SpringerLink
Log in

Green manufacturing via machine learning enabled approaches

  • Review
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

This article has been updated

Abstract

Inefficient production policies and practices have led to unprecedented wastages within various manufacturing domains. This unprecedented wastage and environmental exacerbation due to unsustainable practices have made it necessary to implement green manufacturing methods. However, the traditional methods have not been as efficient as expected and lack precision. Where Machine Learning models have been implemented in various research work done to date, they have shown efficient outcomes, and reduced human errors or even human involvement up to some extent. This review rigorously compiles and presents a set of research works done to date, which will give a clear perspective and applicability of ML models in various manufacturing processes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Change history

  • 16 January 2023

    Citations across the proofs updated

References

  1. Rajput, S.P., Datta, S.: Sustainable and green manufacturing—A narrative literature review. Mater. Today: Proc. 26, 2515–2520 (2020)

    Google Scholar 

  2. Satao, S., Yakkundi, V.: Analyzing Environmental Performance of Green Manufacturing (GM) Initiative using Principal Component Analysis and Multiple Regression Method in Indian Context.

  3. Sangwan, K.S., Mittal, V.K.: A bibliometric analysis of green manufacturing and similar frameworks. Manag. Environ. Qual.: Int. J. (2015).

  4. Azzone, G., Noci, G.: Identifying effective PMSs for the deployment of "green" manufacturing strategies. Int. J. Oper. Prod. Manag. (1998)

  5. Dornfeld, D., Yuan, C., Diaz, N., Zhang, T., Vijayaraghavan, A.: Introduction to green manufacturing. In: Green Manufacturing, pp. 1–23. Springer, Boston, MA (2013).

  6. Aayush, B., Renold, S.E., Daniel, A., Narayan, M.J., Sakthivel, A.R., Chadha, U., Hirpha, B.B.: Prediction and Experimental Verification of Distortion due to Residual Stresses in a Ti-6Al-4V Control Arm Plate. Adv. Mater. Sci. Eng. Vol 2022, page no 5211623 (2022). https://doi.org/10.1155/2022/5211623

  7. Chadha, U., Selvaraj, S.K., Raj, A. et al.: AI-driven techniques for controlling the metal melting production: a review, processes, enabling technologies, solutions, and research challenges. Mater. Res. Express. (2022). https://doi.org/10.1088/2053-1591/ac7b70

  8. Chadha, U., Selvaraj, S.K., Gunreddy N. et al.: A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions. Mater. Design. Process, Commun. vol 2022, page no 2568347 (2022). https://www.hindawi.com/journals/mdp/2022/2568347/

  9. Mohan, P.V., Dixit, S., Gyaneshwar A. et al.: Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions. Sens. Netw. vol 22, pageno 2194 (2022). https://www.mdpi.com/1424-8220/22/6/2194

  10. Mamdiwar, S.D, Akshith, R., Shakruwala, Z. et al.: Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring. Biosensors. vol 11, page no 372. (2021). https://www.mdpi.com/2079-6374/11/10/372

  11. Madhavadas, V., Srivastavaa, D., Chadhaa, U. et al.: A review on metal additive manufacturing for intricately shaped aerospace components. CIRP J. Manuf. Sci. Technol. VOL 39, page no 18-36, (2022). https://www.sciencedirect.com/science/article/abs/pii/S1755581722001304?via%3Dihub

  12. Patel, S.S., Shiva, S.M., Kataray, T.: Trends in tribological behaviour of materials for compressors. J. Phys.: Conf. Series. (2022). https://doi.org/10.1088/1742-6596/2272/1/012023

  13. Selvaraj, S.K., Raj, A., Mahadevan, R.R.: A Review on Machine Learning Models in Injection Molding Machines. Adv. Mater. Sci. Eng. VOL 2022, page no 1949061 (2022). https://www.hindawi.com/journals/amse/2022/1949061/

  14. Raj, A., Kishore, R.S., Jose, L. et al.: A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues. Eur. Phys. J. Plus. (2021). https://doi.org/10.1140/epjp/s13360-021-01689-1

  15. Srinivasan, K., Chadha, U., Mishra, R. et al.: Contemporary Progresses in Ultrasonic Welding of Aluminum Metal Matrix Composites. (2021). https://doi.org/10.3389/fmats.2021.647112

  16. Pang, R., Zhang, X.: Achieving environmental sustainability in manufacture: a 28-year bibliometric cartography of green manufacturing research. J. Clean. Prod. 233, 84–99 (2019)

    Article  Google Scholar 

  17. Eshikumo, S.M., Odock, S.O.: Green manufacturing and operational performance of a firm: case of cement manufacturing in Kenya. Int. J. Bus. Soc. Sci. 8(4), 106–120 (2017)

    Google Scholar 

  18. Gogoi, M., Hazarika, M.: A green process planning system. In 5th International and 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) (2014)

  19. Chatha, K.A., Butt, I.: Themes of study in manufacturing strategy literature. Int. J. Oper. Prod. Manag. (2015)

  20. Rehman, M.A., Shrivastava, R.L.: Green manufacturing (GM): past, present and future (a state of art review). World Rev. Sci. Technol. Sustain. Dev. 10, 17–55 (2013)

    Article  Google Scholar 

  21. Harding, T.S.: Life cycle assessment as a tool for Green manufacturing education. Mater. Eng. 62, 1 (2004)

    Google Scholar 

  22. Wenzel, H., Alting, L.: Danish experience with the EDIP tool for environmental design of industrial products. In: Proceedings 1st International Symposium on Environmentally Conscious Design and Inverse Manufacturing, pp. 370–379. IEEE (1999)

  23. Gandhi, S., Mangla, S. K., Kumar, P., Kumar, D.: A combined approach using AHP and DEMATEL for evaluating success factors in implementation of green supply chain (2016)

  24. Li, K., Zhang, X., Leung, J.Y.T., Yang, S.L.: Parallel machine scheduling problems in green manufacturing industry. J. Manuf. Syst. 38, 98–106 (2016)

    Article  Google Scholar 

  25. Seth, D., Rehman, M.A.A., Shrivastava, R.L.: Green manufacturing drivers and their relationships for small and medium (SME) and large industries. J. Clean. Prod. 198, 1381–1405 (2018)

    Article  Google Scholar 

  26. Belhadi, A., Kamble, S.S., Zkik, K., Cherrafi, A., Touriki, F.E.: The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa. J. Clean. Prod. 252, 119903 (2020)

    Article  Google Scholar 

  27. Sharma, A., Chouhan, A., Pavithran, L. et al.: Implementation of LSS framework in automotive component manufacturing: A review, current scenario and future directions. Materialstoday. Proceed. vol 46, pageno 7815-7824, (2021). https://www.sciencedirect.com/science/article/pii/S2214785321014863?via%3Dihub

  28. Pati, T., Kabra, S., Chadha, U. et al.: Statistical Quality Study of the Parts Produced in an Automobile Industry: A Daimler India Case Study. IOP Conf. Series.: Mater. Sci. Eng. (2021). https://doi.org/10.1088/1757-899X/1206/1/012022

  29. Sivasubramani, R., Verma, A., Rithvik, G. et al.: Influence on nonhomogeneous microstructure formation and its role on tensile and fatigue performance of duplex stainless steel by a solid-state welding process. Materialstoday. Proceed. vol 46, page no 7284-7296, (2021). https://www.sciencedirect.com/science/article/pii/S2214785320407011?via%3Dihub

  30. Selvaraj, S.K., Raj, A., Dharnidharka, M. et al.: A Cutting-Edge Survey of Tribological Behavior Evaluation Using Artificial and Computational Intelligence Models. vol 2021, page no 9529199 (2021). https://www.hindawi.com/journals/amse/2021/9529199/

  31. Reich-Weiser, C., Simon, R., Fleschutz, T., Yuan, C., Vijayaraghavan, A., Onsrud, H. (2013). Metrics for green manufacturing. In: Green manufacturing, pp. 49–81. Springer, Boston, MA.

  32. Johansson, G., Winroth, M.: Lean vs. Green manufacturing: Similarities and differences. In: Proceedings of the 16th International Annual EurOMA Conference, Implementation Realizing Operations Management Knowledge, Göteborg, Sweden (2009)

  33. Mittal, V.K., Sindhwani, R., Kapur, P.K.: Two-way assessment of barriers to lean–green manufacturing system: insights from India. Int. J. Syst. Assur. Eng. Manag. 7(4), 400–407 (2016)

    Article  Google Scholar 

  34. Mittal, V. K., Egede, P., Herrmann, C., Sangwan, K. S.: Comparison of drivers and barriers to green manufacturing: a case of India and Germany. In: Re-engineering manufacturing for sustainability, pp. 723–728. Springer, Singapore (2013)

  35. Trentesaux, D., Giret, A.: Go-green manufacturing holons: a step towards sustainable manufacturing operations control. Manuf. Lett. 5, 29–33 (2015)

    Article  Google Scholar 

  36. Verma, N., Sharma, V.: Energy value stream map** a tool to develop green manufacturing. Procedia Eng. 149, 526–534 (2016)

    Article  Google Scholar 

  37. Drummond, C., McCann, R., Patwardhan, S.V.: A feasibility study of the biologically inspired green manufacturing of precipitated silica. Chem. Eng. J. 244, 483–492 (2014)

    Article  Google Scholar 

  38. Bergmiller, G.G., McCright, P.R.: Lean manufacturers' transcendence to green manufacturing. In: Proceedings of the 2009 industrial engineering research conference, vol. 30, (2009)

  39. Bhattacharya, A., Dey, P. K., & Ho, W. Green manufacturing supply chain design and operations decision support (2015)

  40. Mittal, V.K., Sangwan, K.S.: Prioritising barriers to green manufacturing: environmental, social and economic perspectives. Procedia Cirp. 17, 559–564 (2014)

    Article  Google Scholar 

  41. Mathiyazhagan, K., Sengupta, S., Mathivathanan, D.: Challenges for implementing green concept in sustainable manufacturing: a systematic review. Opsearch 56(1), 32–72 (2019)

    Article  MATH  Google Scholar 

  42. Dharnidharka, M., Chadha, U., Dasari, L.M. et al.: Optical tomography in additive manufacturing: a review, processes, open problems, and new opportunities. Eur. Phys. J. Plus. (2021). https://doi.org/10.1140/epjp/s13360-021-02108-1

  43. Virmani, K., Deepak, C., Sharma, S. et al.: Nanomaterials for automotive outer panel components: a review. Eur. Phys. J. Plus. (2021). https://doi.org/10.1140/epjp/s13360-021-01931-w

  44. Dash, A., Kabra, S., Misra, S. et al.: Comparative property analysis of fused filament fabrication PLA using fresh and recycled feedstocks. Mat Research Exp (2022). https://doi.org/10.1088/2053-1591/ac96d4

  45. Alay, E., Duran, K., Korlu, A.: A sample work on green manufacturing in textile industry. Sustain. Chem. Pharm. 3, 39–46 (2016)

    Article  Google Scholar 

  46. Oumer, A.J., Cheng, J.K., Tahar, R.M.: Green manufacturing and logistics in automotive industry: a simulation model. In: 2015 9th International Conference on IT in Asia (CITA), pp. 1–6. IEEE (2015)

  47. Thomas, P.: Pharma's Green Evolution. Pharma Manufacturing. https://www.pharmamanufacturing.com/articles/2009/149/ (2015)

  48. Shalaby, M.Y., Al-Zahrani, K.H., Baig, M.B., Straquadine, G.S., Aldosari, F.: Threats and challenges to sustainable agriculture and rural development in Egypt: implications for agricultural extension. J. Anim. Plant Sci. 21(3), 581–588 (2011)

    Google Scholar 

  49. Turner, C., Frankel, M., Council, U.G.B.: Energy performance of LEED for new construction buildings. New Build. Inst. 4(4), 1–42 (2008)

    Google Scholar 

  50. Le, T.: The effect of green supply chain management practices on sustainability performance in Vietnamese construction materials manufacturing enterprises. Uncertain Supply Chain Manag. 8(1), 43–54 (2020)

    Article  Google Scholar 

  51. Kim, D.-H., Kim, T.J.Y., Wang, X., Kim, M., Quan, Y.-J., Oh, J.W., Ahn, S.-H.: Smart machining process using machine learning: a review and perspective on machining industry. Int. J. Precis. Eng. Manuf.-Green Technol. 5(4), 555–568 (2018). https://doi.org/10.1007/s40684-018-0057-y

    Article  Google Scholar 

  52. Sangwan, K.S.: Development of a multi criteria decision model for justification of green manufacturing systems. Int. J. Green Econ. 5(3), 285–305 (2011)

    Article  Google Scholar 

  53. Zhou, Y., Xu, G., Minshall, T., Liu, P.: How do public demonstration projects promote green-manufacturing technologies? A case study from China. Sustain. Dev. 23(4), 217–231 (2015)

    Article  Google Scholar 

  54. Prasetyawan, Y.: Defining technology strategy for small to medium enterprise within lean and green manufacturing framework. International Seminar on Industrial Engineering and Management (2016)

  55. Digalwar, A.K., Tagalpallewar, A.R., Sunnapwar, V.K.: Green manufacturing performance measures: an empirical investigation from Indian manufacturing industries. Measuring Business Excellence (2013)

  56. Gandhi, N.S., Thanki, S.J., Thakkar, J.J.: Ranking of drivers for integrated lean-green manufacturing for Indian manufacturing SMEs. J. Clean. Prod. 171, 675–689 (2018)

    Article  Google Scholar 

  57. Chuang, S.P., Yang, C.L.: Key success factors when implementing a green-manufacturing system. Prod. Plan. Control 25(11), 923–937 (2014)

    Article  Google Scholar 

  58. Domingo, R., Aguado, S.: Overall environmental equipment effectiveness as a metric of a lean and green manufacturing system. Sustainability 7(7), 9031–9047 (2015)

    Article  Google Scholar 

  59. Dornfeld, D.A., Arinez, J., Hapaala, K., Helu, M.: Sustainable Manufacturing. J. Manuf. Sci. Eng. 138(10), 1 (2016)

    Article  Google Scholar 

  60. Helu, M., Dornfeld, D.: Principles of green manufacturing. In: Green Manufacturing, pp. 107–115. Springer, Boston, MA (2013)

  61. Rao, R.V.: Environmental aspects of manufacturing processes. In: Advanced modeling and optimisation of manufacturing processes, pp. 339–360. Springer, London (2011)

  62. Zhu, X.Y., Zhang, H., Jiang, Z.G.: Application of green-modified value stream map** to integrate and implement lean and green practices: a case study. Int. J. Comput. Integr. Manuf. 33(7), 716–731 (2020)

    Article  Google Scholar 

  63. Jaiswal, P., Kumar, A.: Prioritisation of barriers in implementation of green manufacturing in Indian SMEs through integrated grey-DEMATEL approach. Int. J. Manuf. Technol. Manage. 32(3), 215–236 (2018)

    Article  Google Scholar 

  64. Digalwar, A.K., Mundra, N., Tagalpallewar, A.R., Sunnapwar, V.K.: Road map for the implementation of green manufacturing practices in Indian manufacturing industries. Benchmark.: Int. J. (2017)

  65. Liu, P., Zhou, Y., Zhou, D.K., Xue, L.: Energy Performance Contract models for the diffusion of green-manufacturing technologies in China: A stakeholder analysis from SMEs’ perspective. Energy Policy 106, 59–67 (2017)

    Article  Google Scholar 

  66. Kong, D., Feng, Q., Zhou, Y., Xue, L.: Local implementation for green-manufacturing technology diffusion policy in China: from the user firms’ perspectives. J. Clean. Prod. 129, 113–124 (2016)

    Article  Google Scholar 

  67. Ahn, S.H.: An evaluation of green manufacturing technologies based on research databases. Int. J. Precis. Eng. Manuf.-Green Technol. 1(1), 5–9 (2014)

    Article  Google Scholar 

  68. Ghazilla, R.A.R., Sakundarini, N., Abdul-Rashid, S.H., Ayub, N.S., Olugu, E.U., Musa, S.N.: Drivers and barriers analysis for green manufacturing practices in Malaysian SMEs: a preliminary findings. Procedia Cirp. 26, 658–663 (2015)

    Article  Google Scholar 

  69. Paul, I.D., Bhole, G.P., Chaudhari, J.R.: A review on green manufacturing: it’s important, methodology and its application. Procedia Mater. Sci. 6, 1644–1649 (2014)

    Article  Google Scholar 

  70. Deif, A.M.: A system model for green manufacturing. J. Clean. Prod. 19(14), 1553–1559 (2011)

    Article  Google Scholar 

  71. Sezen, B., Cankaya, S.Y.: Effects of green manufacturing and eco-innovation on sustainability performance. Procedia Soc. Behav. Sci. 99, 154–163 (2013)

    Article  Google Scholar 

  72. Singh, N.K., Pandey, P.M., Singh, K.K., Sharma, M.K.: Steps towards green manufacturing through EDM process: a review. Cogent Eng. 3(1), 1272662 (2016)

    Article  Google Scholar 

  73. Zhang, Y., Li, Z.H., Qi, T., Zheng, S.L., Li, H.Q., Xu, H.B.: Green manufacturing process of chromium compounds. Environ. Prog. 24(1), 44–50 (2005)

    Article  Google Scholar 

  74. Rehman, M.A., Seth, D., Shrivastava, R.L.: Impact of green manufacturing practices on organisational performance in Indian context: an empirical study. J. Clean. Prod. 137, 427–448 (2016)

    Article  Google Scholar 

  75. Orji, I.J., Wei, S.: Dynamic modeling of sustainable operation in green manufacturing environment. J. Manuf. Technol. Manag. (2015)

  76. Katna, R., Singh, K., Agrawal, N., Jain, S.: Green manufacturing—performance of a biodegradable cutting fluid. Mater. Manuf. Processes 32(13), 1522–1527 (2017)

    Article  Google Scholar 

  77. Pacis, D.M.M., Subido Jr, E.D., Bugtai, N.T.: Research on the application of internet of things (IoT) technology towards a green manufacturing industry: a literature review. In: DLSU Research Congress, pp. 1–11 (2017)

  78. Priore, P., de la Fuente, D., Puente, J., Parreño, J.: A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Eng. Appl. Artif. Intell. 19(3), 247–255 (2006). https://doi.org/10.1016/j.engappai.2005.09.009

    Article  Google Scholar 

  79. Tao, F., Zuo, Y., Da Li, Xu., Zhang, L.: IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans. Ind. Inf. 10(2), 1547–1557 (2014). https://doi.org/10.1109/tii.2014.2306397

    Article  Google Scholar 

  80. Monostori, L., Markus, A., Van Brussel, H., Westkämpfer, E.: Machine learning approaches to manufacturing. CIRP Ann. 45(2), 675–712 (1996). https://doi.org/10.1016/s0007-8506(18)30216-6

    Article  Google Scholar 

  81. Pham, D.T., Afify, A.A.: Machine-learning techniques and their applications in manufacturing. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 219(5), 395–412 (2005). https://doi.org/10.1243/095440505x32274

    Article  Google Scholar 

  82. Wuest, T., Irgens, C., Thoben, K.-D.: An approach to monitoring quality in manufacturing using supervised machine learning on product state data. J. Intell. Manuf. 25(5), 1167–1180 (2013). https://doi.org/10.1007/s10845-013-0761-y

    Article  Google Scholar 

  83. Priore, P., De La Fuente, D., Gomez, A., Puente, J.: A review of machine learning in dynamic scheduling of flexible manufacturing systems. Ai Edam 15(3), 251–263 (2001)

    MATH  Google Scholar 

  84. Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A.P., Karkazis, P.: An artificial intelligence-based collaboration approach in industrial IoT manufacturing: key concepts. Archit. Extens. Potent. Appl. Sensors 20(19), 5480 (2020). https://doi.org/10.3390/s20195480

    Article  Google Scholar 

  85. Dong, J., Ye, C.: Research on collaborative optimisation of green manufacturing in semiconductor wafer distributed heterogeneous factory. Appl. Sci. 9(14), 2879 (2019). https://doi.org/10.3390/app9142879

    Article  Google Scholar 

  86. Kong, M., Pei, J., Liu, X., Lai, P.-C., Pardalos, P.M.: Green manufacturing: order acceptance and scheduling subject to the budgets of energy consumption and machine launch. J. Clean. Prod. (2019). https://doi.org/10.1016/j.jclepro.2019.119300

    Article  Google Scholar 

  87. Meng, K., Lou, P., Peng, X., Prybutok, V.: An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products. Int. J. Prod. Res. 54(18), 5567–5593 (2016). https://doi.org/10.1080/00207543.2016.1176263

    Article  Google Scholar 

  88. Sathish, T.: Modeling of support vector mechanism for green manufacturability quantification of production. Adv. Manuf. Technol. (2019). https://doi.org/10.1007/978-981-13-6374-0_45

    Article  Google Scholar 

  89. Park, J.-K., Kwon, B.-K., Park, J.-H., Kang, D.-J.: Machine learning-based imaging system for surface defect inspection. Int. J. Precis. Eng. Manuf.-Green Technol. 3(3), 303–310 (2016). https://doi.org/10.1007/s40684-016-0039-x

    Article  Google Scholar 

  90. Siew, J.P., Low, H.C., Teoh, P.C.: An interactive mobile learning application using machine learning framework in a flexible manufacturing environment. Int. J. Mobile Learn. Organ. 10(1/2), 1 (2016). https://doi.org/10.1504/ijmlo.2016.076187

    Article  Google Scholar 

  91. Syafrudin, M., Alfian, G., Fitriyani, N., Rhee, J.: Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18(9), 2946 (2018). https://doi.org/10.3390/s18092946

    Article  Google Scholar 

  92. Tayal, A., Solanki, A., Singh, S.P.: Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102383

    Article  Google Scholar 

  93. Gobert, C., Reutzel, E.W., Petrich, J., Nassar, A.R., Phoha, S.: Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit. Manuf. 21, 517–528 (2018). https://doi.org/10.1016/j.addma.2018.04.005

    Article  Google Scholar 

  94. Zhang, W., Mehta, A., Desai, P.S., Higgs, C.: Machine learning enabled powder spreading process map for metal additive manufacturing (AM). In: Int. Solid Free Form Fabr. Symp. Austin, TX (pp. 1235–1249) (2017)

  95. Kolokas, N., Vafeiadis, T., Ioannidis, D., Tzovaras, D.: Fault prognostics in industrial domains using unsupervised machine learning classifiers. Simul. Model. Pract. Theory (2020). https://doi.org/10.1016/j.simpat.2020.102109

    Article  Google Scholar 

  96. Liu, C., Roux, L.L., Ji, Z., Kerfriden, P., Lacan, F., Bigot, S.: Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing. Procedia Comput. Sci. 176, 2586–2595 (2020). https://doi.org/10.1016/j.procs.2020.09.314

    Article  Google Scholar 

  97. Kim, J.S., Lee, C.S., Kim, S.-M., Lee, S.W.: Development of data-driven in-situ monitoring and diagnosis system of fused deposition modeling (FDM) process based on support vector machine algorithm. Int. J. Precis. Eng. Manuf.-Green Technol. 5(4), 479–486 (2018). https://doi.org/10.1007/s40684-018-0051-4

    Article  Google Scholar 

  98. Kiangala, K.S., Wang, Z.: An adaptive framework for configuration of parameters in an Industry 4.0 manufacturing SCADA system by merging machine learning techniques. 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). https://doi.org/10.1109/icabcd49160.2020.9183818 (2020)

  99. González, C., Fernández-León, J.: A machine learning model to detect flow disturbances during manufacturing of composites by liquid moulding. J. Compos. Sci. 4(2), 71 (2020). https://doi.org/10.3390/jcs4020071

    Article  Google Scholar 

  100. Thiede, S., Turetskyy, A., Loellhoeffel, T., Kwade, A., Kara, S., Herrmann, C.: Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production. CIRP Ann. (2020). https://doi.org/10.1016/j.cirp.2020.04.090

    Article  Google Scholar 

  101. Yang, Z., Lu, Y., Yeung, H., Krishnamurty, S.: Investigation of deep learning for real-time melt pool classification in additive manufacturing. In 2019 IEEE 15th International Conference on Automation Science and Engineering (Case) (pp. 640–647). IEEE (2019)

  102. Brito, T., Queiroz, J., Piardi, L., Fernandes, L.A., Lima, J., Leitão, P.: A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems. Procedia Manuf. 51, 11–18 (2020). https://doi.org/10.1016/j.promfg.2020.10.003

    Article  Google Scholar 

  103. Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Do Noh, S.: Smart manufacturing: Past research, present findings, and future directions. Int. J. Precis. Eng. Manuf.-Green Technol. 3(1), 111–128 (2016)

    Article  Google Scholar 

  104. Kappes, B., Moorthy, S., Drake, D., Geerlings, H., Stebner, A.: Machine learning to optimise additive manufacturing parameters for laser powder bed fusion of Inconel 718. In: Proceedings of the 9th International Symposium on Superalloy 718 & Derivatives: Energy, Aerospace, and Industrial Applications (pp. 595–610). Springer, Cham (2018)

  105. Donegan, S.P., Schwalbach, E.J., Groeber, M.A.: Zoning additive manufacturing process histories using unsupervised machine learning. Mater. Char. (2020). https://doi.org/10.1016/j.matchar.2020.110123

    Article  Google Scholar 

  106. Bär, S., Turner, D., Mohanty, P.K., Samsonov, V., Bakakeu, J.R., Meisen, T.: Multi Agent Deep Q-Network Approach for Online Job Shop Scheduling in Flexible Manufacturing.

  107. Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D.: Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23–45 (2016). https://doi.org/10.1080/21693277.2016.1192517

    Article  Google Scholar 

  108. Wan, J., Chen, B., Imran, M., Tao, F., Li, D., Liu, C., Ahmad, S.: Toward dynamic resources management for IoT-based manufacturing. IEEE Commun. Mag. 56(2), 52–59 (2018). https://doi.org/10.1109/mcom.2018.1700629

    Article  Google Scholar 

  109. Nishijima, T. (2021). Universal Approximation Theorem for Neural Networks. ar**v preprint ar**v:2102.10993.

  110. Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021)

    Article  Google Scholar 

  111. Lu, F., Zhou, G., Zhang, C.: Tool geometries optimization based on machine learning for aviation parts towards green manufacturing. In: 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), vol. 2. IEEE (2021)

  112. Turusha, G, Atharva, J., Samgeeth, S., et al.: Blockchain in additive manufacturing processes: Recent trends & its future possibilities. Mater. Today Proc. 5, 2170-2180 (2022). https://doi.org/10.1016/j.matpr.2021.09.444

  113. Dhruvil, P., Kunal, K., Shashank, S., et al.: Machine learning-based CFD simulations: A review, models, open threats, and future tactics. Neural Comput. Appl. 34, 21677–21700 (2022). https://doi.org/10.1007/s00521-022-07838-6

  114. Isha, S., Sivasubramani, R., Utkarsh, C., et al.: Computational AI models in VAT photopolymerization: a review, current trends, open issues, and future opportunities. Neural Comput. Appl. 34, 17207–17229 (2022). https://doi.org/10.1007/s00521-022-07694-4

  115. Utkarsh, C., Aarye, A., Naman, P.V., et al.: Performance evaluation of 3D printing technologies: a review, recent advances, current challenges, and future directions. Prog. Addit. Manuf 7, 853–886 (2022). https://doi.org/10.1007/s40964-021-00257-4

  116. Chadha U., Selvaraj S.K., Lasalle A.K., Maddini Y., Ravinuthala A., Choudhary B., Mishra A., Padala D., Shashank M., Lahti V., Adefris A., Dhanalakshmi S. "Directed Energy Deposition via Artificial Intelligence-Enabled Approaches", Complexity, vol. 2022, Article ID 2767371, 32 pages, 2022. https://doi.org/10.1155/2022/2767371

  117. Mayank. K., Utkarsh, C., Anirban B., et al.: Feasibility of friction stir welding for in-space joining processes: A simulation-based experimentation. Int. J. Interact. Des. Manuf. (IJIDeM) (2022). https://doi.org/10.1007/s12008-022-01055-0

  118. Priyanshi, G., Chaitanya, K., Rahul, R.: Industrial internet of things in intelligent manufacturing: a review, approaches, opportunities, open challenges, and future directions. Int. J. Interact. Des. Manuf. (IJIDeM) (2022). https://doi.org/10.1007/s12008-022-01075-w

  119. Akshay P.M., Vedang, L., Neha, G.: Quality control tools and digitalization of real-time data in sustainable manufacturing. Int. J. Interact. Des. Manuf. (IJIDeM) (2022). https://doi.org/10.1007/s12008-022-01054-1

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering (SITE), Vellore Institute of Technology (VIT), Vellore, Tamilnadu, India

    Aditya Raj

  2. School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamilnadu, India

    Amogh Gyaneshwar, Adi Asija, Ayushma Joshi & Kashyap Bastola

  3. School of Mechanical Engineering (SMEC), Vellore Institute of Technology (VIT), Vellore, Tamilnadu, India

    Utkarsh Chadha, Aarye Abrol, Sharad Priya, Arbab Alam & Senthil Kumaran Selvaraj

  4. Department of Materials Science and Engineering, Faculty of Applied Sciences and Engineering, School of Graduate Studies, University of Toronto, Toronto, ON, M5S 2Z9, Canada

    Utkarsh Chadha

  5. Prakash Higher Secondary School, Ahmedabad, India

    Arisha Chadha

  6. School of Civil Engineering, SASTRA Deemed to Be University, Thanjavur, 613 401, India

    B. Karthikeyan

  7. Department of Computer Science and Engineering, Kakinada Institute of Technological Sciences, Ramachandrapuram, Andhra Pradesh, 533255, India

    R. Nagalakshmi

  8. Plano East High School, Plano, USA

    Vishjit Chandramohan

  9. Department of Mechanical Engineering, College of Engineering, Jazan University, P. O. Box 706, 45142, Jazan, Kingdom of Saudi Arabia

    Haitham Hadidi

Authors
  1. Aditya Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amogh Gyaneshwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Utkarsh Chadha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arisha Chadha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adi Asija
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Aarye Abrol
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sharad Priya
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Arbab Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ayushma Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kashyap Bastola
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Senthil Kumaran Selvaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. B. Karthikeyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. R. Nagalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Vishjit Chandramohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Haitham Hadidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Senthil Kumaran Selvaraj.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A

Appendix A

  • GM: Green Manufacturing

  • GMS: Green Manufacturing Systems

  • LCA: Life Cycle Assessment

  • LM: Lean Manufacturing

  • LGMS: Lean-Green Manufacturing Systems

  • PLA: Polylactic Acid

  • SME: Small and Medium-sized Enterprises

  • QFD: Quality Function Deployment

  • OEEE: Overall Environmental Equipment Effectiveness

  • GSCM: Green Supply Chain Management

  • ISM: Interpretive Structural Modelling

  • SSIM: Structured Self Intersection Matrix

  • ANN: Artificial Neural Network

  • EDM: Electric Discharge Machine

  • LEED: Leadership in Energy and Environment Design

  • EPI: Energy Performance Indicator

  • PAT: Process Analytical Technologies

  • QBD: Quality By Design

  • AHP: Analytical Hierarchy Process

  • DEMATEL: Decision Making Trial and Evaluation Laboratory

  • TOPSIS: The Technique for Order of Preference by Similarity to Ideal Solution

  • AM: Additive manufacturing

  • CNN: Convolutional neural network

  • CPS: Cyber-physical systems

  • DEA: Data envelope analysis

  • EOL: End of life

  • ICA: Improved coevolutionary algorithm

  • IMOGWO: Improved Multi-Objective Grey Wolf Optimizer

  • MOGWO: Multi-objective grey wolf optimiser

  • IoT: Internet of things

  • LBF: Laser bed fusion

  • MPRD: Mean Paired Relative Difference

  • MVNS: Modified variable neighbourhood search

  • NSGA-II: Nondominated sorting genetic algorithm II

  • RFN: Random forests models

  • RHFS: Re-entrant hybrid flow shop problem

  • RL: Reinforcement learning

  • SVM: Support vector machine

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raj, A., Gyaneshwar, A., Chadha, U. et al. Green manufacturing via machine learning enabled approaches. Int J Interact Des Manuf (2022). https://doi.org/10.1007/s12008-022-01136-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12008-022-01136-0

Keywords

Search

Navigation