#

Advances in Intelligent Systems and Technologies

Book Series

About the Book
About the Author
Table of Contents

Buy this Book

eBook
  • • Included format: Online and PDF
  • • eBooks can be used on all reading devices
  • • ISSN : 2959-3042
  • • ISBN : 978-9914-9946-4-3


Hardcover
  • • Including format: Hardcover
  • • Shipping Available for individuals worldwide
  • • ISSN : 2959-3034
  • • ISBN : 978-9914-9946-5-0


Services for the Book

Download Product Flyer
Download High-Resolutions Cover

1st International Conference on Emerging Trends in Mechanical Sciences for Sustainable Technologies

Automating Lathe Manufacturing Processes with Internet of Things: A Review

Sathish K, Ganeshkumar S, Mohan Prasanth D, Barath G and Thiyagarajan V, Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.


Online First : 18 August 2023
Publisher Name : AnaPub Publications, Kenya.
ISSN (Online) : 2959-3042
ISSN (Print) : 2959-3034
ISBN (Online) : 978-9914-9946-4-3
ISBN (Print) : 978-9914-9946-5-0
Pages : 092-100

Abstract


IoT is a new technology that is transforming the manufacturing sector. This study examines how IoT technology is being used to automate the production of lathes. It gives a general summary of the technology's current condition and its possibilities for production in the future. The paper evaluates the advantages of IoT-enabled automation for lathe manufacturing processes and the challenges faced in its implementation. It also looks at various IoT-enabled solutions that have been successfully deployed in lathe manufacturing processes. Additionally, the paper discusses some potential applications of IoT technology in lathe manufacturing processes and the potential benefits they could bring. Finally, the paper provides recommendations for further research into the application of IoT in lathe manufacturing processes. This paper provides valuable insight into the potential of IoT-enabled automation for lathe manufacturing processes and the challenges faced in its implementation. It gives an overview of the technology's position at the moment and the chances for further growth that may exist.

Keywords


Internet of Things, IoT, Manufacturing, Automation, Lathe Manufacturing Processes, Advantages, Challenges, Solutions, Applications.

  1. S. I. Shafiq, E. Szczerbicki, and C. Sanin, “Manufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenario,” Cybernetics and Systems, vol. 49, no. 5–6, pp. 280–295, Feb. 2018, doi: 10.1080/01969722.2017.1418265.
  2. D. M. Segura Velandia, N. Kaur, W. G. Whittow, P. P. Conway, and A. A. West, “Towards industrial internet of things: Crankshaft monitoring, traceability and tracking using RFID,” Robotics and Computer-Integrated Manufacturing, vol. 41, pp. 66–77, Oct. 2016, doi: 10.1016/j.rcim.2016.02.004.
  3. T. F. Aydos and J. C. E. Ferreira, “RFID-based system for Lean Manufacturing in the context of Internet of Things,” 2016 IEEE International Conference on Automation Science and Engineering (CASE), Aug. 2016, doi: 10.1109/coase.2016.7743533.
  4. C. Sanin, Z. Haoxi, I. Shafiq, M. M. Waris, C. Silva de Oliveira, and E. Szczerbicki, “Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies,” Future Generation Computer Systems, vol. 92, pp. 604–616, Mar. 2019, doi: 10.1016/j.future.2018.01.062.
  5. F.-T. Cheng et al., “Industry 4.1 for Wheel Machining Automation,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 332–339, Jan. 2016, doi: 10.1109/lra.2016.2517208.
  6. Y. Zuo, F. Tao, and A. Y. C. Nee, “An Internet of things and cloud-based approach for energy consumption evaluation and analysis for a product,” International Journal of Computer Integrated Manufacturing, vol. 31, no. 4–5, pp. 337–348, Feb. 2017, doi: 10.1080/0951192x.2017.1285429.
  7. Karabegović, E. Karabegović, M. Mahmić, and E. Husak, “Implementation of Industry 4.0 and Industrial Robots in the Manufacturing Processes,” Lecture Notes in Networks and Systems, pp. 3–14, Apr. 2019, doi: 10.1007/978-3-030-18072-0_1.
  8. Yan and S. Melkote, “Automated manufacturability analysis and machining process selection using deep generative model and Siamese neural networks,” Journal of Manufacturing Systems, vol. 67, pp. 57–67, Apr. 2023, doi: 10.1016/j.jmsy.2023.01.006.
  9. Kondratenko, Y.P., Kozlov, O.V., Korobko, O.V. and Topalov, A.M., “Internet of Things Approach for Automation of the Complex Industrial Systems,” In ICTERI (pp. 3-18), May 2017.
  10. Y. Lu and M. R. Asghar, “Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing,” Journal of Manufacturing Systems, vol. 55, pp. 348–359, Apr. 2020, doi: 10.1016/j.jmsy.2020.05.001.
  11. R. J. Setiawan, A. Tarnadi, and I. Surfani, “Design and Manufacture an Automatic Mushroom Sprinkler based Internet of Things to Increase Oyster Mushroom Productivity,” JMPM (Jurnal Material dan Proses Manufaktur), vol. 5, no. 1, pp. 1–9, Oct. 2021, doi: 10.18196/jmpm.v5i1.12043.
  12. X. Wu, S. Tian, and L. Zhang, “The Internet of Things Enabled Shop Floor Scheduling and Process Control Method Based on Petri Nets,” IEEE Access, vol. 7, pp. 27432–27442, 2019, doi: 10.1109/access.2019.2900117.
  13. X. He, Y. Yang, C. Long, T. Xi, G. Wu, and S. Tang, “The Design Innovation of Shop Floor Paradigm Towards Smart Manufacturing,” 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ), Oct. 2022, doi: 10.1109/iaeac54830.2022.9929861.
  14. C.-H. Chen, M.-Y. Lin, and C.-C. Liu, “Edge Computing Gateway of the Industrial Internet of Things Using Multiple Collaborative Microcontrollers,” IEEE Network, vol. 32, no. 1, pp. 24–32, Jan. 2018, doi: 10.1109/mnet.2018.1700146.
  15. D. De Guglielmo, G. Anastasi, and A. Seghetti, “From IEEE 802.15.4 to IEEE 802.15.4e: A Step Towards the Internet of Things,” Advances onto the Internet of Things, pp. 135–152, 2014, doi: 10.1007/978-3-319-03992-3_10.
  16. S. Sutopo, B. R. Setiadi, and M. Hanzla, “Upgrading Manual Turning Machine Towards IoT-Based Manufacturing,” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 26, no. 2, pp. 155–161, Sep. 2020, doi: 10.21831/jptk.v26i2.27334.
  17. P. Kilimis, W. Zou, M. Lehmann, and U. Berger, “A Survey on Digitalization for SMEs in Brandenburg, Germany,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 2140–2145, 2019, doi: 10.1016/j.ifacol.2019.11.522.
  18. M. Liu, X. Li, J. Li, Y. Liu, B. Zhou, and J. Bao, “A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing,” Advanced Engineering Informatics, vol. 51, p. 101515, Jan. 2022, doi: 10.1016/j.aei.2021.101515.
  19. X. Chen, C. Li, Y. Tang, and Q. Xiao, “An Internet of Things based energy efficiency monitoring and management system for machining workshop,” Journal of Cleaner Production, vol. 199, pp. 957–968, Oct. 2018, doi: 10.1016/j.jclepro.2018.07.211.
  20. H. Zhang, Q. Yan, and Z. Wen, “Information modeling for cyber-physical production system based on digital twin and AutomationML,” The International Journal of Advanced Manufacturing Technology, vol. 107, no. 3–4, pp. 1927–1945, Mar. 2020, doi: 10.1007/s00170-020-05056-9.
  21. M. B. Raval and H. Joshi, “Categorical framework for implementation of industry 4.0 techniques in medium-scale bearing manufacturing industries,” Materials Today: Proceedings, vol. 65, pp. 3531–3537, 2022, doi: 10.1016/j.matpr.2022.06.090.
  22. H. Yang, S. Kumara, S. T. S. Bukkapatnam, and F. Tsung, “The internet of things for smart manufacturing: A review,” IISE Transactions, vol. 51, no. 11, pp. 1190–1216, May 2019, doi: 10.1080/24725854.2018.1555383.
  23. Junankar, J. K. Purohit, and N. V. Bhende, “A Framework for Integration of Internet of Things with Minimum Quantity Lubrication System,” SSRN Electronic Journal, 2019, doi: 10.2139/ssrn.3356491.
  24. V. Taratukhin, Y. Yadgarova, and A. Stelvaga, “Hybrid Cloud Environment for Manufacturing Control System,” 2016 ASEE Annual Conference & Exposition Proceedings, doi: 10.18260/p.25503.
  25. T. Qiu, J. Chi, X. Zhou, Z. Ning, M. Atiquzzaman, and D. O. Wu, “Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2462–2488, 2020, doi: 10.1109/comst.2020.3009103.
  26. Ahamed, M.S., Hasan, S., Rashid, A.A. and Rahman, M.A., “A Cyber-Physical System (CPS) for Automating Additive Manufacturing Process with Industry 4.0. In Proceedings of the International Conference on Mechanical,” Industrial and Energy Engineering, Khulna, Bangladesh (pp. 19-21), December, 2020.
  27. K. Krot, G. Iskierka, B. Poskart, and A. Gola, “Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology,” Materials, vol. 15, no. 19, p. 6561, Sep. 2022, doi: 10.3390/ma15196561.
  28. H. Tieng, T.-H. Tsai, C.-F. Chen, H.-C. Yang, J.-W. Huang, and F.-T. Cheng, “Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 934–941, Apr. 2018, doi: 10.1109/lra.2018.2792690.
  29. “Research and Practice of Engineering Training Intelligent Manufacturing Practice Teaching Platform Based on the Cultivation of New Engineering Innovative Talents,” International Journal of Frontiers in Sociology, vol. 3, no. 14, 2021, doi: 10.25236/ijfs.2021.031415.
  30. Y. Lu, Z. Liu, and Q. Min, “A digital twin-enabled value stream mapping approach for production process reengineering in SMEs,” International Journal of Computer Integrated Manufacturing, vol. 34, no. 7–8, pp. 764–782, Jan. 2021, doi: 10.1080/0951192x.2021.1872099.
  31. S. Venkatesh, S. P. Sivapirakasam, M. Sakthivel, S. Ganeshkumar, M. Mahendhira Prabhu, and M. Naveenkumar, “Experimental and numerical investigation in the series arrangement square cyclone separator,” Powder Technology, vol. 383, pp. 93–103, May 2021, doi: 10.1016/j.powtec.2021.01.031.
  32. Ganeshkumar, S., Thirunavukkarasu, V., Sureshkumar, R., Venkatesh, S. and Ramakrishnan, T., “Investigation of wear behaviour of silicon carbide tool inserts and titanium nitride coated tool inserts in machining of en8 steel. International Journal of Mechanical Engineering and Technology, 10(01), pp.1862-1873, 2019.
  33. Ganeshkumar, S., Sureshkumar, R., Sureshbabu, Y. and Balasubramani, S., “A review on cutting tool measurement in turning tools by cloud computing systems in industry 4.0 and IoT. GIS science journal,” 7(8), pp.1-7, 2020.
  34. Ganeshkumar, S., Sureshkumar, R., Sureshbabu, Y. and Balasubramani, S., “A numerical approach to cutting tool stress in CNC turning of EN8 steel with silicon carbide tool insert,” International Journal of Scientific & Technology Research, 8(12), pp.3227-3231, 2019.
  35. S. G. Kumar and V. Thirunavukkarasu, “Investigation of Tool Wear and Optimization of Process Parameters in Turning of EN8 and EN 36 Steels,” Asian Journal of Research in Social Sciences and Humanities, vol. 6, no. 11, p. 237, 2016, doi: 10.5958/2249-7315.2016.01188.6.
  36. G. Gokilakrishnan, S. Ganeshkumar, H. Anandakumar, and M. Vigneshkumar, “A Critical Review of Production Distribution Planning Models,” 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Mar. 2021, doi: 10.1109/icaccs51430.2021.9441879.
  37. S. Ganeshkumar et al., “Investigation of Tensile Properties of Different Infill Pattern Structures of 3D-Printed PLA Polymers: Analysis and Validation Using Finite Element Analysis in ANSYS,” Materials, vol. 15, no. 15, p. 5142, Jul. 2022, doi: 10.3390/ma15155142.
  38. S. Ganeshkumar and S. Venkatesh, “Manufacturing Techniques and Applications of Multifunctional Metal Matrix Composites,” Functional Composite Materials: Manufacturing Technology and Experimental Application, pp. 157–167, Apr. 2022, doi: 10.2174/9789815039894122010013.
  39. G. S, D. T, and A. Haldorai, “A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing,” Defence Science Journal, vol. 72, no. 5, pp. 712–720, Nov. 2022, doi: 10.14429/dsj.72.17533.
  40. S. Ganeshkumar et al., “Performance of Multilayered Nanocoated Cutting Tools in High-Speed Machining: A Review,” International Journal of Photoenergy, vol. 2022, pp. 1–8, Oct. 2022, doi: 10.1155/2022/5996061.
  41. S. Ganeshkumar et al., “Study of Wear, Stress and Vibration Characteristics of Silicon Carbide Tool Inserts and Nano Multi-Layered Titanium Nitride-Coated Cutting Tool Inserts in Turning of SS304 Steels,” Materials, vol. 15, no. 22, p. 7994, Nov. 2022, doi: 10.3390/ma15227994.
  42. Garcia-Crespo, B. Ruiz-Mezcua, J. L. Lopez-Cuadrado, and J. M. Gomez-Berbis, “Conceptual model for semantic representation of industrial manufacturing processes,” Computers in Industry, vol. 61, no. 7, pp. 595–612, Sep. 2010, doi: 10.1016/j.compind.2010.01.004.
  43. Liu and P. Jiang, “A Cyber-physical System Architecture in Shop Floor for Intelligent Manufacturing,” Procedia CIRP, vol. 56, pp. 372–377, 2016, doi: 10.1016/j.procir.2016.10.059.
  44. Awasthi, K. K. Saxena, and V. Arun, “Sustainable and smart metal forming manufacturing process,” Materials Today: Proceedings, vol. 44, pp. 2069–2079, 2021, doi: 10.1016/j.matpr.2020.12.177.
  45. Y. Liau, H. Lee, and K. Ryu, “Digital Twin concept for smart injection molding,” IOP Conference Series: Materials Science and Engineering, vol. 324, p. 012077, Mar. 2018, doi: 10.1088/1757-899x/324/1/012077.
  46. M. Ikumapayi, S. T. Oyinbo, E. T. Akinlabi, and N. Madushele, “Overview of recent advancement in globalization and outsourcing initiatives in manufacturing systems,” Materials Today: Proceedings, vol. 26, pp. 1532–1539, 2020, doi: 10.1016/j.matpr.2020.02.315.

Cite this article


Sathish K, Ganeshkumar S, Mohan Prasanth D, Barath G and Thiyagarajan V, “Automating Lathe Manufacturing Processes with Internet of Things: A Review”, Advances in Intelligent Systems and Technologies, pp. 092-100, August. 2023. doi:10.53759/aist/978-9914-9946-4-3_15

Copyright


© 2023 Sathish K, Ganeshkumar S, Mohan Prasanth D, Barath G and Thiyagarajan V. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.