Investigating the Role of Artificial Intelligence and Data Analytics in Hybrid Manufacturing

Abstract

Hybrid production technologies have made industrial operations more efficient, flexible and competitive with the use of automation, data analytics and artificial intelligence. It allows reducing production interruptions, improving product quality and optimizing production processes more effectively. The integrated use of artificial intelligence and data analytics in hybrid manufacturing, together with big data analysis, the Internet of things and robotic systems accelerates the transformation of industry 4.0 and greatly shapes the future potential. In addition to the role of hybrid manufacturing technologies and artificial intelligence in industrial applications, the future potential of these technologies is high. The future of hybrid manufacturing technologies will be shaped by the further integration of these two areas and their innovative applications. It will be a starting point to examine the details of this transformation in manufacturing and to understand the impact of artificial intelligence in industrial applications.

Keywords

• Artificial intelligence
• Data analytics
• Hybrid manufacturing
• Machine learning

HİBRİT İMALATTA YAPAY ZEKÂ VE VERİ ANALİTİĞİNİN ROLÜNÜN ARAŞTIRILMASI

Abstract

Hibrit üretim teknolojileri, otomasyon, veri analitiği ve yapay zekâ kullanımıyla endüstriyel operasyonları daha verimli, esnek ve rekabetçi hale getirmiştir. Üretimdeki kesintilerin azaltılması, ürün kalitesinin artırılması ve üretim süreçlerinin daha etkili bir şekilde optimize edilmesine olanak sağlar. Yapay zekâ ve veri analitiğinin hibrit imalata entegre kullanımı, büyük veri analizi, nesnelerin interneti ve robotik sistemlerle birlikte endüstri 4.0 dönüşümünü hızlandırır ve gelecekteki potansiyeli büyük ölçüde şekillendirir. Hibrit imalat teknolojilerinin ve yapay zekânın endüstriyel uygulamalardaki rolünün yanı sıra bu teknolojilerin gelecekteki potansiyeli de yüksektir. Hibrit imalat teknolojilerinin geleceği, bu iki alanın daha fazla entegrasyonu ve yenilikçi uygulamaları ile şekillenecektir. İmalattaki bu dönüşümün detaylarını incelemek ve endüstriyel uygulamalardaki yapay zekâ etkisini anlamak için bir başlangıç noktası olacaktır.

Keywords

• Yapay zekâ
• Veri analitiği
• Hibrit imalat
• Makine öğrenmesi

References

1. AB Proje Yönetimi, (2023). Proje Yönetim Okulu. Erişim Adresi: https://www.abprojeyonetimi.com/alan-turing-hakkinda-bilmediginiz-8-sey/, (Erişim Tarihi: 23.11.2023).
2. Acar, O. (2020). Yapay zekâ fırsat mı yoksa tehdit mi? Kriter Yayınevi, İstanbul.
3. AI & Machine Learning, Google Cloud Blog, (2021). Erişim Adresi: https://cloud.google.com/blog/products/ai-machine-learning/research-on-ai-trends-in-manufacturing, (Erişim Tarihi: 18.08.2024).
4. Amanov, F., & Pradeep, A. (2023, June). The Significance of Artificial Intelligence in the Second Scientific Revolution-A Review. In 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 01-05). IEEE. 10.1109/ECAI58194.2023.10194056
5. Avalle, G., De Pace, F., Fornaro, C., Manuri, F., Sanna, A. (2019). An augmented reality system to support fault visualization in industrial robotic tasks. Ieee Access, 7, 132343-132359. https://doi.org/10.1109/access.2019.2940887
6. Bartsch, K., Pettke, A., Hübert, A., Lakämper, J., Lange, F. (2021). On the digital twin application and the role of artificial intelligence in additive manufacturing: a systematic review. Journal of Physics: Materials, 4(3), 032005. https://doi.org/10.1088/2515-7639/abf3cf
7. Bhat, N. N., Kumari, K., Dutta, S., Pal, S. K., Pal, S. (2015). Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. Journal of Manufacturing Processes, 20, 274-281. https://doi.org/10.1016/j.jmapro.2015.07.002
8. Bilik, M., Aydın, Ü. (2018). Finansal hizmetlerde dijital dönüşüm ve etkileri. In Book Of Proceedıngs 3rd Internatıonal Congress On Economıcs, Fınance And Energy ISBN: 978-601-7805-32-6, 22. https://doi.org/10.58830/ozgur.pub147.c1234

9. Bragança, S., Costa, E., Castellucci, I., Arezes, P. M. (2019). A brief overview of the use of collaborative robots in industry 4.0: Human role and safety. Occupational And Environmental Safety And Health, 641-650. https://doi.org/10.1007/978-3-030-14730-3_68
10. BrainyQuote, (2021). Warren Bennis Quotes. Erişim Adresi: https://www.brainyquote.com/quotes/warren_bennis_402360; (Erişim Tarihi: 01.06.2024).
11. Chen, C., Wang, X., Wang, Y., Yang, D., Yao, F., Zhang, W., ... Hu, D. (2020). Additive manufacturing of piezoelectric materials. Advanced Functional Materials, 30(52), 2005141. https://doi.org/10.1002/adfm.202005141
12. Cañas, H., Mula, J., Campuzano-Bolarín, F., & Poler, R. (2022). A conceptual framework for smart production planning and control in Industry 4.0. Computers & Industrial Engineering, 173, 108659. https://doi.org/10.1016/j.cie.2022.108659
13. 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
14. Çakır, N.N. (2019). Endüstri 4.0 ve Çalışmanın Geleceği, Electronic Journal of Vocational Colleges, 8(2),97-105
15. Devagiri JS, Paheding S, Niyaz Q et al (2022) Augmented reality and artifcial intelligence in industry: trends, tools, and future challenges. Expert Systems with Applications, 207, 118002. https://doi.org/10.1016/j.eswa. 2022.118002
16. Dumitrache I, Caramihai SI, Moisescu MA, Sacala IS (2019) Neuro-inspired Framework for cognitive manufacturing control. IFAC-PapersOnLine, 52(13), 910–915. https://doi.org/10.1016/j.ifacol.2019.11.311
17. Egwim, C. N., Alaka, H., Egunjobi, O. O., Gomes, A., & Mporas, I. (2024). Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics. Journal of Engineering, Design and Technology, 22(4), 1325-1350. https://doi.org/10.1108/JEDT-05-2022-0238
18. Gao, X., You, D., Katayama, S. (2012). Infrared image recognition for seam tracking monitoring during fiber laser welding. Mechatronics, 22(4), 370-380. https://doi.org/10.1016/j.mechatronics.2011.09.005
19. G. Immerman,(2022). Production and Process Optimization in Manufacturing, MachineMetrics, 2022. Erişim Adresi: https://www.machine metrics.com/blog/process-optimization-manufacturing, (Erişim Tarihi: 01.04.2022).
20. Grzesik, W. (2018). Hybrid additive and subtractive manufacturing processes and systems: A review. Journal of Machine Engineering, 18(4), 5-24. https://doi.org/10.5604/01.3001.0012.7629
21. Grzesik, W. and Ruszaj, A. (2021) Hybrid Manufacturing Processes, Springer, 150- 166. https://doi.org/10.1007/978-3-030-77107-2
22. Güler, S., Serindağ, H. T., Gürel, Ç. A. M. (2022). Tel ark eklemeli imalat: son gelişmeler ve değerlendirmeler. Mühendis ve Makina, 63(706), 82-116. https://doi.org/10.46399/muhendismakina.995979
23. Gürsoy Ulusoy, Ş. (2024). Artificial Intelligence & Chatgpt For Communication Science. Sosyal Bilimler Dergisi/The Journal of Social Sciences. Doi: http://dx.doi.org/10.29228/SOBIDER.76342
24. Haiming Shen (2016) A study of welding robot path planning application based on Genetic Ant Colony Hybrid Algorithm. In: 2016 IEEE Advanced information management, communicates, electronic and automation control conference (IMCEC). IEEE, 1743–1746. https://doi.org/10.1109/imcec.2016.7867517
25. Heiden B, Alieksieiev V, Volk M, Tonino-Heiden B (2021) Framing artifcial intelligence (AI) additive manufacturing (AM). Procedia Comput Sci, 186:387–394. https://doi.org/10.1016/j.procs.2021.04.161
26. Hosseini E, Ghanbari PG, Keller F et al (2021) Deploying artifcial intelligence for component-scale multiphysical feld simulation of metal additive manufacturing. In: Meboldt M, Klahn C (eds) Industrializing additive manufacturing. Springer International Publishing, Cham, 268–276. https://doi.org/10.1007/978-3-030-54334-1_19
27. Iarovyi S, Lastra JLM, Haber R, Del Toro R (2015) From artifcial cognitive systems and open architectures to cognitive manufacturing systems. Proceeding: 2015 IEEE Int Conf Ind Informatics, INDIN 2015, 1225–1232. https://doi.org/10.1109/INDIN.2015.7281910
28. Jandric, Z., Labudovic, M., and Kovacevic, R., 2004. Effect of heat sink on microstructure of three-dimensional parts built by welding-based deposition. International Journal of Machine Tools Manufacture, 44 (7–8), 785–796. https://doi.org/10.1016/j.ijmachtools.2004.01.009
29. Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A., Guizani, M. (2021). The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions. IEEE Internet of Things Journal, 9(15), 12861-12885. https://doi.org/10.1109/jiot.2021.3139827
30. Kamath C, Fan YJ (2018) Regression with small data sets: a case study using code surrogates in additive manufacturing. Knowledge and Information Systems, 57:475–493. https://doi.org/10.1007/s10115-018-1174-1
31. Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1 (2), 99-107
32. Kaya, M. (2021). Sanayi 4.0’da yapay zekâ ve Türkiye. Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, 5(2), 63-94.
33. Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 1-19. https://doi.org/10.1007/ s12541-021-00600-3
34. Ko, H., Witherell, P., Lu, Y., Kim, S., & Rosen, D. W. (2021). Machine learning and knowledge graph based design rule construction for additive manufacturing. Additive Manufacturing, 37, 101620. https://doi.org/10.1016/j.addma.2020. 101620
35. Liu, H., Fang, T., Zhou, T., & Wang, L. (2018). Towards robust human-robot collaborative manufacturing: Multimodal fusion. IEEE Access, 6, 74762-74771. https://doi.org/10.1109/access.2018.2884793
36. Martins, H., & Puga, H. (2023). Ultrasonic assisted machining overview: Accessing feasibility and overcoming challenges for milling applications. Metals, 13(5), 908. https://doi.org/10.3390/met13050908
37. Matheson, E., Minto, R., Zampieri, E. G., Faccio, M., & Rosati, G. (2019). Human–robot collaboration in manufacturing applications: A review. Robotics, 8(4), 100. https://doi.org/10.3390/robotics8040100
38. McAndrew, A. R., Rosales, M. A., Colegrove, P. A., Hönnige, J. R., Ho, A., Fayolle, R., ... & Pinter, Z. (2018). Interpass rolling of Ti-6Al-4V wire+ arc additively manufactured features for microstructural refinement. Additive Manufacturing, 21, 340-349. https://doi.org/10.1016/j.addma.2018.03.006
39. Mikalef, P., Pappas, I. O., Krogstie, J., Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information systems and e-business management, 16, 547-578. https://doi.org/10.1007/s10257-017-0362-y
40. Milazzo M, Libonati F (2022) The synergistic role of additive manufacturing and artifcial intelligence for the design of new advanced intelligent systems. Advanced Intelligent Systems, 4(6),2100278. https://doi.org/10.1002/ aisy.202100278
41. Mypati, O., Mukherjee, A., Mishra, D., Pal, S. K., Chakrabarti, P. P., Pal, A. (2023). A critical review on applications of artificial intelligence in manufacturing. Artificial Intelligence Review, 56(Suppl 1), 661-768. https://doi.org/10.1007/s10462-023-10535-y
42. Nizam MSH, Marizan S, Zaki SA, Mohd Zamzuri AR (2016) Vision based identifcation and classifcation of weld defects in welding environments: A review. Indian journal of science and technology, 9:83–89. https://doi.org/10.17485/ijst/2016/v9i20/82779
43. Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N. (2019). Deep generative design: Integration of topology optimization and generative models. Journal of Mechanical Design, 141(11), 111405.https://doi.org/10.1115/1.4044229
44. OCI, (2024). Neden Büyük Veri? (2024) Erişim Adresi: https://www.oracle.com/tr/big-data/what-is-big-data/; (Erişim Tarihi: 18.08.2021)
45. Olu-lawal, K. A., Olajiga, O. K., Adeleke, A. K., Ani, E. C., & Montero, D. J. P. (2024). Innovative material processing techniques in precision manufacturing: a review. International Journal of Applied Research in Social Sciences, 6(3), 279-291. https://doi.org/10.51594/ijarss.v6i3.886
46. Omairi A, Ismail ZH (2021) Towards machine learning for error compensation in additive manufacturing. Applied Sciences, 11:1–27. https://doi.org/10.3390/app11052375
47. Ortt R, Stolwijk C, Punter M (2020) Implementing Industry 4.0: assessing the current state. Journal of Manufacturing Technology Management, 31:825–836. https://doi.org/10.1108/JMTM-07-2020-0284
48. Önel, S. (2014). Otomatik Kontrollü Elektrokimyasal İşlemenin (EKİ) İncelenmesi ve Uygulaması. Journal of the Faculty of Engineering Architecture of Gazi University/Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 29(1). https://doi.org/10.17341/gummfd.41007
49. Öztürk, K., Şahin, M. E. (2018). Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36.
50. Pal, K., Bhattacharya, S., Pal, S. K. (2011). Optimisation of weld deposition efficiency in pulsed MIG welding using hybrid neuro-based techniques. International Journal of Computer Integrated Manufacturing, 24(3), 198-210. https://doi.org/10. 1080/0951192X.2010.542181
51. Plathottam, S. J., Rzonca, A., Lakhnori, R., Iloeje, C. O. (2023). A review of artificial intelligence applications in manufacturing operations. Journal of Advanced Manufacturing and Processing, 5(3), e10159. https://doi.org/10.1002/amp2.10159
52. Peres RS, Jia X, Lee J et al (2020) Industrial artifcial intelligence in industry 4.0: systematic review, challenges and outlook. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3042874
53. Petruck, H., Nelles, J., Faber, M., Giese, H., Geibel, M., Mostert, S., ... Nitsch, V. (2020). Human-robot cooperation in manual assembly–interaction concepts for the future workplace. In Advances in Human Factors in Robots and Unmanned Systems: Proceedings of the AHFE 2019 International Conference on Human Factors in Robots and Unmanned Systems, July 24-28, 2019, Springer International Publishing. Washington DC, USA 10, 60-71. https://doi.org/10.1007/978-3-030-20467-9_6
54. Powell, J., Koti, D., Garmendia, X., & Voisey, K. T. (2023). Assessing the quality and productivity of laser cladding and direct energy deposition: guidelines for researchers. Journal of Laser Applications, 35(1). https://doi.org/10.2351/7.0000897
55. Ranjan, R., Khan, A. R., Parikh, C., Jain, R., Mahto, R. P., Pal, S., ... Chakravarty, D. (2016). Classification and identification of surface defects in friction stir welding: An image processin approach. Journal of Manufacturing Processes, 22, 237-253. https://doi.org/10. 1016/j.jmapro.2016.03.009
56. Rathee G, Ahmad F, Iqbal R, Mukherjee M (2021) Cognitive automation for smart decision-making in industrial internet of things. IEEE Transactions on Industrial Informatics, 17:2152–2159. https://doi.org/10.1109/TII. 2020.3013618
57. Rech, J., & Grzesik, W. (2023). New trends in hybrid finishing processes of metallic additively fabricated parts–a short review. Journal of Machine Engineering, 23(1). http;//doi.org/10.36897/jme/162284
58. Rzevski, G. (2024). Artificial intelligence in engineering: past, present and future. WIT Transactions on Information and Communication Technologies, 10. doi: 10.2495/AI950011
59. Scime L, Singh A, Paquit V (2022) A scalable digital platform for the use of digital twins in additive manufacturing. Manufacturing Letters, 31, 28–32. https://doi.org/10.1016/j.mfglet.2021.05.007
60. Sharma, S., & Dvivedi, A. (2023). Simultaneous electrochemical and electrodischarge machining process: an approach to sustainable manufacturing. Journal of Manufacturing Processes, 104, 123-137. https://doi.org/10.1016/j.jmapro.2023.09.009
61. Shrivastava, P.K. and Dubey, A.K. (2014) ‘‘Electrical discharge machining–based hybrid machining processes: A review’’, Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture,228(6), 799-825. https://doi.org/10.1177/0954405413508939
62. Silbernagel C, Aremu A, Ashcroft I (2020) Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing. Rapid Prototyping Journal, 26(4), 625–637. https://doi.org/10. 1108/RPJ-08-2019-0213
63. Smith, S., Schmitz, T., Feldhausen, T., & Sealy, M. (2024). Hybrid metal additive/subtractive machine tools and applications. CIRP Annals. https://doi.org/10.1016/j.cirp.2024.05.002
64. Singh, V., Sharma, A. K., Goyal, A., Kumar Saxena, K., Negi, P., & Rao, P. C. S. (2024). Electric discharge machining performance measures and optimisation: A review. Advances in Materials and Processing Technologies, 10(2), 517-530. https://doi.org/10.1080/2374068X.2023.2168775
65. Singh, M., Singh, S., & Kumar, S. (2020). Investigating the impact of LASER assistance on the accuracy of micro-holes generated in carbon fiber reinforced polymer composite by electrochemical discharge machining. Journal of Manufacturing Processes, 60, 586-595. https://doi.org/10.1016/j.jmapro.2020.10.056
66. StorageCraft, (2021). File Storage Cost, By The Numbers, StorageCraft Technology, LLC, 2016. Erişim Adresi: https://blog.storagecraft.com/filestorage-cost-statistics/, (Erişim Tarihi: 01.04.2024).
67. Sun H, Ma L (2020) Generative design by using exploration approaches of reinforcement learning in density-based structural topology optimization. Designs, 4(2), 10. https://doi.org/10.3390/designs4020010
68. Sun, M., & Toyserkani, E. (2024). A Novel Hybrid Ultrasound Abrasive-Driven Electrochemical Surface Finishing Technique for Additively Manufactured Ti6Al4V Parts. Inventions, 9(2), 45. https://doi.org/10.3390/inventions9020045
69. Tezel, T., Topal, E. S., Kovan, V. (2018). Hibrit İmalat: Eklemeli İmalat ile Talaşlı İmalat Yöntemlerinin Birlikte Kullanılabilirliğinin İncelenmesi. International Journal of 3D Printing Technologies and Digital Industry, 2(3), 60-65.
70. Tufail, M. S., Giri, J., Makki, E., Sathish, T., Chadge, R., & Sunheriya, N. (2024). Machinability of different cutting tool materials for electric discharge machining: A review and future prospects. AIP Advances, 14(4). https://doi.org/10.1063/5.0201614
71. Vassakis, K., Petrakis, E., Kopanakis, I. (2018). Big data analytics: applications, prospects and challenges. Mobile big data: A roadmap from models to technologies, 3-20. https://doi.org/10.1007/978-3-319-67925-9_1
72. Wang, D., & Snooks, R. (2021). Artificial intuitions of generative design: an approach based on reinforcement learning. In Proceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020). Springer Singapore, 189-198. https://doi.org/10.1007/978-981-33-4400-6_18
73. Wang, X., Xue, L., Yan, Y., & Gu, X. (2017). Welding robot collision-free path optimization. Applied Sciences, 7(2), 89.https://doi.org/10.3390/app7020089
74. Woo, W. S., Kim, E. J., Jeong, H. I., & Lee, C. M. (2020). Laser-assisted machining of Ti-6Al-4V fabricated by DED additive manufacturing. International Journal of Precision Engineering and Manufacturing-Green Technology, 7, 559-572.
75. Wu, B., Pan, Z., Ding, D., Cuiuri, D., Li, H., Xu, J., & Norrish, J. (2018). A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. Journal of manufacturing processes, 35, 127-139.https://doi.org/10.1016/j.jmapro.2018.08.001
76. Wu M, Zhou H, Lin LL et al (2017) Detecting attacks in cybermanufacturing systems: additive manufacturing example. In MATEC Web of Conferences, 108:8–11. https://doi.org/10.1051/matecconf/201710806005
77. Xia, C., Pan, Z., Zhang, S., Li, H., Xu, Y., & Chen, S. (2020). Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing. The International Journal of Advanced Manufacturing Technology, 110, 2131-2142.https://doi.org/10.1007/s00170-020-05998-0
78. Yadav, D., Chhabra, D., Gupta, R. K., Phogat, A., & Ahlawat, A. (2020). Modeling and analysis of significant process parameters of FDM 3D printer using ANFIS. Materials Today: Proceedings, 21, 1592-1604. https://doi.org/10.1016/j.matpr.2019.11.227
79. Yao, B., Imani, F., Sakpal, A. S., Reutzel, E. W., & Yang, H. (2018). Multifractal analysis of image profiles for the characterization and detection of defects in additive manufacturing. Journal of Manufacturing Science and Engineering, 140(3), 031014. https://doi.org/10.1115/1.4037891
80. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616-630. https://doi.org/10.1016/j.eng.2017.05.015
81. Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., & Meng, L. (2018). Toward new-generation intelligent manufacturing. Engineering, 4(1), 11-20. https://doi.org/10.1016/j.eng.2018.01.002
82. Zhu, Z., Dhokia, V. G., Nassehi, A., Newman, S. T. (2013). A review of hybrid manufacturing processes–state of the art and future perspectives. International Journal of Computer Integrated Manufacturing, 26(7), 596-615. https://doi.org/10.1080/0951192x.2012.749530