Skip to main content
SpringerLink
Log in

Artificial Intelligence in Manufacturing

  • Chapter
  • First Online:
Artificial Intelligence and Lean Manufacturing

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

The definition of artificial intelligence (AI) is undetermined.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. K.D. Pandl, S. Thiebes, M. Schmidt-Kraepelin, A. Sunyaev, On the convergence of artificial intelligence and distributed ledger technology: a scoping review and future research agenda. IEEE Access 8, 57075–57095 (2020)

    Article  Google Scholar 

  2. P. Perico, J. Mattioli, Empowering process and control in lean 4.0 with artificial intelligence, in Third International Conference on Artificial Intelligence for Industries (2020), pp. 6–9

    Google Scholar 

  3. C. Labreuche, S. Fossier, Explaining multi-criteria decision aiding models with an extended Shapley value, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (2018), pp. 331–339

    Google Scholar 

  4. D. Devereaux, Smaller manufacturers get lean with artificial intelligence (2019). http://www.nist.gov/blogs/manufacturing-innovation-blog/smaller-manufacturers-get-leanartificial-intelligence

  5. Y. Sun, L. Li, H. Shi, D. Chong, The transformation and upgrade of China’s manufacturing industry in Industry 4.0 era. Syst. Res. Behav. Sci. 37(4), 734–740 (2020)

    Google Scholar 

  6. P. Palensky, D. Bruckner, A. Tmej, T. Deutsch, Paradox in AI–AI 2.0: the way to machine consciousness, in International Conference on IT Revolutions (2008), pp. 194–215

    Google Scholar 

  7. Y.H. Pan, Heading toward artificial intelligence 2.0. Engineering 2(4), 409–413 (2016)

    Google Scholar 

  8. P.J. Lisboa, AI 2.0: Augmented intelligence, data science and knowledge engineering for sensing decision support, in Proceedings of the 13th International FLINS Conference (2018), pp. 10–11

    Google Scholar 

  9. B.H. Li, B.C. Hou, W.T. Yu, X.B. Lu, C.W. Yang, Applications of artificial intelligence in intelligent manufacturing: a review. Front. Inform. Technol. Electron. Eng. 18(1), 86–96 (2017)

    Article  Google Scholar 

  10. A. Manghani, A primer on machine learning (2017). https://ce.uci.edu/pdfs/certificates/machine_learning_article.pdf

  11. IBM, Supervised learning (2022). https://www.ibm.com/cloud/learn/supervised-learning

  12. JavaTpoint, Unsupervised machine learning (2022). https://www.javatpoint.com/unsupervised-machine-learning

  13. B. Dickson, What is semi-supervised machine learning? (2021). https://bdtechtalks.com/2021/01/04/semi-supervised-machine-learning/

  14. B. Osiński, K. Budek, What is reinforcement learning? The complete guide (2018). https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/

  15. T. Wuest, D. Weimer, C. Irgens, K.D. Thoben, Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23–45 (2016)

    Google Scholar 

  16. L. Haldurai, T. Madhubala, R. Rajalakshmi, A study on genetic algorithm and its applications. Int. J. Comput. Sci. Eng. 4(10), 139 (2016)

    Google Scholar 

  17. D. Graupe, Principles of Artificial Neural Networks, vol. 7 (World Scientific, 2013)

    Google Scholar 

  18. J. Mockus, Bayesian Approach to Global Optimization: Theory and Applications, vol. 37 (Springer Science & Business Media, 2012)

    Google Scholar 

  19. H.C. Wu, T. Chen, CART–BPN approach for estimating cycle time in wafer fabrication. J. Ambient. Intell. Humaniz. Comput. 6(1), 57–67 (2015)

    Article  Google Scholar 

  20. C. Wang, X.P. Tan, S.B. Tor, C.S. Lim, Machine learning in additive manufacturing: State-of-the-art and perspectives. Addit. Manuf. 36, 101538 (2020)

    Google Scholar 

  21. S.C.H. Lu, D. Ramaswamy, P.R. Kumar, Efficient scheduling policies to reduce mean and variation of cycle time in semiconductor manufacturing plant. IEEE Trans. Semicond. Manuf. 7(3), 374–388 (1994)

    Article  Google Scholar 

  22. T.C. Chen, Y.C. Wang, Y.C. Lin, A fuzzy-neural system for scheduling a wafer fabrication factory. Int. J. Innov. Comput. Inform. Control 6(2), 687–700 (2010)

    Google Scholar 

  23. A. Amindoust, S. Ahmed, A. Saghafinia, A. Bahreininejad, Sustainable supplier selection: a ranking model based on fuzzy inference system. Appl. Soft Comput. 12(6), 1668–1677 (2012)

    Article  Google Scholar 

  24. T. Madhusudan, J.L. Zhao, B. Marshall, A case-based reasoning framework for workflow model management. Data Knowl. Eng. 50(1), 87–115 (2004)

    Article  Google Scholar 

  25. A. González-Briones, J. Prieto, F. De La Prieta, E. Herrera-Viedma, J.M. Corchado, Energy optimization using a case-based reasoning strategy. Sensors 18(3), 865 (2018)

    Article  Google Scholar 

  26. J. Lim, M.J. Chae, Y. Yang, I.B. Park, J. Lee, J. Park, Fast scheduling of semiconductor manufacturing facilities using case-based reasoning. IEEE Trans. Semicond. Manuf. 29(1), 22–32 (2015)

    Article  Google Scholar 

  27. P.C. Chang, J.C. Hsieh, T.W. Liao, A case-based reasoning approach for due-date assignment in a wafer fabrication factory, in International Conference on Case-Based Reasoning (2001), pp. 648–659

    Google Scholar 

  28. S. Shigeo, A.P. Dillon. A Revolution in Manufacturing: The SMED System (Routledge, 2019)

    Google Scholar 

  29. R.J. Kuo, L.M. Lin, Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decis. Support Syst. 49(4), 451–462 (2010)

    Article  Google Scholar 

  30. T. Chen, C.W. Lin, Smart and automation technologies for ensuring the long-term operation of a factory amid the COVID-19 pandemic: an evolving fuzzy assessment approach. Int. J. Adv. Manuf. Technol. 111(11), 3545–3558 (2020)

    Article  Google Scholar 

  31. H. Kurniawan, T.D. Sofianti, A.T. Pratama, P.I. Tanaya, Optimizing production scheduling using genetic algorithm in textile factory. J. Syst. Manage. Sci. 4(4), 27–44 (2014)

    Google Scholar 

  32. Y.Y. Hong, P.S. Yo, Novel genetic algorithm-based energy management in a factory power system considering uncertain photovoltaic energies. Appl. Sci. 7(5), 438 (2017)

    Article  Google Scholar 

  33. T. Chen, Estimating unit cost using agent-based fuzzy collaborative intelligence approach with entropy-consensus. Appl. Soft Comput. 73, 884–897 (2018)

    Article  Google Scholar 

  34. T. Chen, Y.C. Lin, A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting. Internat. J. Uncertain. Fuzziness Knowl.-Based Syst. 16(01), 35–58 (2008)

    Article  Google Scholar 

  35. T.C.T. Chen, Y.C. Wang, Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant. Complex Intell. Syst. 7(4), 2141–2154 (2021)

    Article  Google Scholar 

  36. J. Wang, J. Zhang, X. Wang, Bilateral LSTM: A two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Trans. Indus. Inform. 14(2), 748–758 (2017)

    Article  Google Scholar 

  37. G. Montavon, W. Samek, K.R. Müller, Methods for interpreting and understanding deep neural networks. Digit Signal Process 73, 1–15 (2018)

    Article  MathSciNet  Google Scholar 

  38. E. Alhoniemi, J. Hollmén, O. Simula, J. Vesanto, Process monitoring and modeling using the self-organizing map. Integr. Comput. Aided Eng. 6(1), 3–14 (1999)

    Article  Google Scholar 

  39. L.B. Fazlic, Z. Avdagic, I. Besic, GA-ANFIS expert system prototype for detection of tar content in the manufacturing process, in 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (2015), pp. 1194–1199

    Google Scholar 

  40. J. Moyne, J. Samantaray, M. Armacost, Big data capabilities applied to semiconductor manufacturing advanced process control. IEEE Trans. Semicond. Manuf. 29(4), 283–291 (2016)

    Article  Google Scholar 

  41. IBM Cloud Education, Convolutional neural networks (2020). https://www.ibm.com/cloud/learn/convolutional-neural-networks

  42. B. Jones, I. Jenkinson, Z. Yang, J. Wang, The use of Bayesian network modelling for maintenance planning in a manufacturing industry. Reliab. Eng. Syst. Saf. 95(3), 267–277 (2010)

    Article  Google Scholar 

  43. J. Lee, J. Son, S. Zhou, Y. Chen, Variation source identification in manufacturing processes using Bayesian approach with sparse variance components prior. IEEE Trans. Autom. Sci. Eng. 17(3), 1469–1485 (2020)

    Google Scholar 

  44. L. Yang, J. Lee, Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems. Robot. Comput.-Integr. Manuf. 28(1), 66–74 (2012)

    Article  Google Scholar 

  45. T. Chen, A fuzzy-neural DBD approach for job scheduling in a wafer fabrication factory. Int. J. Innov. Comput. Inform. Control 8(6), 4024–4044 (2012)

    Google Scholar 

  46. T. Chen, Y.C. Wang, H.C. Wu, A fuzzy-neural approach for remaining cycle time estimation in a semiconductor manufacturing factory—a simulation study. Int. J. Innov. Comput. Inform. Control 5(8), 2125–2139 (2009)

    Google Scholar 

  47. T. Chen, Y.C. Wang, Y.C. Lin, A bi-criteria four-factor fluctuation smoothing rule for scheduling jobs in a wafer fabrication factory. Int. J. Innov. Comput. Inform. Control 6(10), 4289–4304 (2009)

    Google Scholar 

  48. T.C.T. Chen, Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system. AI EDAM 33(4), 458–468 (2019)

    Google Scholar 

  49. Y.C. Wang, M.C. Chiu, T. Chen, A fuzzy nonlinear programming approach for planning energy-efficient wafer fabrication factories. Appl. Soft Comput. 95, 106506 (2020)

    Article  Google Scholar 

  50. H. Kodama, A scheme for three-dimensional display by automatic fabrication of three-dimensional model. IEICE Trans. Electron. J. 64-C(4), 237–241 (1981)

    Google Scholar 

  51. T.C.T. Chen, Y.C. Lin, A three-dimensional-printing-based agile and ubiquitous additive manufacturing system. Robot. Comput.-Integr. Manuf. 55, 88–95 (2019)

    Article  Google Scholar 

  52. A.H. Espera, J.R.C. Dizon, Q. Chen, R.C. Advincula, 3D-printing and advanced manufacturing for electronics. Progress Additive Manuf. 4(3), 245–267 (2019)

    Article  Google Scholar 

  53. Q. Ge, A.H. Sakhaei, H. Lee, C.K. Dunn, N.X. Fang, M.L. Dunn, Multimaterial 4D printing with tailorable shape memory polymers. Sci. Rep. 6(1), 1–11 (2016)

    Article  Google Scholar 

  54. T. Yiu, Understanding random forest (2019). https://towardsdatascience.com/understanding-random-forest-58381e0602d2

  55. V.E. Sathishkumar, M. Lee, J. Lim, Y. Kim, C. Shin, J. Park, Y. Cho, An energy consumption prediction model for smart factory using data mining algorithms. KIPS Trans. Softw. Data Eng. 9(5), 153–160 (2020)

    Google Scholar 

  56. K. Liu, X. Hu, H. Zhou, L. Tong, W.D. Widanage, J. Marco, Feature analyses and modeling of lithium-ion battery manufacturing based on random forest classification. IEEE/ASME Trans. Mechatron. 26(6), 2944–2955 (2021)

    Article  Google Scholar 

  57. M.L. George Sr, D.K. Blackwell, D. Rajan, Lean Six Sigma in the Age of Artificial Intelligence: Harnessing the Power of the Fourth Industrial Revolution (McGraw-Hill Education, 2019)

    Google Scholar 

  58. A. Susilawati, J. Tan, D. Bell, M. Sarwar, Fuzzy logic based method to measure degree of lean activity in manufacturing industry. J. Manuf. Syst. 34, 1–11 (2015)

    Article  Google Scholar 

  59. A. Popa, R. Ramos, A.B. Cover, C.G. Popa, Integration of artificial intelligence and lean sigma for large field production optimization: Application to Kern River Field, in SPE Annual Technical Conference and Exhibition (2005)

    Google Scholar 

  60. K. Antosz, L. Pasko, A. Gola, The use of artificial intelligence methods to assess the effectiveness of lean maintenance concept implementation in manufacturing enterprises. Appl. Sci. 10(21), 7922 (2020)

    Article  Google Scholar 

  61. T. Küfner, T.H.J. Uhlemann, B. Ziegler, Lean data in manufacturing systems: Using artificial intelligence for decentralized data reduction and information extraction. Procedia CIRP 72, 219–224 (2018)

    Article  Google Scholar 

  62. S. Vahabi Nejat, S. Avakh Darestani, M. Omidvari, M.A. Adibi, Evaluation of green lean production in textile industry: a hybrid fuzzy decision-making framework. Environ. Sci. Pollut. Res. 29(8), 11590–11611 (2022)

    Google Scholar 

  63. A. Alinezhad, J. Khalili, COPRAS method. Internat. Ser. Oper. Res. Manage. Sci. 277, 87–91 (2019)

    Google Scholar 

  64. G. Ante, F. Facchini, G. Mossa, S. Digiesi, Developing a key performance indicators tree for lean and smart production systems. IFAC-PapersOnLine 51(11), 13–18 (2018)

    Article  Google Scholar 

  65. E. Pourjavad, R.V. Mayorga, A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system. J. Intell. Manuf. 30(3), 1085–1097 (2019)

    Article  Google Scholar 

  66. M.A. Almomani, M. Aladeemy, A. Abdelhadi, A. Mumani, A proposed approach for setup time reduction through integrating conventional SMED method with multiple criteria decision-making techniques. Comput. Ind. Eng. 66(2), 461–469 (2013)

    Article  Google Scholar 

  67. K. Maniya, M.G. Bhatt, A selection of material using a novel type decision-making method: preference selection index method. Mater. Des. 31(4), 1785–1789 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Tin-Chih Toly Chen

  2. Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan

    Yi-Chi Wang

Authors
  1. Tin-Chih Toly Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi-Chi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tin-Chih Toly Chen .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, TC.T., Wang, YC. (2022). Artificial Intelligence in Manufacturing. In: Artificial Intelligence and Lean Manufacturing. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-04583-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04583-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04582-0

  • Online ISBN: 978-3-031-04583-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation