Rise of Intelligent Machines: Influence of Artificial Intelligence on Mechanical Engineering Innovation
DOI:
https://doi.org/10.31181/sems1120244hKeywords:
Intelligent Machines, Artificial Intelligence, Smart Systems, Industry 4.0Abstract
The integration of artificial intelligence (AI) into mechanical engineering has precipitated a profound transformation in the way engineers conceive, design, and execute projects. This paper explores the multifaceted impact of AI on mechanical engineering innovation, elucidating the myriad ways in which intelligent machines are revolutionizing traditional practices and catalyzing unprecedented advancements. In the realm of design, AI algorithms are revolutionizing the conceptualization and optimization processes. By leveraging machine learning and optimization techniques, engineers can explore vast design spaces with unparalleled efficiency, uncovering innovative solutions that might otherwise remain elusive. These AI-driven design tools not only expedite the development cycle but also enable the creation of products and systems with enhanced performance characteristics, such as improved energy efficiency, structural integrity, and functional versatility. Moreover, AI's influence extends beyond the design phase and permeates the entire manufacturing ecosystem. AI-driven automation is reshaping production lines, enabling agile and adaptive manufacturing processes that respond dynamically to changing demands and conditions. Through the integration of sensors, actuators, and AI-powered control systems, factories are becoming increasingly intelligent and autonomous, optimizing resource utilization, minimizing waste, and maximizing throughput.
Downloads
References
Liu, J., Chang, H., Forrest, J. Y. L., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors. Technological Forecasting and Social Change, 158, 120142. https://doi.org/10.1016/j.techfore.2020.120142.
Chang, C. W., Lee, H. W., & Liu, C. H. (2018). A review of artificial intelligence algorithms used for smart machine tools. Inventions, 3(3), 41. https://doi.org/10.3390/inventions3030041.
David, S., Anand, R. S., Sheikh, S., Jebapriya, S., Andrew, J., & Xavier, S. B. (2021). A comprehensive overview on intelligent mechanical systems and its applications. Materials Today: Proceedings, 37, 733-736. https://doi.org/10.1016/j.matpr.2020.05.737.
Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R. (2018). Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integrating Materials and Manufacturing Innovation, 7, 157-172. https://doi.org/10.1007/s40192-018-0117-8.
Sahoo, S. K., Das, A. K., Samanta, S., & Goswami, S. S. (2023). Assessing the role of sustainable development in mitigating the issue of global warming. Journal of process management and new technologies, 11(1-2), 1-21. https://doi.org/10.5937/jpmnt11-44122.
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492. https://doi.org/10.3390/su12020492.
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, 142(11), 110804. https://doi.org/10.1115/1.4047855.
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111. https://doi.org/10.1142/S2424862221300040.
Wang, K., Ying, Z., Goswami, S. S., Yin, Y., & Zhao, Y. (2023). Investigating the role of artificial intelligence technologies in the construction industry using a Delphi-ANP-TOPSIS hybrid MCDM concept under a fuzzy environment. Sustainability, 15(15), 11848. https://doi.org/10.3390/su151511848.
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006.
Natale, S., & Ballatore, A. (2020). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence, 26(1), 3-18. https://doi.org/10.1177/1354856517715164.
Sahoo, S. K., Goswami, S. S., & Halder, R. (2024). Supplier Selection in the Age of Industry 4.0: A Review on MCDM Applications and Trends. Decision Making Advances, 2(1), 32-47. https://doi.org/10.31181/dma21202420.
Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia-Social and Behavioral Sciences, 195, 564-573. https://doi.org/10.1016/j.sbspro.2015.06.134.
Mittal, U., & Panchal, D. (2023). AI-based evaluation system for supply chain vulnerabilities and resilience amidst external shocks: An empirical approach. Reports in Mechanical Engineering, 4(1), 276-289. https://doi.org/10.31181/rme040122112023m.
Boyd, R., & Holton, R. J. (2018). Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation?. Journal of Sociology, 54(3), 331-345. https://doi.org/10.1177/1440783317726591.
Mittal, U. (2023, August). Detecting Hate Speech Utilizing Deep Convolutional Network and Transformer Models. International Conference on Electrical, Electronics, Communication and Computers, IEEE, pp. 1-4. https://doi.org/10.1109/ELEXCOM58812.2023.10370502.
Sahoo, S. K., Goswami, S. S., Sarkar, S., & Mitra, S. (2023). A review of digital transformation and industry 4.0 in supply chain management for small and medium-sized enterprises. Spectrum of Engineering and Management Sciences, 1(1), 58-72. https://doi.org/10.31181/sems1120237j.
Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?. Sustainability, 13(11), 5788. https://doi.org/10.3390/su13115788.
Lee, J., Ghaffari, M., & Elmeligy, S. (2011). Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems. Annual Reviews in Control, 35(1), 111-122. https://doi.org/10.1016/j.arcontrol.2011.03.007.
Mittal, U., Yang, H., Bukkapatnam, S. T., & Barajas, L. G. (2008). Dynamics and performance modeling of multi-stage manufacturing systems using nonlinear stochastic differential equations. International Conference on Automation Science and Engineering, IEEE, pp. 498-503. https://doi.org/10.1109/COASE.2008.4626530.
Sahoo, S. K., & Goswami, S. S. (2024). Green Supplier Selection using MCDM: A Comprehensive Review of Recent Studies. Spectrum of Engineering and Management Sciences, 2(1), 1-16. https://doi.org/10.31181/sems1120241a.
Rodriguez-Rodriguez, I., Rodriguez, J. V., Shirvanizadeh, N., Ortiz, A., & Pardo-Quiles, D. J. (2021). Applications of artificial intelligence, machine learning, big data and the internet of things to the COVID-19 pandemic: A scientometric review using text mining. International Journal of Environmental Research and Public Health, 18(16), 8578. https://doi.org/10.3390/ijerph18168578.
Hoosain, M. S., Paul, B. S., & Ramakrishna, S. (2020). The impact of 4IR digital technologies and circular thinking on the United Nations sustainable development goals. Sustainability, 12(23), 10143. https://doi.org/10.3390/su122310143.
Al-Gerafi, M. A., Goswami, S. S., Khan, M. A., Naveed, Q. N., Lasisi, A., AlMohimeed, A., & Elaraby, A. (2024). Designing of an effective e-learning website using inter-valued fuzzy hybrid MCDM concept: A pedagogical approach. Alexandria Engineering Journal, 97, 61-87. https://doi.org/10.1016/j.aej.2024.04.012.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics. https://doi.org/10.1016/j.cogr.2023.04.001.
Mohan, T. R., Roselyn, J. P., Uthra, R. A., Devaraj, D., & Umachandran, K. (2021). Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery. Computers & Industrial Engineering, 157, 107267. https://doi.org/10.1016/j.cie.2021.107267.
Ionașcu, A. E., Goswami, S. S., Dănilă, A., Horga, M. G., Barbu, C., & Adrian, Ş. C. (2024). Analyzing Primary Sector Selection for Economic Activity in Romania: An Interval-Valued Fuzzy Multi-Criteria Approach. Mathematics, 12(8), 1157. https://doi.org/10.3390/math12081157.
Jenis, J., Ondriga, J., Hrcek, S., Brumercik, F., Cuchor, M., & Sadovsky, E. (2023). Engineering applications of artificial intelligence in mechanical design and optimization. Machines, 11(6), 577. https://doi.org/10.3390/machines11060577.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Spectrum of Engineering and Management Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
