., Ali A. I. Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach // J. Mater. Process. Technol. 2008. V. 200, Is. 1 – 3. P. 271 – 278.

Dhanasekar B., Ramamoorthy B. Restoration of blurred ima¬ges for surface roughness evaluation using machine vision // Tribol. Int. 2010. V. 43, Is. 1 – 2. P. 268 – 276.

Aleksendrić D., Barton D. C. Neural network prediction of disc brake performance // Tribol. Int. 2009. V. 42, Is. 7. P. 1074 – 1080.

Senatore A., D’Agostino V., Di Giuda R., Petrone V. Experimental investigation and neural network prediction of brakes and clutch material frictional behaviour considering the sliding acceleration influence // Tribol. Int. 2011. V. 44, Is. 10. P. 1199 – 1207.

Wang J., Ma Y., Zhang L. et al. Deep learning for smart manufacturing: Methods and applications // J. Manuf. Syst. 2018. V. 48. P. 144 – 156.

Butler Keith T., Daniel W. Davies, Hugh Cartwright et al. Machine learning for molecular and materials science // Nature. 2018. V. 559, Is. 7715. P. 547 – 555.

Bhadeshia H. Neural networks in materials science // ISIJ Int. 1999. V. 39, Is. 10. P. 966 – 979.

Sha W., Edwards K. L. The use of artificial neural networks in materials science based research // Mater. Des. 2007. V. 28, Is. 6. P. 1747 – 1752.

Raeder J., Larson D., Li W. et al. Open GGCM simulations for the THEMIS mission // Space Sci. Rev. 2008. V. 141, Is. 1. P. 535 – 555.

Desale Girish R., Gandhi Bhupendra K., Jain S. C. Improvement in the design of a pot tester to simulate erosion wear due to solid–liquid mixture // Wear. 2005. V. 259, Is. 1 – 6. P. 196 – 202.

Desale Girish R., Gandhi Bhupendra K., Jain S. C. Slurry erosion of ductile materials under normal impact condition // Wear. 2008. V. 264, Is. 3 – 4. P. 322 – 330.

Desale Girish R., Gandhi Bhupendra K., Jain S. C. Particle size effects on the slurry erosion of aluminium alloy (AA6063) // Wear. 2009. V. 266, Is. 11 – 12. P. 1066 – 1071.

Desale Girish R., Gandhi Bhupendra K., Jain S. C. Development of correlations for predicting the slurry erosion of ductile materials // J. Tribol. 2011. V. 133, Is. 3. 031603.

Shitole Pankaj P., Gawande Shravan H., Desale Girish R., Nandre Bhushan D. Effect of impacting particle kinetic energy on slurry erosion wear // J. Bio Tribo Corros. 2015. V. 1, Is. 4. P. 29.

More S. R., Bhatt D. V., Menghani J. V. Study of the parametric performance of solid particle erosion wear under the slurry pot test rig // Tribol. Ind. 2017. V. 39, Is. 4. P. 471.

Tarodiya Rahul, Gandhi Bhupendra K. Experimental investigation of centrifugal slurry pump casing wear handling so¬lid-li¬quid mixtures // Wear. 2019. V. 434. 202972.

Finnie I. Some observations on the erosion of ductile metals // Wear. 1972. V. 19, Is. 1. P. 81 – 90.

Levy A. V., Hickey G. Liquid-solid particle slurry erosion of steels // Wear. 1987. V. 117, Is. 2. P. 129 – 146.

Wheeler D. W., Wood R. J. K. Erosion of hard surface coatings for use in offshore gate valves // Wear. 2005. V. 258, Is. 1 – 4. P. 526 – 536.

More Satish R., Bhatt Dhananjay V., Menghani Jyoti V. Failure analysis of coal bottom ash slurry pipeline in thermal power plant // Eng. Fail. Anal. 2018. V. 90. P. 489 – 496.

Rawat Anubhav, Singh S. N., Seshadri V. Erosion wear studies on high concentration fly ash slurries // Wear. 2017. V. 378. P. 114 – 125.




DOI: https://doi.org/10.30906/mitom.2023.6.35-41

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