Artificial Intelligence in Manufacturing and Processing: A Comprehensive Review of Applications and Future Trends
Keywords:
Artificial Intelligence, Manufacturing, Processing Industries, Machine Learning, Predictive Maintenance, Quality Control, Automation, Edge Computing, Sustainability.Abstract
The renewed strength of Friday robots in the production and processing of products is happening with the assistance of Artificial Intelligence (AI), and the intelligent review becomes fiercer and more ground-breaking. This review reviews the most important concepts of AI and points out the ways it is applied to production planning and predictive maintenance, quality control, robotics, and supply chain optimization. In processing, AI is used in developing processes, monitoring in-situ and safety standards in pharmaceutical, chemical and food companies. The examples of the best companies are the real cases to prove the effectiveness of the improvement of the productivity, quality, and sustainability. Beside its capability to change the world, the implementation of AI is confronted with problems of data quality, cost of implementation, lack of skills in the labor force and ethics. The additional new trends described in the review are explaining AI, edge AI, hyper automation, and sustainable AI practices. Overall, the article notes that the journey to AI integration is not easy, yet it is the important step towards having robust, adaptive, and future-sustaining industrial systems.
References
. Malhi, A., Yan, R., and Gao, R., 2011, “Prognosis of Defect Propagation Based on Recurrent Neural Networks,” IEEE Trans. Instrum. Meas., 60(3), pp. 703–711.
. Lu Y. Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of management analytics. 2019 Jan 2;6(1):1-29.
. Zhang, J., Wang, P., Yan, R., and GAO, R., 2018, “Long Short-Term Memory for Machine Remaining Life Prediction,” J. Manuf. Syst., 48, pp. 78–86.
. Zhang, Y., Xiong, R., He, H., and Pecht, M. G., 2018, “Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries,” IEEE Trans. Veh. Technol., 67(7), pp. 5695–5705.
. Arinez JF, Chang Q, Gao RX, Xu C, Zhang J. Artificial intelligence in advanced manufacturing: current status and future outlook. Journal of Manufacturing Science and Engineering. 2020 Nov 1;142(11):110804.
. Plathottam SJ, Rzonca A, Lakhnori R, Iloeje CO. A review of artificial intelligence applications in manufacturing operations. Journal of Advanced Manufacturing and Processing. 2023 Jul;5(3):e10159.
. Elahi M, Afolaranmi SO, Martinez Lastra JL, Perez Garcia JA. A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discover Artificial Intelligence. 2023 Dec 7;3(1):43.
. Guo, L., Li, N., Jia, F., Lei, Y., and Lin, J., 2017, “A Recurrent Neural Network Based Health Indicator for Remaining Useful Life Prediction of Bearings,” Neurocomputing, 240, pp. 98–109.
. Lu T. Towards a fully automated 3D printability checker. In: 2016 IEEE International conference on industrial technology (ICIT). 2016. pp. 922–927.
. Wang, P., and Gao, R., 2017, “Automated Performance Tracking for Heat Exchangers in HVAC,” IEEE Trans. Autom. Sci. Eng., 14(2), pp. 634–645.
. Wang, P., and Gao, R., 2016, “Markov Nonlinear System Estimation for Engine Performance Tracking,” ASME J. Eng. Gas Turbines Power. 138(9), p. 091201.
. Yang, C., Li, Z., Liang, B., Lu, W., Wang, X., and Liu, H., 2017, “A Particle Filter and Long Short Term Memory Fusion Algorithm for Failure Prognostic of Proton Exchange Membrane Fuel Cells,” Proceedings of the Chinese Control Decision Conference, Chongqing, China, pp. 5646–5651.
. Rao PK, Liu J, Roberson D, Kong Z, Williams C. Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors. J Manuf Sci Eng. 2015. https://doi.org/10.1115/1.4029823.
. Tang Y, Dong G, Zhou Q, Zhao YF. Lattice structure design and optimization with additive manufacturing constraints. IEEE Trans Autom Sci Eng. 2018; 15:1546–62. https://doi.org/10.1109/TASE.2017.2685643.
. Munguía J, Ciurana J, Riba C. Neural-network-based model for build-time estimation in selective laser sintering. Proc Inst Mech Eng Part B J Eng Manuf. 2009; 223:995–1003. https://doi.org/10.1243/09544054JEM1324.
. Baturynska I, Semeniuta O, Wang K. Application of machine learning methods to improve dimensional accuracy in additive manufacturing. In: Wang K, Wang Y, Strandhagen JO, Yu T, editors. Advanced manufacturing and automation VIII 8. Singapore: Springer; 2019. p. 245–52.
. Chowdhury S, Mhapsekar K, Anand S. Part build orientation optimization and neural network-based geometry compensation for additive manufacturing process. J Manuf Sci Eng. 2017. https://doi.org/10.1115/1.4038293.
. Khanzadeh M, Rao P, Jafari-Marandi R, Smith BK, Tschopp MA, Bian L. Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused flament fabrication additive manufacturing parts. J Manuf Sci Eng. 2018; 140: 031011.
. Samie Tootooni M, Dsouza A, Donovan R, Rao PK, Kong Z, Borgesen P. Classifying the dimensional variation in additive manufactured parts from laser-scanned three-dimensional point cloud data using machine learning approaches. J Manuf Sci Eng. 2017; 139: 091005
. He K, Yang Z, Bai Y, Long J, Li C. Intelligent fault diagnosis of delta 3D printers using attitude sensors based on support vector machines. Sensors. 2018; 18:1298.
. Scime L, Beuth J. Using machine learning to identify in-situ melt pool signatures indicative of faw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf. 2019; 25:151–65.
. Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C, et al. A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences 2020; 10:559.
. Ovbiagbonhia AR, Kollöffel B, Den Brok P. Teaching for innovation competence in higher education Built Environment engineering classrooms: teachers’ beliefs and perceptions of the learning environment. European Journal of Engineering Education 2020; 45:917–36.
. Lee J, Davari H, Singh J, Pandhare V. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters 2018; 18:20–3.
. Sottilare R, VanLehn K. Intelligent tutoring systems swot analysis. Design Recommendations for Intelligent Tutoring Systems 2020.27.
. Li S, Guo J, Gao Y, Lou J, Yang D, Xiao Y, et al. How to manage a task-oriented virtual assistant software project: an experience report. Frontiers of Information Technology & Electronic Engineering 2022; 23:749–62.
. McCardle L, Young BW, Baker J. Self-regulated learning and expertise development in sport: Current status, challenges, and future opportunities. International Review of Sport and Exercise Psychology 2019; 12:112– 38.
. Zeba S, Haque MA, Alhazmi S, Haque S. Advanced Topics in Machine Learning. Machine Learning Methods for Engineering Application Development 2022:197.
. Basnet RB, Johnson C, Doleck T. Dropout prediction in Moocs using deep learning and machine learning. Education and Information Technologies 2022; 27:11499–513.
. Wang Y. When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale. Studies in Educational Evaluation 2021; 69:100872.
. Chen X, Zou D, Xie H, Cheng G, Liu C. Two decades of artificial intelligence in education. Educational Technology & Society 2022; 25:28–47.
. Renken V, Albinger S, Goch G, Neef A, Emmelmann C. Development of an adaptive, self-learning control concept for an additive manufacturing process. CIRP J Manuf Sci Technol. 2017; 19:57–61.
. Khanzadeh M, Tian W, Yadollahi A, Doude HR, Tschopp MA, Bian L. Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Addit Manuf. 2018; 23:443–56. 152. Delli U, Chang S. Automated process monitoring in 3D printing using supervised machine learning. Proc Manuf. 2018; 26:865–70.
. Uhlmann E, Pontes RP, Laghmouchi A, Bergmann A. Intelligent pattern recognition of a SLM machine process and sensor data. Proc Cirp. 2017; 62:464–9.
. Jafari-Marandi R, Khanzadeh M, Tian W, Smith B, Bian L. From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing. J Manuf Syst. 2019; 51:29–41.
. Khanzadeh M, Chowdhury S, Tschopp MA, Doude HR, Marufuzzaman M, Bian L. In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Transact. 2019; 51:437–55.
. Okaro IA, Jayasinghe S, Sutclife C, Black K, Paoletti P, Green PL. Automatic fault detection for selective laser melting using semi-supervised machine learning. 2018.
. Okaro IA, Jayasinghe S, Sutclife C, Black K, Paoletti P, Green PL. Automatic fault detection for laser powder-bed fusion using semisupervised machine learning. Addit Manuf. 2019; 27:42–53.
. Li Z, Zhang Z, Shi J, Wu D. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robot Comput Integr Manuf. 2019; 57:488–95.
. Jourdan N, Longard L, Biegel T, et al. Machine Learning for Intelligent Maintenance and Quality Control: A Review of Existing Datasets and Corresponding Use Cases. Hannover: publishing; 2021. doi: 10.15488/11280
. Panayotov V, Chen G, Povey D, et al. Librispeech: An ASR corpus based on public domain audio books. In: Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 5206-5210.
. Arinez JF, Chang Q, Gao RX, et al. Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook. Journal of Manufacturing Science and Engineering. 2020; 142(11). doi: 10.1115/1.4047855
. Wang L. From Intelligence Science to Intelligent Manufacturing. Engineering. 2019; 5(4): 615-618. doi: 10.1016/j.eng.2019.04.011
. Lee J, Davari H, Singh J, et al. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters. 2018; 18: 20-23. doi: 10.1016/j.mfglet.2018.09.002
. Shehab N, Badawy M, Arafat H. Big data analytics concepts, technologies challenges, and opportunities. In: Hassanien A, Shaalan K, Tolba M. (eds.) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. Advances in Intelligent Systems and Computing. Springer, Cham: Springer International Publishing; 2020. p. 92-101. Available from: doi: 10.1007/978-3-030-31129-2_9.
. Li W, Chai Y, Khan F, Jan SR, Verma S, Menon VG, et al. A comprehensive survey on machine learning-based big data analytics for IoT-enabled smart healthcare system. Mobile Networks and Applications. 2021; 26: 234-252.
. Bartoletti I. AI in healthcare: Ethical and privacy challenges. In: Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019. Poznan, Poland: Springer International Publishing; 2019. p. 7-10.
. Shehab N, Badawy M, Arafat H. Big data analytics and preprocessing. In: Hassanien AE, Darwish A. (eds.) Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Studies in Big Data. Vol. 77. Switzerland: Springer Cham; 2021. p. 25-43. Available from: doi: 10.1007/978-3-030-59338-4_2.
. Chui KT, Lytras MD, Visvizi A, Sarirete A. An overview of artificial intelligence and big data analytics for smart healthcare: requirements, applications, and challenges. Artificial Intelligence and Big Data Analytics for Smart Healthcare. 2021; 243-254.
. Rehman A, Naz S, Razzak I. Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems. 2022; 28(4): 1339-1371.
. Hassan S, Dhali M, Zaman F, Tanveer M. Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges. Heliyon. 2021; 7(6): e07179.
. Guidolin K, Catton J, Rubin B, Bell J, Marangos J, Munro-Heesters A, et al. Ethical decision making during a healthcare crisis: a resource allocation framework and tool. Journal of Medical Ethics. 2022; 48(8): 504-509.
. Khan ZF, Alotaibi SR. Applications of artificial intelligence and big data analytics in m-health: a healthcare system perspective. Journal of Healthcare Engineering. 2020; 2020: 1-5.
. Chauhan M. AI-Centric Smart City Ecosystems: Smart Healthcare Solutions for Smart Cities. Boca Raton: CRC Press; 2022. p. 247-260.
