HİBRİT İMALATTA YAPAY ZEKÂ VE VERİ ANALİTİĞİNİN ROLÜNÜN ARAŞTIRILMASI

Year 2024, Volume: 29 Issue: 3, 949 - 968, 24.12.2024

Büşra Çerçer , Şeref Öcalır

https://doi.org/10.17482/uumfd.1486513

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

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

References

• AB Proje Yönetimi, (2023). Proje Yönetim Okulu. Erişim Adresi: https://www.abprojeyonetimi.com/alan-turing-hakkinda-bilmediginiz-8-sey/, (Erişim Tarihi: 23.11.2023).
• Acar, O. (2020). Yapay zekâ fırsat mı yoksa tehdit mi? Kriter Yayınevi, İstanbul.
• 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).
• 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
• 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
• 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
• 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
• 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
• 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
• BrainyQuote, (2021). Warren Bennis Quotes. Erişim Adresi: https://www.brainyquote.com/quotes/warren_bennis_402360; (Erişim Tarihi: 01.06.2024).
• 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
• 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
• 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
• Çakır, N.N. (2019). Endüstri 4.0 ve Çalışmanın Geleceği, Electronic Journal of Vocational Colleges, 8(2),97-105
• 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
• 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
• 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
• 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
• 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).
• 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
• Grzesik, W. and Ruszaj, A. (2021) Hybrid Manufacturing Processes, Springer, 150- 166. https://doi.org/10.1007/978-3-030-77107-2
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1 (2), 99-107
• Kaya, M. (2021). Sanayi 4.0’da yapay zekâ ve Türkiye. Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, 5(2), 63-94.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• OCI, (2024). Neden Büyük Veri? (2024) Erişim Adresi: https://www.oracle.com/tr/big-data/what-is-big-data/; (Erişim Tarihi: 18.08.2021)
• 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
• 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
• 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
• 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
• Öztürk, K., Şahin, M. E. (2018). Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Rzevski, G. (2024). Artificial intelligence in engineering: past, present and future. WIT Transactions on Information and Communication Technologies, 10. doi: 10.2495/AI950011
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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).
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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