A Theoretical Evaluation of Smart Production Systems and Industrial Robots within the Context of Industry 4.0

Öz

This study comprehensively addresses the role of intelligent manufacturing systems, industrial robots, and dark factories in production processes and the transformation process brought about by Industry 4.0. Today, manufacturers are pressured to produce higher-quality products with shorter lead times. This requirement has necessitated that production systems become integrated, automated, and intelligent. Smart manufacturing creates flexible, responsive and highly efficient production environments using artificial intelligence (AI), internet of things, big data analytics and robotics. Industrial robots have become systems that can not only act on a task basis but also learn, predict, and actively participate in production processes by interacting with humans. In this direction, dark factories are facilities that can produce 24 hours a day without human intervention and offer significant advantages regarding occupational safety, quality and efficiency. However, there are difficulties in the widespread use of these systems, such as high investment costs, applicability limitations in some sectors and the decreasing need for unskilled labour. In conclusion, for the technological opportunities offered by Industry 4.0 to provide sustainable and inclusive benefits, investments incompetent human resources should be increased, education policies should be restructured, and strategic public support should be activated.

Anahtar Kelimeler

• Industrial robots
• intelligent production systems
• artificial intelligence

Etik Beyan

It is declared that the study was designed to realistically and ethically meet the needs, and that integrity was maintained in obtaining data, concluding the study, and publishing the results. Ethical committee approval was not required for this research. No research requiring ethics committee approval was conducted in this study.

Endüstri 4.0 Bağlamında Akıllı Üretim Sistemleri ve Endüstriyel Robotlar Üzerine Teorik Bir Değerlendirme

Öz

Bu çalışma, Endüstri 4.0’ın getirdiği dönüşüm sürecinde akıllı üretim sistemleri, endüstriyel robotlar ve karanlık fabrikaların üretim süreçlerindeki rolünü kapsamlı biçimde ele almaktadır. Günümüzde üreticiler, daha kısa teslim süreleriyle daha yüksek kaliteli ürünler üretme baskısıyla karşı karşıyadır. Bu gereksinim, üretim sistemlerinin entegre, otomatik ve akıllı hâle gelmesini zorunlu kılmıştır. Akıllı üretim; yapay zekâ, nesnelerin interneti, büyük veri analitiği ve robotik sistemlerin entegre kullanımıyla esnek, duyarlı ve yüksek verimli üretim ortamları oluşturmaktadır. Endüstriyel robotlar, yalnızca görev bazlı hareket etmekle kalmayıp, insanlarla etkileşime geçerek öğrenebilen, tahmin yürütebilen ve üretim süreçlerine aktif biçimde katılabilen sistemler hâline gelmiştir. Bu doğrultuda karanlık fabrikalar, insan müdahalesi olmaksızın 24 saat üretim yapılabilen, iş güvenliği, kalite ve verimlilik açısından önemli avantajlar sunan tesislerdir. Ancak bu sistemlerin yaygınlaşmasında; yüksek yatırım maliyetleri, bazı sektörlerde uygulanabilirlik sınırlamaları ve vasıfsız iş gücüne duyulan ihtiyacın azalması gibi zorluklar mevcuttur. Sonuç olarak, Endüstri 4.0’ın sunduğu teknolojik imkânların sürdürülebilir ve kapsayıcı fayda sağlayabilmesi için, yetkin insan kaynağına yapılan yatırımların artırılması, eğitim politikalarının yeniden yapılandırılması ve stratejik kamu desteklerinin devreye alınması gerekmektedir.

Anahtar Kelimeler

• Endüstriyel robotlar
• akıllı üretim sistemleri
• yapay zeka

Etik Beyan

Araştırmanın gerçekçi ve etik olarak ihityaca cevap verebilecek şekilde tasarlanmasında; araştırmada kullanılan verilerin elde edilmesine, sonlandırılmasına ve çalışma sonuçlarının yayınlanmasında dürüst bir şekilde davranıldığı beyan edilmiştir. Araştırmada etik kurulu alınmasına gerek bulunmamaktadır. Çalışmada etik kurul gerektiren bir araştırma yapılmamıştır.

Kaynakça

1. Akben, İ. & Avşar, İ. İ. (2018). Endüstri 4.0ve Karanlık Üretim: Genel Bir Bakış. Türk Sosyal Bilimler Araştırmaları Dergisi, 3 (1), 26-37.
2. Bahrin, M. A., Othman, M., Nor Azli, N., & Talib, M. (2016). Industry 4.0: A Revıew On Industrıal Automatıon And Robotıc. Jurnal Teknologi, 78(6-13), 137–143.
3. Bolatan, G. İ. S. (2020). Kalite 4.0. Iğdır Üniversitesi Sosyal Bilimler Dergisi, (21), 437-454. Brogårdh, T. (2007). Present and future robot control development—An industrial perspective. Annual Reviews in Control, 31(1), 69–79.
4. Buchmeister, B., Palcic, I., & Ojstersek, R. (2019). Artificial Intelligence in Manufacturing Companies And Broader: An Overview. DAAAM International Scientific Book.
5. Cheng, Y. S., Chuah, J., & Wang, Y. (2021). Industrial revolution 4.0 – big data and big data analytics for smart manufacturing. W. Y. Leong, J. Chuah, & B. Tee (Ed.), The Nine Pillars of Technologies for Industry 4.0 (s. 35-60). London: The Institution of Engineering and Technology.
6. Choi, S., Kim, B., & Do Noh, S. (2015). A diagnosis and evaluation method for strategic planning and systematic design of a virtual factory in smart manufacturing systems. International Journal of Precision Engineering and Manufacturing, 16(6), 1107–1115.
7. Chuah, J. H. (2021). The Nine Pillars of technology for Industry 4.0. W. Y. Leong, J. H. Chuah, & B. T. Tee (Ed.), The Nine Pillars of Technologies for Industry 4.0 (s. 1-22). London: The Institution of Engineering and Technology. Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145–156.
8. Dilek, S. (2010). Türkiye’de Yeni Ekonominin Tüketicilere Enformasyon Sağlama Olanakları. Uluslararası Yönetim İktisat ve İşletme Dergisi, 6(11), 63-78.

9. Engelberger, J. F. (1980). Robotics in Practice Management and applications of industrial robots . London: Kogan Page.
10. Evjemo, L. D., Gjerstad, T., Grøtli, E., & Sziebig, G. (2020). Trends in Smart Manufacturing: Role of Humans and Industrial Robots in Smart Factories. Current Robotics Reports, 1(2), 35–41.
11. Hägele, M., Nilsson, K., & Pires, J. (2008). Industrial Robotics. B. Siciliano, & O. Khatib (Ed.), Springer Handbook of Robotics (s. 963–986). German: Springer.
12. Hamet, P., & Tremblay, J. (2017). Artificial Intelligence in Medicine . Metabolism , 69, 36-40. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer , 18 (8), 500-510.
13. Hozdić, E. (2015). Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies, 7(1), 28-35.
14. Indri, M., Grau, A., & Ruderman, M. (2018). Guest editorial special section on recent trends and developments in industry 4.0 motivated robotic solutions. IEEE Transactions on Industrial Informatics, 14(4), 1677-1680.
15. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology , 2 (4). 230-243
16. Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., et al. (2018). Artificial Intelligence in Cardiology. Journal of the American College of Cardiology , 71 (23), 2668-2679.
17. Kannaraya, P. S., Dilip, S., Deshpande, C., & Arora, M. (2019). Smart Multi-material Weight Tracking Resource Bin. A. Chakrabarti, & M. Arora (Ed.), Industry 4.0 and Advanced Manufa cturing Proceedings of I-4AM 2019 içinde (s. 65-74). India: Springer.
18. Kaushal, I., Siddharth, L., & Chakrabarti, A. (2019). A Conceptual Model for Smart Manufacturing Systems. A. Chakrabarti, & M. Arora (Ed.), Industry 4.0 and Advanced Manufa cturing Proceedings of I-4AM 2019 içinde (s. 75-88). India: Springer.
19. Koch, M., Manuylov, I., & Smolka, M. (2021). Robots and firms. The Economic Journal, 131, 2553–2584.
20. Krishnan, S., & Mendoza Santos, R. X. (2021). Real-Time Asset Tracking for Smart Manufacturing. C. Toro, W. Wang, & H. Akhtar (Ed.), Implementing Industry 4.0 The Model Factory as the Key Enabler for the Future of Manufacturing (s. 25-54). Switzerland: Springer.
21. Krittanawong, C., Zhang, H., Wang, Z., & Aydar, M. (2017). Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology , 69 (21), 2657-2664.
22. Lee, N. K. (2018). Total automation: The possibility of lights-out manufacturing in the near future. Missouri S&T’s Peer to Peer, 2(1), 4.
23. Lu, P., Chen, S., & Zheng, Y. (2012). Artificial Intelligence in Civil Engineering. Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2012, Article ID 145974, 22 pages doi:10.1155/2012/145974
24. Mehrpouya M., Dehghanghadikolaei A., Fotovvati B., Vosooghnia A., Emamian S.S. 5 & Gisario A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied. Science. 9, 3865, 1-34; doi:10.3390/app9183865
25. Mellit, A., & Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Progress in Energy and Combustion Science , 34, 574-632.
26. Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics: Research and Applications , 13 (9), 13-39.
27. Pascual, D. G., Daponte, P., & Kumar, U. (2020). Handbook of Industry 4.0 and SMART Systems. New York: CRC Press.
28. Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857.
29. Pham, D. T., & Pham, P. (1999). Artificial intelligence in engineering. International Journal of Machine Tools and Manufacture , 36 (6), 937-949.
30. Pires, J. N. (2007). Industrial Robots Programming: Building Applications for the Factories of the Future. New York: Springer.
31. Pirim, H. (2006). Yapay zeka. Yaşar Üniversitesi E-Dergisi, 1(1), 81-93.
32. Ram, P. S., & Lawrence, K. (2019). Implementation of Quality Information Framework (QIF): Towards Automatic Generation of Inspection Plan from Model-Based Definition (MBD) of Parts. A. Chakrabarti, & M. Arora (Ed.), Industry 4.0 and Advanced Manufa cturing Proceedings of I-4AM 2019 içinde (s. 127-138). India: Springer.
33. Reji, A. T., Dogra, A., & Singla, E. (2019). Workspace Reconstruction for Designing Modular Reconfigurable Manipulators. A. Chakrabarti, & M. Arora (Ed.), Industry 4.0 and Advanced Manufa cturing Proceedings of I-4AM 2019 içinde (s. 277-287). India: Springer.
34. Roads, C. (1985). Research in Music and Artificial Intelligence. ACM Computing Surveys (CSUR) , 17 (2), 163-190.
35. Roveda L., Magni M., Cantoni M., Piga D. & Bucca G. (2021). Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization.
36. Ruishu, Z., Chang, Z., & Weigang, Z. (2018). The status and development of industrial robots. IOP Conference Series: Materials Science and Engineering, 423(012051), 1-5.
37. Sabzehmeidani, Y. (2021). How industrial robots form smart factories. W. Y. Leong, J. H. Chuah, & B. T. Tee (Ed.), The Nine Pillars of Technologies for Industry 4.0 (s. 177-191). London: The Institution of Engineering and Technology.
38. Sarkar, B., Guchhait, R., Sarkar, M., & Cárdenas-Barrón, L. (2019). How does an industry manage the optimum cash flow within a smart production system with the carbon footprint and carbon emission under logistics framework? International Journal of Production Economics, 213, 243–257.
39. Seetharam, K., Sirish, S., & Sengupta, P. P. (2019). Artificial Intelligence in Cardiovascular Medicine. Curr Treat Options Cardio Med (2019) 21: 251-14 DOI 10.1007/s11936-019-0728-
40. Shimizu, H., & Nakayama, K. I. (2020). Artificial intelligence in oncology. Cancer science , 111 (5). 1452-1460
41. Şekelli, Z. H., & Bakan, İ. (2018). Akıllı Fabrikalar. Journal of Life Economics, 5(4), 203-220.
42. Şen, A. T. (2024). Kamu Çalışanlarının Yapay Zeka Kaygı Düzeylerinin Belirlenmesi: Kastamonu Örneği. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(2), 232–246. http://doi.org/10.25287/ohuiibf.1384425.
43. Thoben, K., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and Smart Manufacturing – A Review of Research Issues and Application Examples. International Journal of Automation Technology, 11(1), 4–16.
44. Todd, D. J. (1986). Fundamentals of Robot Technology: An Introduction to Industrial Robots, Teleoperators and Robot Vehicles. London: Kogan Page.
45. Ulusoy, T. & Civek, F. (2020). İnsani İhtiyaçlar Değişiyor Mu? Maslow İhtiyaçlar Hiyarirşisinden Covid-19 İhtiyaçlar Hiyararşisine Teorik Bir Değerlendirme. Social Sciences Studies Journal, 10.
46. Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia manufacturing, 20, 233-238.
47. Waibel, M. W., Steenkamp, L., Moloko, N., & Oosthuizen, G. (2017). Investigating the Effects of Smart Production Systems on Sustainability Elements. Procedia Manufacturing, 8, 731–737.
48. Wan, J., Li, X., Dai, H. N., Kusiak, A., Martínez-García, M., & Li, D. (2020). Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges. Proceedings of the IEEE, 109(4), 377-398.
49. Winfield, A. F., & Jirotka, M. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180085.
50. Yılmaz, C. (2021). The Role of Human Resources in the Dark Factories of Industry 4.0: A Dysfunctional Classroom? International Journal of Innovative Approaches in Social Sciences, 5 (4), 236-247.
51. Zhang T., Zhang Q., Lim E., & Sun J. (2022). "Deep Learning Based 3D Point Clouds Recognition for Robotic Manufacturing," 27th International Conference on Automation and Computing (ICAC),1-5. doi: 10.1109/ICAC55051.2022.9911175.
52. Zheng, P., Wang, H., Sang, Z., Zhong, R., Liu, Y., Liu, C., . . . XU, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150.
53. Üster, Z. (2024). Supply Chain Management And Logistics In The Industry 4.0 Environment: A Theoretical Evaluation. Quantrade Journal of Complex Systems in Social Sciences, 6(1), 94-107.
54. Kaya, S., & Yıldız, B. (2024). New Trend in Supply Chain and Logistics Operations: Blockchain Technology. Quantrade Journal of Complex Systems in Social Sciences, 6(2), 217-225.