Industry 4.0- Applications of machine learning in the field of industrial engineering: Systematic review of the literature
الموضوعات :MARCO ANTONIO DIAZ MARTINEZ 1 , REINA VERONICA ROMAN SALINAS 2 , SANTOS RUIZ HERNANDEZ 3 , GABRIELA CERVANTES ZUBIRIAS 4 , MARIO ALBERTO MORALES RODRIGUEZ 5
1 - TecNM- Higher Technological Institute of Pánuco (ITSP)
2 - TecNM- Higher Technological Institute of Pánuco (ITSP)
3 - TecNM- Higher Technological Institute of Pánuco (ITSP)
4 - Multidisciplinary Academic Unit, Reynosa-Aztlan-UAT
5 - Multidisciplinary Academic Unit, Reynosa-Aztlan-UAT
الکلمات المفتاحية: Machine Learning, Industry 4.0, Supply Chain, maintenance, artificial intelligence, Deep Learning, Additive Manufacturing,
ملخص المقالة :
The aim of this research is to determine how the implementation of machine learning has generated advantages in the field of engineering. Through a systematic review of the literature, it seeks to understand the importance of machine learning and its various applications in engineering, such as equipment maintenance, business demand forecasting, production chain optimization, customer service, and quality control. In this article, we conduct a systematic review and bibliometric analysis to explore the current state of research on machine learning and Industry 4.0 applications in the field of industrial engineering. Our goal is to identify established and emerging fields of research to guide future research. To carry out this study, we initially identified 639 scientific journal publications indexed by publishers such as Ebsco essentials, ScienceDirect, IEEEXplore, and MDPI, collected from 1 January of 2015 to May 2023. Subsequently, a group of specialists evaluated these publications, carefully selecting 65 of them that were placed in the literature review section and that were considered relevant to our analysis. In a second stage, we applied a detailed analysis using MAXQDA v.2020 software on our collected data, focusing on citation and keyword evaluation. This approach allowed us to gain a deeper understanding of trends and connections in existing research in this field.
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., Alghamdi, A., & Alshamrani, S. (2022). Energetics systems and artificial intelligence: Applications of industry 4.0. Energy reports, 8, 334-361. https://doi.org/10.1016/j.egyr.2021.11.256
Akande, O., Li, F., & Reiter, J. (2017). An empirical comparison of multiple imputation methods of categorical data. The American Statistician, 71 (2), 162-170. https://doi.org/10.1080/00031305.2016.1277158
Ali, N., Ghazal, T., Ahmed, A.., Abbas, S., Khan, M., Alzoubi, M., Farooq, U., Ahmad, M., & Khan, M. (2022). Fusion-based supply chain collaboration using machine learning techniques. Tech Science Press, 31, 1-18. https://doi.org/10.32604/iasc.2022.019892
Altmann, M., Benthien, T., Ellendt, N., & Toenjes, A. (2023). Defect classification for additive manufacturing with machine learning. Materials, 16 (18), 1-16. https://doi.org/10.3390/ma16186242
Alzahrani, A., & Asghar, M. (2023) Intelligent risk prediction system in IoT-based supply chain management in logistics sector. Electronics, 12 (13), 1-24. https://doi.org/10.3390/electronics12132760
Amin, R., Saeed, N., Masood, M., Moradi, Y., & Slim, M., Al, T. (2021) Deep learning in the industrial internet of things: potentials, challenges, and emerging applications. IEEEXplore, 10, 1-29. https://doi.org/10.1109/JIOT.2021.3051414
Anastasi, S., Madonna, M., & Monica, L. (2021). Implications of embedded artificial intelligence-machine learning on safety of machinery. Procedia compute science, 180, 338-343. https://doi.org/10.1016/j.procs.2021.01.171
Arifovic, J., Zhong, X., & Wei, L. (2022). Machine learning and speed in high frequency trading. Journal of economic dynamics and control. 139. https://doi.org/10.1016/j.jedc.2022.104438
Awd, M., Münstermann, S., & Walther, F. (2022). Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning. Fatigue & fracture of engineering materials & structure, 1, 1-25. https://doi.org/10.1111/ffe.13816
Barzizza, E., Biasetton, N., Ceccato, R., & Salmaso, L. (2023). Big data analytics and machine learning in supply chain 4.0: a literature review. Stats, 6, 1-22. https://doi.org/10.3390/stats6020038
Batu, T., Lemu, H., & Shimels, H. (2023). Application of artificial intelligence for surface roughness prediction of additively manufactured components. Materials, 16 (18), 1-34. https://doi.org/10.3390/ma16186266
Berg, T., Burg, V., Gombović., & Puri, M. (2023). On the rise of findtechs: Credit scoring using digital footprints. The review of financial studies, 33 (7), 2845–2897. https://doi.org/10.1093/rfs/hhz099
Biamonte, J; Wittek, P; Pancotti, N; Rebentrost, P; Wiebe, & N; Lloyd, S. (2017). Quantum machine learning. Nature, 549, 195-202. https://doi.org/10.1038/nature23474
Bridgeball, R. (2023). Unlocking drone potential in the pharma supply chain: A hybrid machine learning and GIS approach. Stand, 3, 283-296. https://doi.org/10.3390/standards3030021
Carpanzo, E., & Knüttel, D. (2022). Advances in artificial intelligence methods applications in industrial control systems: Toward cognitive self-optimizing manufacturing systems. Applied sciences, 12, 1-20. https://doi.org/10.3390/app122110962
Chopra, R., & Deep, G. (2021). Application of artificial intelligence in stock market forecasting: A critique, review, and research agenda. Journal of risk and financial management, 14 (11), 1–34. https://doi.org/10.3390/jrfm14110526
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C., Choudhary, A., Agrawal, A., Billinge, S., Holm, E., Ong, S., & Wolverton, C. (2022). Recent advances and applications of deep learning methods in material science. Npj computational materials, 8, 1-26. https://doi.org/10.1038/s41524-022-00734-6
Chui, M., Manyika, J., & Miremadi, M. (2016). Where Machines Could Replace Humans—And Where They Can’t (Yet). McKinsey.
Chung, H., Pong, R., Fai, K., & Lam, K. (2008). Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on information systems, 26 (3), 1-37. https://doi.org/10.1145/1361684.1361686
Constantin, C., Zichil, V., Andrei, V., & Marius, S. (2024). Machine learning mechatronics, and stretch forming: A history of innovation in manufacturing engineering. Machines, 12 (3), 1-36. https://doi.org/10.3390/machines12030180
Constantin, C., Zichil, V., Andrei, V., & Marius, S. (2024). Machine learning, mechatronics, and stretch forming: A History of innovation in manufacturing engineering. Machines. 12 (3), 1-36. https://doi.org/10.3390/machines12030180
Dong, Z., Wu, L., Wang, L.., Li, W., Wang, Z., & Liu, Z. (2022). Optimization of fracturing parameters with machine-learning and evolutionary algorithm methods. Energies, 15, 1-23. https://doi.org/10.3390/en15166063
Drakaki, M., Karnavas, Y., Tziaffetas, I., Linardos, V., & Tzionas, P. (2022). Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: A state of art survey. Journal of industrial engineering and management, 15 (1), 31-57. https://doi.org/10.3926/jiem.3597
Duong, N., Born, S., Woo, J., Schermeyer, M., Paulick, K., Borisyak, M., Cruz, M., Werner, T., Scholz, R., Schmidt, L., Neubauer, P., & Martínez, E. (2023). When bioprocess engineering meets machine learning: A survey from perspective of automated bioprocess development. Biochemical engineering journal, 190, 1-85. https://doi.org/10.1016/j.bej.2022.108764
Egan, P. (2023). Design for additive manufacturing: Recent innovations and future directions. Designs, 7, 1-33. https://doi.org/10.3390/designs7040083
Ekramirad, N., Doyle, L., Loeb, J., Santra, D., & Adedeji, A. (2024). Hyperspectral imaging and machine learning as a nondestructive method for Proso millet seed detection and classification. Foods. 13 (9), 1-11. https://doi.org/10.3390/foods13091330
El, M., & Hammoud, J. (2023). Unveiling the influence of artificial intelligence and machine learning on financial markets: A comprehensive analysis of AI applications in trading, risks management, and financial operations. Journal of risk and financial management, 16 (10), 1–16. https://doi.org/10.3390/jrfm16100434
Engelmann, B., Schmitt, S., Miller, E., Braütigam, V., & Schmitt, J. (2020). Advances in machine learning detecting changeover processes in cyber physical production systems. Journal of manufacturing and materials processing, 4 (4), 1-20. https://doi.org/10.3390/jmmp4040108
Esoso, A., Ikumapayi, M., Jen, T; & Akinlabi, E. (2023). Exploring machine learning tools for enhancing additive manufacturing: A comparative. International information and engineering technology association, 28, 535-544. https://doi.org/10.18280/isi.280301
Failing, J., Abellán, J., Benavent, S., Rosado, P., & Romero, F. (2023). A tool condition monitoring system based on low-cost sensors and an IoT platform for rapid deployment. processes, 11 (3), 1-18. https://doi.org/10.3390/pr11030668
Falkner, D., Bögl, M., Gattinger, A., Stainko, R., Zenisek, J., & Affenzeller, M. (2024). Integrating machine learning into supply chain management: Challenges and opportunities. Procedia computer sciences. 232, 1779-1788. https://doi.org/10.1016/j.procs.2024.01.176
Ghasemkhani, B., Yilmaz, R., Birant, D., & Alp, R. (2022). Machine learning models for the prediction of energy consumption based on cooling and heating loads in internet of things based smart buildings. symmetry. 14 (8), 1-24. https://doi.org/10.3390/sym14081553
Gibson, I., Rosen, D., & Stucker, B. (2015). Additive manufacturing technologies- 3D printing, rapid prototyping, and direct digital manufacturing. Springer, 1-510. https://doi.org/10.1007/978-1-4939-2113-3
Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services. Journal of Management Information Systems, 35(1), 220–265. https://doi.org/10.1080/07421222.2018.1440766
Guo, Z., Wang, H., Kong, X., Shen, L., & Jia, Y. (2021). Machine learning-based production prediction model and its application in duvernay formation. Energies, 14, 1-18. https://doi.org/10.3390/en14175509
Haddaway, N., Page, M., Pritchard, C., & McGuiness, L. (2020). An R package and shiny app for producing PRISMA 2020-compliant flow diagrams with interactivity for optimized digital transparency and open synthesis. Campbell collaboration Wiley, 18, 1-12. https://doi.org/10.1002/cl2.1230
Heng, S., Tariq, U., Joy, R., Sparks, T., Flood, A., & Liou, F. (2024). Experimental, computational, and machine learning methods for prediction of residual stresses in laser additive manufacturing: A critical review. materials. 17 (7), 1-26. https://doi.org/10.3390/ma17071498
Hermann, M., Pentek, T., & Otto, B. (2015). Design principles for industrie 4.0 scenarios. IEEEXplore, 1, 1-16. https://doi.org/10.1109/HICSS.2016.488
Hürkamp, A., Gellrich, S., Ossowski, T., Beuscher, J., Thiede, S., Herrman, & C., Dröder. (2020). Combining simulation and machine learning as digital twin for the manufacturing of overmolded thermoplastic composites. Journal of manufacturing and materials processing, 4 (3), 1-21. https://doi.org/10.3390/jmmp4030092
Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021). Industry 4.0 technologies for manufacturing sustainability: a systematic review and future research directions. Applied sciences, 11, 1-27. https://doi.org/10.3390/app11125725
Kang, K., Chen, Q., Wang, K., Zhang, Y., Zhang, D., Zheng, G., Xing, J., Long, T., Ren, X., Shang, C., & Cui, B. (2023). Application of interpretable machine learning for production feasibility prediction of gold mine project. Applied sciences, 13, 1-17. https://doi.org/10.3390/app13158992
Kannisto, P., Hästbacka, D., Gutiérrez, T., Suominen, O., Vilkko, M, & Craamer, P. (2021). Plant-wide interoperability and decoupled, data-driven process control with message bus communication. Journal of industrial information integration. 26, 1-15. https://doi.org/10.1016/j.jii.2021.100253
Khchine, R., Amar, A., Guennoun, Z., Bensouda, C., & Laaroussi, Y. (2018). Machine learning for supply chain´s big data: State of the art and application to social networks´ data. Matec web conf, 200, 1-7. https://doi.org/10.1051/matecconf/201820000015
Khorasani, A., Gibson, I., Veetil, J., & Ghasemi, A. (2020). A review of technological improvements in laser-based powder bed fusion of metal printers. The international journal of advanced manufacturing technology, 1-19. https://doi.org/10.1007/s00170-020-05361-3
Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H., & Kim, N. (2019). Deep learning in medical imaging. Neurospine, 16, 657-668. https://doi.org/10.14245/ns.1938396.198
Kim, S., Seo, J., & Kim, S. (2024). Machine learning technologies in the supply chain management research of biodiesel: A review. Energies. 17 (6), 1-28. https://doi.org/10.3390/en17061316
Kouicem, D., Bouabdallah, A., & Lakhlef, H. (2018). Internet of things security: A top-down survey. Computer networks, 1-24. https://doi.org/10.1016/j.comnet.2018.03.012
Kumar, R. (2023). Improving quality of predictive maintenance through machine learning algorithms industry 4.0 environment. Proceedings on engineering sciences, 5 (1), 1-10. https://doi.org/10.24874/PES05.01.006
Lee, C., & Lim, C. (2021). From technological development to social advance: A review of industry 4.0 through machine learning. Technological forecasting and social change, 167, 1-12. https://doi.org/10.1016/j.techfore.2021.120653
Lei, Y; Qiaoming, H; & Tong, Z. (2023). Research on supply chain financial risk prevention based on machine learning. Computational intelligence and neuroscience, 1-15. https://doi.org/10.1155/2023/6531154
Li, L; Prato, C; & Wang, Y. (2020). Ranking contributors to traffic crashes on mountains freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier. Accident analysis & prevention, 146, 1-11. https://doi.org/10.1016/j.aap.2020.105744
Liebsch, A., Koshulow, W., Gabauer, J., Kupfer, R., & Gude, M. (2019). Overmoulding of consolidated fibre-reinforced thermoplastics-increasing the bonding strength by physical surface pre-treatments. Procedia CIRP, 85, 212-217. https://doi.org/10.1016/j.procir.2019.09.047
López, R., Panizo, L., & Del Mar, M. (2024). Flextory: Flexible software factory of IoT data consumers. Sensors. 24 (8), 1-25. https://doi.org/10.3390/s24082550
Lu, W., & Yan, X. (2021). Variable-weighted FDA combined with t-SNE and multiple extreme learning machines for visual industrial process monitoring, ISA transactions 163-171. https://doi.org/10.1016/j.isatra.2021.04.030
Mad, M., Mohd, M., Bin, F., & Bin, E. (2021). An unsupervised machine learning-based framework for transferring local factories into supply chain networks. mathematics. 9 (23), 1-31. https://doi.org/10.3390/math9233114
Mazzei, D., & Ramjattan, R. (2022). Machine learning for industry 4.0: A systematic review using deep learning-based topic modelling. Sensors, 22, 1-26. https://doi.org/10.3390/s22228641
Mishara, S. (2022). Blockchain and machine learning-based hybrid IDS to protect smart networks and preserve privacy. electronics, 12 (16), 1-23. https://doi.org/10.3390/electronics12163524
Mo, H., Morin, D., Magne, K., He, J., & Zhang, Z. (2024). Transferability of temperature evolution of dissimilar wire-arc additively manufactured components by machine learning. Materials. 17 (3), 1-28. https://doi.org/10.3390/ma17030742
Mohseni, S., Wang, H., Xiao, C., Yu, Z., Wang, Z., & Yadawa, J. (2022). Taxonomy of machine learning safety: A survey and primer. ACM Digital Library, 55, 1-38. https://doi.org/10.1145/3551385
Ngo, T., Kashani, A., Imbalzano, G., Nguyen, K., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites part B: Engineering, 143, 172-196. https://doi.org/10.1016/j.compositesb.2018.02.012
Otoum, Y., Liu, D., & Nayak, A. (2021). DL-IDS: A Deep learning-based intrusion detection framework for securing IoT. Trans. Emerg. Emerging telecommunications technologies, 33 (3), 1-17. https://doi.org/10.1002/ett.3803
Paré, G., Trudel, M., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52 (2), 183-199. https://doi.org/10.1016/j.im.2014.08.008
Patil, D., Nigam, A., Mohapatra, S., & Nikam, S. (2023). A deep learning approach to classify and detect defects in the components manufactured by laser directed energy deposition process. Machines, 11, 1-19. https://doi.org/10.3390/machines11090854
Pelzer, L., Schulze, T., Buschmann, D., Enslin, C., Schmitt, R., & Hopmann, C. (2023). Acquiring process knowledge in extrusion-based additive manufacturing via interpretable machine learning. Polymers, 15, 1-18. https://doi.org/10.3390/polym15173509
Pereira, I., Madureira, A., Bettencourt, N., Coelho, D., Ângelo, M., Araújo, C., & Alves, D. (2024). A machine learning as a servisse (MLaaS) Approach to Improve marketing success. Informatics. 11 (2), 1-34. https://doi.org/10.3390/informatics11020019
Pereira, T., Kennedy, J., & Potgieter, J. (2019). A comparison of traditional manufacturing vs additive manufacturing, the best method for the job. Procedia manufacturing, 30, 11-18. https://doi.org/10.1016/j.promfg.2019.02.003
Rathnayake, R., Maduranga, M., Tilwari, V., & Dissanayake, M. (2023). RSSI and machine learning-based indoor localization systems for smart cities. Eng, 4 (2), 1468-1494. https://doi.org/10.3390/eng4020085
Roh, Y., Heo, G., Whang, S., & Whang, S. (2019). A survey on data collection for machine learning: A big data – AI integration perspective. IEEEXplore, 33, 1328-1347. https://doi.org/10.1109/TKDE.2019.2946162
Rojek, I., Jasiulewicz, M., Piechowski, M., & Mikolajewski, D. (2023). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Applied sciences, 33 (4), 1-17. https://doi.org/10.3390/app13084971
Romero, J., Macgluf, A., Rodríguez, A., Espinoza, J., & Suárez, A. (2021). Aplicación de Machine learning en la industria 4.0 en tiempos de pandemia. Intercontectando saberes, 11 (6), 1-7. https://doi.org/10.25009/is.v0i11.2692
Sanaei, N., & Fatemi, A. (2020). Defects in additive manufactured metals and their effect on fatigue performance: A State of the art review. Progress in materials science, https://doi.org/10.1016/j.pmatsci.2020.100724
Sani, S., Xia, H., Syed, J., & Salonitis, K. (2023). Supply chain 4.0: A machine learning-based Bayesian-optimized lightGBM model for predicting supply chain risk. Machines , 11 (9), 1-20. https://doi.org/10.3390/machines11090888
Sazzadur, M., Tapotosh, G., Ferdous, N., Shamim, M., Anannya, M., & Sanwar, A. (2023). Machine learning and internet of things in industry 4.0: A review. Measurement: sensors, 28, 1-19. https://doi.org/10.1016/j.measen.2023.100822
Schlagenhauf, T., & Burghard, N. (2021). Intelligent vision based wear forecasting on surfaces of machine tool elements. Sn. Applied sciences, 3 (858), 1-13. https://doi.org/10.1007/s42452-021-04839-3
Schlagenhauf, T., Jan, W., & Puchta, A. (2022). Convolutional-based encoder-decoder network for time series anomaly detection during the milling of 16MnCr15. data, 7 (12), 1-8. https://doi.org/10.3390/data7120175
Schroeder, M., & Lodeman, S. (2021). A systematic investigation of the integration of machine learning into supply chain risk management. Logistics, 5, 1-16. https://doi.org/10.3390/logistics5030062
Sharma, R., Raj, A., Mukherjee, M., Krishna, S., Kumar, R., & Kumar, A. (2024). Forecasting of process parameters using machine learning techniques for wire arc additive manufacturing process. Materialstoday: proceedings. 80 (1), 248-253. https://doi.org/10.1016/j.matpr.2022.12.081
Smith, D., & Simpson, K. (2020). The safety critical systems handbook. Elsevier publishing, UK. https://doi.org/10.1016/C2019-0-00966-1
Song, K. (2024). Compact data learning for machine learning classifications. axions. 13 (3), 1-16. https://doi.org/10.3390/axioms13030137
Tancredi, G., Vignali, G., & Bottani, E. (2022). Integration of digital twin, machine-learning and industry 4.0 tools for anomaly detection: An application to a food plant. Sensors, 22, 1-23. https://doi.org/10.3390/s22114143
Tariq, N., Asim, M., Obeidat, F., Farooqi, M., Baker, T., Hammoudeh, M., & Ghafir, I. (2019). The security of big data in fog-enable IoT applications including blockchain: A survey. Sensors, 19, 1-33. https://doi.org/10.3390/s19081788
Tien, D., Anh, T., Klempe, H., Pradhan, B., & Revhaug, I. (2015). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13, 361-378. https://doi.org/10.1007/s10346-015-0557-6
Togo, H., Asanuma, K., Nishi, T., & Liu, Z. (2022). Machine learning and inverse optimization for estimation of weighting factors in multi-objective production scheduling problems. Applied. Sciences, 12, 1-18. https://doi.org/10.3390/app12199472
Togo, H., Asanuma, K., Nishi, T., & Liu, Z. (2022). Machine learning and inverse optimization for estimation of weighting factors in multi-objective production scheduling problems. Applied sciences. 12 (19), 1-18. https://doi.org/10.3390/app12199472
Tridello, A., Ciampaglia, A., Berto, F., & Paolino, D. (2023). Assessment of the critical defect in additive manufacturing components through machine learning algorithms. Applied sciences, 12 (19), 1-22. https://doi.org/10.3390/app13074294
Vaccari, M., di Capaci, R.B., Brunazzi, E., Tognotti, L., Pierno, P., Vagheggi, R., & Pannocchia, G. (2020). Implementation of an Industry 4.0 system to optimally manage chemical plant operation. IFAC-PapersOnLine. 53 (2), 11545-11550. https://doi.org/10.1016/j.ifacol.2020.12.631
Velásquez, D., Pérez, E., Oregui, X., Artetxe, A., Manteca, J., Mansilla, J., Toro, M., Maiza, M., & Sierra, B. (2022). A Hybrid machine-learning ensemble for anomaly detection in real-time industry 4.0 systems. IEEEXplore, 10, 72024-72036. https://doi.org/10.1109/ACCESS.2022.3188102
Wei, Z., Liu, H., Tao, X., Pan, K., Huang, R., Ji, W., & Wang, J. (2023). Insights into the application of machine learning in industrial risk assessment: A bibliometric mapping analysis. Sustainability, 15 (8), 1-30. https://doi.org/10.3390/su15086965
Wiemer, H., Dementyev, A., & Ihlenfeldt, S. (2021). A holistic quality assurance approach for machine learning applications in cyber-physical production systems. Applied sciences, 11 (20), 1-20. https://doi.org/10.3390/app11209590
Wong, S., Woon, J., Yip, Y., & Kawasaki, T. (2023). A case study of how Maersk adopts cloud-based blockchain integrated with machine learning for sustainable practices. Sustainability, 15, 1-18. https://doi.org/10.32604/su15097305
Wong, S., Yeung, J., Lau, Y., & So, J. (2021). Technical sustainability of cloud-based blockchain integrated with machine learning for supply chain management. Sustainability, 13 (15), 1-21. https://doi.org/10.3390/su13158270
Wuest, T., Weimer, D., Irgens, C., & Dieter, K. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production and manufacturing research, 4, 1-24. https://doi.org/10.1080/21693277.2016.1192517
Xia, Y., Xu, T., Wei, M., Wei, Z., & Tang, L. (2023). Predicting chain´s manufacturing SME credit risk in supply chain finance based on machine learning methods. Sustainability, 15 (2), 1-18. https://doi.org/10.3390/su15021087
Xu, A., Darbandi, M., Javaheri, D., Navimipour, N., & Yalcin, S. (2023). The management of IoT- based organizational and industrial digitalization using machine learning methods. Sustainability, 15 (7), 1-28. https://doi.org/10.3390/su15075932
Xu, J., Tao, Y., & Lin, H. (2016). Semantic word cloud Generation based on word embeddings. IEEE Pacific visualization symposium (PacificVis), 239-243. https://doi.org/10.1109/PACIFICVIS.2016.7465278
Yoon, S., Cantwell, W.J., Yeun, C., Cho, C., Byon, Y., Kim, T. (2023). Defect detection in composites by deep learning using highly nonlinear solitary waves. Int. J. Mech. Sci. https://doi.org/10.1016/j.ijmecsci.2022.107882
Zhang, H., Bai, X., Dong, H., & Zhang, H. (2024). Modelling and prediction of process parameters with low energy consumption in wire arc additive manufacturing based on machine learning. metals. https://doi.org/10.3390/met14050567
Zhang, Y., Soon, H., Ye, D., Fuh, J., & Zhu, K. (2019). Powder-bed fusion process monitoring by machine vision with hybrid convolutional neural networks. IEEEXplore, 16 (9), 5769-5779. https://doi.org/10.1109/TII.2019.2956078