Investigation of Utilization of Machine Learning Applications in Mechanical Engineering

Zahin, Muhtadi Munawar

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dc.contributor.author	Zahin, Muhtadi Munawar
dc.date.accessioned	2020-10-31T17:38:52Z
dc.date.available	2020-10-31T17:38:52Z
dc.date.issued	2018-11-15
dc.identifier.citation	1. Finger, Susan & R. Dixon, John. (1989). A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes. Research in Engineering Design. 1. 51-67. 10.1007/BF01580003. 2. Dutton. D.M., Conroy. G.V., (1997), A review of Machine Learning, The Knowledge Engineering Review, 12, 4,: 341-367. 3. Engineering "mechanical engineering". The American Heritage Dictionary of the English Language, Fourth Edition. 4. "Heron of Alexandria". Encyclopædia Britannica 2010 - Encyclopædia Britannica OnlineNeedham, Joseph (1986). Science and Civilization in China: Volume 4. Taipei: Caves Books, Ltd. 5. Needham, Joseph (1986). Science and Civilization in China: Volume 4. Taipei: Caves Books, Ltd. 6. Al-Jazarí. The Book of Knowledge of Ingenious Mechanical Devices: Kitáb fí ma'rifat al- hiyal al-handasiyya. Springer, 1973. 7. https://www.britannica.com/technology/engineering 8. R. A. Buchanan. The Economic History Review, New Series, Vol. 38, No. 1 (Feb., 1985): 42–60. 9. http://archive.wikiwix.com/cache/20110223145123/http://anniversary.asme.org/history.s html 10. https://www.britannica.com/science/thermodynamics 11. https://www.qsstudy.com/physics/significance-first-law-thermodynamics 12. Knudsen, Jens M.; Hjorth, Poul (2012). Elements of Newtonian Mechanics. Springer Science & Business Media. : 30. ISBN 978-3-642-97599-8. 13. Rustum Roy (1979) interdisciplinary science on campus, pages 161–96 in Interdisciplinarity and Higher Education, J.J. Kockelmans editor, Pennsylvania State University Press 14. Olson, Gregory. "A Materials Science Timeline". Materials World Modules. 15. Vincent, Bernedetta. "Materials science and engineering: an artificial discipline about to explode". History of Recent Materials Science. 16. https://chemistry.stackexchange.com/questions/69346/a-unit-cell-for-graphene 17. Papageorigiou. D.M., Kinloch I.A.m Young R.J., (2017), Mechanical properties of graphene and graphene-base nanocomposites, Progress in Materials Science, 90, : 75-127. 18. Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C. "Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)", JOM 65: 1501-1509 (2013). doi:10.1007/s11837-013- 0755-4 19. https://en.wikipedia.org/wiki/Structural_analysis 20. Victor E. Saouma. "Lecture Notes in Structural Engineering" (PDF). University of Colorado. 21. https://en.wikipedia.org/wiki/History_of_structural_engineering#cite_note-Saouma-1 22. https://www.padeepz.net/ce6602-syllabus-structural-analysis-2-regulation-2013-anna- university/ 23. https://en.wikipedia.org/wiki/Fluid_mechanics 24. https://www.scribd.com/document/39741480/History-of-Fluid-mechanics 25. https://www.chmarine.com/acatalog/Barigo-Sole-70mm-Brass-Barometer.html 26. http://www.npd-solutions.com/cfd.html 27. Gorgius. A., (2012), A reliable approach to the solution of Navier-Stokes equations, Applied Mathematics Letters, 25, 12, 28. http://www.corvetteblogger.com/images/content/080409_19.jpg 29. Landau. L.D., Lifshitz. E.M., Course of Theoretical Physics: Theory of Elasticity Butterworth-Heinemann, ISBN 0-7506-2633-X 30. https://www.britannica.com/science/mechanics-of-solids 31. https://www.journals.elsevier.com/mechatronics 32. https://en.wikipedia.org/wiki/Mechatronics 33. https://medium.com/mistyrobotics/mech-a-what-mechatronics-the-engineering-field-you- didnt-know-existed-1b6edc7b3fa9 34. http://engineering.nyu.edu/mechatronics/Description/evolution.htm 35. Xia F., Yang L.T., Wang L., Vinel A., (2012). Internet of Things. International Journal of Communications System. 25 : 1101-1102. DOI: 10.1002/dac.2417 36. https://www.forbes.com/sites/bernardmarr/2016/02/19/a-short-history-of-machine- learning-every-manager-should-read/#64e734a615e7 37. https://en.wikipedia.org/wiki/Machine_learning 38. Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media. 39. Morris, A. (2014, August) World’s Largest Materials Database Now Open, Retrieved from:https://www.mccormick.northwestern.edu/magazine/fall-2014/materials- database.html 40. Rafiee, J., Tse, P. W., Harifi, A., & Sadeghi, M. H. (2009). A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system. Expert Systems with Applications, 36(3), 4862-4875. 41. Ling, J., & Templeton, J. (2015). Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. Physics of Fluids, 27(8), 085103. 42. Şencan, A., & Kalogirou, S. A. (2005). A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples. Energy Conversion and Management, 46(15-16), 2405-2418. 43. Domanski, P. A., Brown, J. S., Heo, J., Wojtusiak, J., & McLinden, M. O. (2014). A thermodynamic analysis of refrigerants: Performance limits of the vapor compression cycle. International Journal of Refrigeration, 38, 71-79. 44. https://www.mathworks.com/help/stats/machine-learning-in-matlab.html 45. https://www.britannica.com/science/algorithm 46. Powell, M. J. (1973). On search directions for minimization algorithms. Mathematical programming, 4(1), 193-201. 47. Yu, Wen Ci. 1979. "The convergent property of the simplex evolutionary technique". Scientia Sinica [Zhongguo Kexue]: 69–77. 48. Spendley, W. G. R. F. R., Hext, G. R., & Himsworth, F. R. (1962). Sequential application of simplex designs in optimisation and evolutionary operation. Technometrics, 4(4), 441- 461. 49. http://www.scholarpedia.org/article/Nelder-Mead_algorithm 50. https://codesachin.wordpress.com/2016/01/16/nelder-mead-optimization/ 51. https://www.mathworks.com/products/matlab.html 52. Zhu, J. (2017). Improvements on mapping soil liquefaction at a regional scale (Doctoral dissertation, Tufts University). 53. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York, NY, USA:: Springer series in statistics. 54. Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140. 55. Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation forest: A new classifier ensemble method. IEEE transactions on pattern analysis and machine intelligence, 28(10), 1619-1630. 56. https://en.wikipedia.org/wiki/Density_functional_theory#cite_ref-dftmag_1-0 57. http://newton.ex.ac.uk/research/qsystems/people/coomer/dft_intro.html 58. Assadi, M. H. N., & Hanaor, D. A. (2013). Theoretical study on copper's energetics and magnetism in TiO2 polymorphs. Journal of Applied Physics, 113(23), 233913. 59. Grimme, S. (2006). Semiempirical hybrid density functional with perturbative second- order correlation. The Journal of chemical physics, 124(3), 034108. 60. Vignale, G., & Rasolt, M. (1987). Density-functional theory in strong magnetic fields. Physical review letters, 59(20), 2360. 61. Runge, E., & Gross, E. K. (1984). Density-functional theory for time-dependent systems. Physical Review Letters, 52(12), 997. 62. Gross, E. K., & Dreizler, R. M. (Eds.). (2013). Density functional theory (Vol. 337). Springer Science & Business Media. 63. Pearson, M., Smargiassi, E., & Madden, P. A. (1993). Ab initio molecular dynamics with an orbital-free density functional. Journal of Physics: Condensed Matter, 5(19), 3221. 64. Leithold, L. (1996). The calculus 7 (p. 1394). HarperCollins College Publishing. 65. https://en.wikipedia.org/wiki/Gradient_boosting 66. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. 67. Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. (2000). Boosting algorithms as gradient descent. In Advances in neural information processing systems (pp. 512-518). 68. https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and- different-methods-of-clustering/ 69. Wilks, D. S. (2011). Cluster analysis. In International geophysics (Vol. 100, pp. 603-616). Academic press. 70. Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C., ... & Ray, T. S. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine, 8(1), 68. 71. Shapiro, E. Y. (1981). Inductive inference of theories from facts. Yale University, Department of Computer Science. 72. Džeroski, S. (2002). Data mining tasks and methods: Rule discovery: inductive logic programming approaches (pp. 348-353). Oxford University Press, Inc. 73. https://skymind.ai/wiki/deep-reinforcement-learning 74. Bertsekas, D. P., & Tsitsiklis, J. N. (1995, December). Neuro-dynamic programming: an overview. In Proceedings of the 34th IEEE Conference on Decision and Control (Vol. 1, pp. 560-564). Piscataway, NJ: IEEE Publ.. 75. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237-285. 76. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828. 77. Chopra, S., Hadsell, R., & LeCun, Y. (2005, June). Learning a similarity metric discriminatively, with application to face verification. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on(Vol. 1, pp. 539- 546). IEEE. 78. Weinberger, K. Q., Blitzer, J., & Saul, L. K. (2006). Distance metric learning for large margin nearest neighbor classification. In Advances in neural information processing systems (pp. 1473-1480). 79. Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. Knowledge discovery in databases , 229-238. 80. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273- 297. 81. Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapnik, V. (2001). Support vector clustering. Journal of machine learning research, 2(Dec), 125-137. 82. Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197-387. 83. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828. 84. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828. 85. https://www.python.org/doc/essays/blurb/ 86. https://www.techopedia.com/definition/3533/python 87. Python, J. (2007). Python programming language. In USENIX Annual Technical Conference. 88. Chemisky, Y., Chatazigeorgiou, G., & Meraghni, F. Simmit: An open source project for Interactive Learning Experience in Mechanics of Materials. 89. https://www.artima.com/intv/pythonP.html 90. http://python-history.blogspot.com/2009/01/brief-timeline-of-python.html 91. http://www.amk.ca/python/2.0 92. http://www.numpy.org/ 93. http://www.numpy.org/license.html#license 94. https://en.wikipedia.org/wiki/SciPy 95. https://www.scipy.org/about.html 96. Abu-Mostafa, Y.S. (2014, February), Learning from Data, Retrieved from: https://www.edx.org/course/learning-data-caltechx-cs1156x-0 97. VanderPlas, J. (2015, July 06), The Model Complexity Myth, Retrieved from: https://jakevdp.github.io/blog/2015/07/06/model-complexity-myth/	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/630
dc.description	Supervised by Prof. Dr. Md. Nurul Absar Chowdhury	en_US
dc.description.abstract	Machine learning is a subfield of Artificial Intelligence (AI). Its goal is to make sense of a particular group of data and convert them into models that can be utilized by machine and man. The important aspect of Machine Learning is this: given that it is properly executed, a system aided by machine learning will be able to re-define its parameters of consideration and update its mode of action with minimal human interaction with continued accumulation of case data. This aspect of it makes it very suitable for optimization projects, including those related to Mechanical Engineering. In this thesis, an investigation was carried out to assess the prospects of utilizing Machine Learning applications as a comprehensive analytical tool in the research activities by the Islamic University of Technology’s Mechanical and Chemical Department (IUT-MCE). It aimed to do so by conducting a project using Machine Learning and assessing the progress of said report to determine the possibilities of inducting the practice of utilizing Machine Learning applications in IUT-MCE and provide a possible route in order to make such an induction possible. The results of the thesis show that while Machine Learning applications do have a potential to enhance the research capabilities of IUT-MCE, there is a need for extensive investment in the avenue with an expected delay of useful outcome by 5-6 years depending on the specific area of research. Nevertheless, the writers feel confident that utilizing Machine Learning applications will be worth the investment considering the benefits it is likely to bring.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Mechanical and Production Engineering, Islamic University of Technology, Board Bazar, Gazipur, Bangladesh	en_US
dc.title	Investigation of Utilization of Machine Learning Applications in Mechanical Engineering	en_US
dc.type	Thesis	en_US