Skip to main content
Top

2023 | OriginalPaper | Chapter

2. Künstliche Intelligenz in der Fertigung

Authors : Tin-Chih Toly Chen, Yi-Chi Wang

Published in: Künstliche Intelligenz und schlanke Produktion

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Zusammenfassung

Die Definition von künstlicher Intelligenz (KI) ist unbestimmt. Mit der Entwicklung von Computer-, Netzwerk- und Sensortechnologien wird sich die Bedeutung von KI weiterhin ändern [1]. Perico und Mattioli [2] haben KI-Technologien in zwei Kategorien unterteilt:
  • Datengetriebene KI (d. h., gehirnähnliches Lernen), einschließlich künstlicher neuronaler Netzwerke, maschinelles Lernen (überwachtes Lernen, unüberwachtes Lernen, statistisches Lernen), evolutionäres Rechnen, unscharfe Logik usw. Datengetriebene KI wird oft im Kontext von Mustererkennung, Klassifizierung, Clustering oder Wahrnehmung verwendet.
  • Symbolische KI (d. h., Modellierung und Wissensschlussfolgerung), einschließlich Ontologie, semantische Graphen, wissensbasierte Systeme, Schlussfolgerungen usw. Multikriterielle Entscheidungsfindung, Produktionsplanung und Auftragsplanung sind typische Anwendungen dieser Kategorie.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference 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)CrossRef 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)CrossRef
2.
go back to reference 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), S. 6–9 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), S. 6–9
3.
go back to reference 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), S. 331–339 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), S. 331–339
5.
go back to reference 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) 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)
6.
go back to reference 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), S. 194–215 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), S. 194–215
7.
go back to reference Y.H. Pan, Heading toward artificial intelligence 2.0. Engineering 2(4), 409–413 (2016) Y.H. Pan, Heading toward artificial intelligence 2.0. Engineering 2(4), 409–413 (2016)
8.
go back to reference 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), S. 10–11 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), S. 10–11
9.
go back to reference 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)CrossRef 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)CrossRef
15.
go back to reference 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) 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)
16.
go back to reference L. Haldurai, T. Madhubala, R. Rajalakshmi, A study on genetic algorithm and its applications. Int. J. Comput. Sci. Eng. 4(10), 139 (2016) L. Haldurai, T. Madhubala, R. Rajalakshmi, A study on genetic algorithm and its applications. Int. J. Comput. Sci. Eng. 4(10), 139 (2016)
17.
go back to reference D. Graupe, Principles of Artificial Neural Networks, vol. 7 (World Scientific, 2013) D. Graupe, Principles of Artificial Neural Networks, vol. 7 (World Scientific, 2013)
18.
go back to reference J. Mockus, Bayesian Approach to Global Optimization: Theory and Applications, Bd. 37 (Springer Science & Business Media, 2012) J. Mockus, Bayesian Approach to Global Optimization: Theory and Applications, Bd. 37 (Springer Science & Business Media, 2012)
19.
go back to reference 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)CrossRef 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)CrossRef
20.
go back to reference 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) 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)
21.
go back to reference 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)CrossRef 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)CrossRef
22.
go back to reference 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) 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)
23.
go back to reference 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)CrossRef 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)CrossRef
24.
go back to reference T. Madhusudan, J.L. Zhao, B. Marshall, A case-based reasoning framework for workflow model management. Data Knowl. Eng. 50(1), 87–115 (2004)CrossRef T. Madhusudan, J.L. Zhao, B. Marshall, A case-based reasoning framework for workflow model management. Data Knowl. Eng. 50(1), 87–115 (2004)CrossRef
25.
go back to reference 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)CrossRef 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)CrossRef
26.
go back to reference 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)CrossRef 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)CrossRef
27.
go back to reference 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), S. 648–659 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), S. 648–659
28.
go back to reference S. Shigeo, A.P. Dillon. A Revolution in Manufacturing: The SMED System (Routledge, 2019) S. Shigeo, A.P. Dillon. A Revolution in Manufacturing: The SMED System (Routledge, 2019)
29.
go back to reference 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)CrossRef 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)CrossRef
30.
go back to reference 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)CrossRef 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)CrossRef
31.
go back to reference 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) 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)
32.
go back to reference 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)CrossRef 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)CrossRef
33.
go back to reference T. Chen, Estimating unit cost using agent-based fuzzy collaborative intelligence approach with entropy-consensus. Appl. Soft Comput. 73, 884–897 (2018)CrossRef T. Chen, Estimating unit cost using agent-based fuzzy collaborative intelligence approach with entropy-consensus. Appl. Soft Comput. 73, 884–897 (2018)CrossRef
34.
go back to reference 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)CrossRef 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)CrossRef
35.
go back to reference 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)CrossRef 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)CrossRef
36.
go back to reference 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)CrossRef 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)CrossRef
37.
go back to reference G. Montavon, W. Samek, K.R. Müller, Methods for interpreting and understanding deep neural networks. Digit Signal Process 73, 1–15 (2018)MathSciNetCrossRef G. Montavon, W. Samek, K.R. Müller, Methods for interpreting and understanding deep neural networks. Digit Signal Process 73, 1–15 (2018)MathSciNetCrossRef
38.
go back to reference 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)CrossRef 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)CrossRef
39.
go back to reference 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), S. 1194–1199 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), S. 1194–1199
40.
go back to reference 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)CrossRef 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)CrossRef
42.
go back to reference 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)CrossRef 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)CrossRef
43.
go back to reference 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) 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)
44.
go back to reference 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)CrossRef 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)CrossRef
45.
go back to reference 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) 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)
46.
go back to reference 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) 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)
47.
go back to reference 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) 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)
48.
go back to reference 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) 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)
49.
go back to reference 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)CrossRef 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)CrossRef
50.
go back to reference 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) 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)
51.
go back to reference 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)CrossRef 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)CrossRef
52.
go back to reference A.H. Espera, J.R.C. Dizon, Q. Chen, R.C. Advincula, 3D-printing and advanced manufacturing for electronics. Prog. Addit. Manuf. 4(3), 245–267 (2019)CrossRef A.H. Espera, J.R.C. Dizon, Q. Chen, R.C. Advincula, 3D-printing and advanced manufacturing for electronics. Prog. Addit. Manuf. 4(3), 245–267 (2019)CrossRef
53.
go back to reference 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)CrossRef 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)CrossRef
55.
go back to reference 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) 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)
56.
go back to reference 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)CrossRef 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)CrossRef
57.
go back to reference 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) 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)
58.
go back to reference 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)CrossRef 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)CrossRef
59.
go back to reference 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) 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)
60.
go back to reference 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)CrossRef 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)CrossRef
61.
go back to reference 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)CrossRef 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)CrossRef
62.
go back to reference 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) 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)
63.
go back to reference A. Alinezhad, J. Khalili, COPRAS method. Internat. Ser. Oper. Res. Manage. Sci. 277, 87–91 (2019) A. Alinezhad, J. Khalili, COPRAS method. Internat. Ser. Oper. Res. Manage. Sci. 277, 87–91 (2019)
64.
go back to reference 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)CrossRef 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)CrossRef
65.
go back to reference 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)CrossRef 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)CrossRef
66.
go back to reference 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)CrossRef 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)CrossRef
67.
go back to reference 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)CrossRef 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)CrossRef
Metadata
Title
Künstliche Intelligenz in der Fertigung
Authors
Tin-Chih Toly Chen
Yi-Chi Wang
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-44280-3_2

Premium Partners