Skip to main content
SpringerLink
Log in

Development of the Product State Concept

  • Chapter
  • First Online:
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Part of the book series: Springer Theses ((Springer Theses))

  • 1214 Accesses

Abstract

In this section, the product state concept and its development will be illustrated from a theoretical perspective. The main intension is to provide a general understanding of the goals and basic pillars of the concept and its argumentation. Another major goal of this section is to discuss and present the challenges and limitations to the application of the presented theoretical approach in practice. This outcome is crucial for the selection of appropriate methods and the following approach to identify state drivers despite the knowledge gap concerning process intra- and inter-relations using ML which will bring the product state concept to life.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2011a), Knoke et al. (2012).

  2. 2.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2011a, b, 2012b, 2013a).

  3. 3.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2011a, b), Wuest and Thoben (2012), Wuest et al. (2012b), Knoke et al. (2012).

  4. 4.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2012b), Knoke et al. (2012).

  5. 5.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2012b), Knoke et al. (2012), Wuest et al. (2014a).

  6. 6.

    The content of this section has been partly published in accordance with Universität Bremen (2007) in Wuest et al. (2013a).

References

  • Alpaydın, E. (2010). Introduction to machine learning (2nd ed.). Cambridge: The MIT Press.

    MATH  Google Scholar 

  • Anderl, R., Picard, A., & Albrecht, K. (2013). Smart product engineering. In M. Abramovici & R. Stark (Eds.), 23rd CIRP Design Conference (pp. 1–10). Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-30817-8.

    Google Scholar 

  • Angel, E., & Zissimopoulos, V. (1998). On the quality of local search for the quadratic assignment problem. Discrete Applied Mathematics, 82(1–3), 15–25. doi:10.1016/S0166-218X(97)00129-7.

    Article  MATH  MathSciNet  Google Scholar 

  • Apley, D., & Shi, J. (2001). A factor-analysis method for diagnosing variability in mulitvariate manufacturing processes. Technometrics, 43(1), 84–95.

    Article  MATH  MathSciNet  Google Scholar 

  • Aytug, H., Khouja, M., & Vergara, F. E. (2003). Use of genetic algorithms to solve production and operations management problems: A review. International Journal of Production Research, 41(17), 3955–4009. doi:10.1080/00207540310001626319.

    Article  Google Scholar 

  • Baker, A. D. (1988). Complete manufacturing control using a contract net: A simulation study. In International Conference on Computer Integrated Manufacturing, 1988 (pp. 100–109). doi:10.1109/CIM.1988.5399.

  • Batini, C., Ceri, S., & Navathe, S. B. (1992). Conceptual database design. An entity-relationship-approach. Redwood City: Addison Wesley.

    MATH  Google Scholar 

  • Becker, J. (1998). Die Grundsätze ordnungsmäßiger Modellierung und ihre Einbettung in ein Vorgehensmodell zur Erstellung betrieblicher Informationsmodelle. Retrieved April 15, 2012, from http://www.wi-inf.uni-duisburg-essen.de/MobisPortal/pages/rundbrief/pdf/Beck98.pdf.

  • Becker, J., & Schütte, R. (2004). Handelsinformationssysteme. Frankfurt am Main: Redline Wirtschaft.

    Google Scholar 

  • Borror, C., Montgomery, D., & Runger, G. (1999). Robustness of the EWMA control chart to non-normality. Journal of Quality Technology, 31(3), 309–316.

    Google Scholar 

  • Brinksmeier, E. (1991). Prozeß-und Werkstückqualität in der Feinbearbeitung. Fortschritt-Berichte VDI, Reihe 2: Fertigungstechnik (p. 256). Düsseldorf: VDI-Verlag.

    Google Scholar 

  • Brinksmeier, E., & Brockhoff, T. (1996). Utilization of grinding heat as a new heat treatment process. CIRP Annals—Manufacturing Technology, 45(1), 283–286. doi:10.1016/S0007-8506(07)63064-9.

    Article  Google Scholar 

  • Brockhoff, T. (1999). Grind-hardeing: A comprehensive view. CIRP—Manufacturing Technology, 48(1), 255–260.

    Google Scholar 

  • Brun, Y. (2008). Solving NP-complete problems in the tile assembly model. Theoretical Computer Science, 395(1), 31–46. doi:10.1016/j.tcs.2007.07.052.

    Google Scholar 

  • Chander, A., Dean, D., & Mitchell, J. C. (2001). A state-transition model of trust management and access control. In Proceedings of the 14th IEEE Computer Security Foundations Workshop, 2001, Ieee (pp. 27–43). doi:10.1109/CSFW.2001.930134.

  • Chen, J., Su, H., Wu, C., & Oh, C. H. (2012). Quantumness of product states. eprint arXiv:1204.1798.

    Google Scholar 

  • Chou, Y., Polansky, A., & Mason, R. (1998). Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133–141.

    Google Scholar 

  • Chryssolouris, G., & Guillot, M. (1988). An AI approach to the selection of process parameters in intelligent machining. Proceedings of the Winter Annual Meeting of the ASME on Sensors and Controls for Manufacturin. Chicago, Illinois.

    Google Scholar 

  • Cook, S. A. (1971). The complexity of theorem-proving procedures. In STOC ’71 Proceedings of the Third Annual ACM Symposium on Theory of computing (pp. 151–158). doi:10.1145/800157.805047.

  • Cooper, R. G. (2008). Perspective: The stage-gates idea-to-launch process—update, what’s new and NexGen systems. Journal of Product Innovation Management, 25(3), 213–232.

    Article  Google Scholar 

  • Cooper, R. G. (2010). Top oder Flop in der Produktentwicklung. 2. Auflage: Weinheim.

    Google Scholar 

  • Crama, Y., & Klundert, J. J. (1997). Robotic flowshop scheduling is strongly NP-complete. Maastricht: METEOR, Maastricht research school of Economics of Technology and Organizations.

    Google Scholar 

  • Denkena, B., & Tönshoff, H. K. (2011). Spanen. Grundlagen (3rd ed.). Heidelberg: Springer. doi:10.1007/978-3-642-19772-7.

    Google Scholar 

  • Dijkman, M. (2009). Automated compensation of distortion in the production process of bearing rings. Dissertation Universität Bremen, Verlagshaus Mainz GmbH, Aachen.

    Google Scholar 

  • Elmaraghy, W., Elmaraghy, H., Tomiyama, T., & Monostori, L. (2012). Complexity in engineering design and manufacturing. CIRP Annals—Manufacturing Technology, 61, 793–814.

    Article  Google Scholar 

  • Garetti, M., & Terzi, S. (2004). Product lifecycle management: Definition, trends and open issues. In Proceedings at the 3rd International Conference on Advances in Production Engineering, June 17–19, 2004. Warsaw, Poland.

    Google Scholar 

  • Geller, W. (2006). Thermodynamik für Maschinenbauer—Grundlagen für die Praxis. Heidelberg: Springer.

    Google Scholar 

  • Gogolla, M., & Parisi-Presicce, F. (1998). State diagrams in UML: A formal semantics using graph transformations. In M. Broy, D. Coleman, T. S. E. Maibaum, & B. Rumpe (Eds.), Proceedings PSMT’98 Workshop on Precise Semantics for Modeling Techniques. Technische Universität München, TUM-I9803.

    Google Scholar 

  • Goseva-Popstojanova, K., Wang, F., Wang, R., Gong, F., Vaidyanathan, K., Trivedi, K., & Muthusamy, B. (2001). Characterizing intrusion tolerant systems using a state transition model. In Proceedings of the DARPA Information Survivability Conference & Exposition II, 2001. DISCEX’01 (Vol. 2, pp. 211–221).

    Google Scholar 

  • Haegeman, J., Cirac, J. I., Osborne, T. J., & Verstraete, F. (2012). Calculus of continuous matrix product states. eprint arXiv:1211.3935. Retrieved from arXiv:1211.3935.

    Google Scholar 

  • Harding, J., Shahbaz, A., Srinivas, M., & Kusiak, A. (2006). Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering, 128(4), 969. doi:10.1115/1.2194554.

    Google Scholar 

  • Heinrich, L. J., Heinzl, A., & Roithmayr, F. (2007). Wirtschaftsinformatik: Einführung und Grundlegung. München: Oldenbourg Verlag.

    Google Scholar 

  • Heinecke, G., Lamparter, S., & Kunz, A. (2011). Process transparency: Effects of a structured read point selection. In Proceedings of the 21st International Conference on Production Research, Innovation in Product and Production, July 31–August 4, 2011. Stuttgart, Germany.

    Google Scholar 

  • Hüttig, G. F. (1943). Zur Systematik der Aggregatzustände. Colloid and Polymer Science, 104(2–3), 161–167.

    Google Scholar 

  • Kaiser, M. J. (1998). Generalized zone separation functionals for convex perfect forms and incomplete data sets. International Journal of Machine Tools and Manufacture, 38(4), 375–404. doi:10.1016/S0890-6955(97)00042-4.

    Article  Google Scholar 

  • Kalpakjian, S., & Schmid, S. R. (2009). Manufacturing engineering and technology. New Jersey: Prentice Hall.

    Google Scholar 

  • Kano, M., & Nakagawa, Y. (2008). Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry. Computers and Chemical Engineering, 32(1–2), 12–24. doi:10.1016/j.compchemeng.2007.07.005.

    Article  Google Scholar 

  • Keferstein, C. (2011). Fertigungsmesstechnik praxisorientierte Grundlagen, mo-derne Messverfahren. Wiesbaden: Vieweg+Teubner-Verlag.

    Google Scholar 

  • Kent, J. T. (1983). Information gain and a general measure of correlation. Biometrika, 70(1), 163–173. doi:10.1093/biomet/70.1.163.

    Article  MATH  MathSciNet  Google Scholar 

  • Knoke, B., Wuest, T., & Thoben, K.-D. (2012). Understanding product state relations within manufacturing processes. In C. Emmanouilidis, M. Taisch, & D. Kiritsis (Eds.), International Conference of Advances in Production Management Systems (APMS 2012)—Competitive Manufacturing for Innovative Products and Services. Berlin, Heidelberg: Springer.

    Google Scholar 

  • Kobler, M. (2010). Qualität von Prozessmodellen: Kennzahlen zur analytischen Qualitätssicherung bei der Prozessmodellierung. Berlin: Logos Verlag.

    Google Scholar 

  • König, W., & Klocke, F. (2008). Fertigungsverfahren Drehen, Fräsen, Bohren 8, neu (bearbeitete ed.). Berlin-Heidelberg: Springer.

    Google Scholar 

  • Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249–268.

    MATH  MathSciNet  Google Scholar 

  • Kumar, S. (2002). Intelligent manufacturing systems (pp. 1–20). Ranchi. Retrieved from http://pchats.tripod.com/int_manu.pdf.

  • Kwak, D.-S., & Kim, K.-J. (2012). A data mining approach considering missing values for the optimization of semiconductor-manufacturing processes. Expert Systems with Applications, 39(3), 2590–2596. doi:10.1016/j.eswa.2011.08.114.

    Article  MathSciNet  Google Scholar 

  • Laili, Y., Tao, F., Zhang, L, & Ren, L. (2011). The optimal allocation model of computing resources in cloud manufacturing system. Seventh International Conference on Natural Computation (pp. 2322–2326). doi:10.1109/ICNC.2011.6022564.

  • Larose, D. (2005). Discovering knowledge in data—an introduction to data mining. Hoboken: Wiley.

    Book  MATH  Google Scholar 

  • Lewis, F. L., Horne, B. G., & Abdallah, C. T. (1996). On the computational complexity of the manufacturing job shop and reentrant flow time (pp. 1–27). Retrieved July 10, 2013, from http://ise.unm.edu/controls/papers/on-the-computational-complexity.pdf.

  • Lu, S. C.-Y. (1990). Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation. Computers in Industry, 15(1990), 105–120.

    Article  Google Scholar 

  • Lu, S. C.-Y., & Suh, N.-P. (2009). Complexity in design of technical systems. CIRP Annals—Manufacturing Technology, 58(1), 157–160. doi:10.1016/j.cirp.2009.03.067.

    Article  Google Scholar 

  • Manning, C. D., Raghavan, P., & Schütze, H. (2009). An introduction to information retrieval. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Mayer-Bachmann, R. (2007). Integratives Anforderungsmanagement—Konzept und Anforderungsmodell am Beispiel der Fahrzeugentwicklung. Dissertation Universität Karlsruhe (TH), Universitätsverlag Karlsruhe, Karlsruhe.

    Google Scholar 

  • Mei, D. C., Xie, C. W., & Zhang, L. (2004). The stationary properties and the state transition of the tumor cell growth mode. The European Physical Journal B, 41(1), 107–112. doi:10.1140/epjb/e2004-00300-1.

    Article  Google Scholar 

  • Monostori, L. (2002). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. In 15th Triennial World Congress (p. 12). Barcelona, Spain.

    Google Scholar 

  • Monostori, L., Márkus, A., Van Brussel, H., & Westkämper, E. (1996). Machine learning approaches to manufacturing. CIRP Annals, 45(2), 675–712.

    Google Scholar 

  • Monostori, L., Hornyák, J., Egresits, C., & Viharos, Z. J. (1998). Soft computing and hybrid AI approaches to intelligent manufacturing. In Tasks and Methods in Applied Artificial Intelligence Lecture Notes in Computer Science (Vol. 1416, pp. 765–774). doi:10.1007/3-540-64574-8_463.

    Google Scholar 

  • Monostori, L., Váncza, J., & Kumara, S. R. T. (2006). Agent-based systems for manufacturing. CIRP Annals—Manufacturing Technology, 55(2), 697–720.

    Article  Google Scholar 

  • Musa, A., Gunasekaran, A., & Yusuf, Y. (2013). Supply chain product visibility: Methods, systems and impacts. Expert Systems with Applications. doi:10.1016/j.eswa.2013.07.020.

    Google Scholar 

  • Nearchou, A. C. (2011). Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization. International Journal of Production Economics, 129(2), 242–250. doi:10.1016/j.ijpe.2010.10.016.

    Google Scholar 

  • Nilsson, N. J. (2005). Introduction to machine learning (p. 188). Stanford: Stanford University Press.

    Google Scholar 

  • OMG. (2010). Business process model and notation (BPMN). Object management group. Retrieved October 4, 2012, from http://www.omg.org/spec/BPMN/2.0/PDF.

  • Pham, D. T., & Afify, A. A. (2005). Machine-learning techniques and their applications in manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 219(5), 395–412. doi:10.1243/095440505X32274.

    Article  Google Scholar 

  • Pearl, J. (2003). Causality: Models, reasoning, and inference. Econometric Theory, 19, 675–685. doi:10.1017/S0266466603004109.

  • Ponsignon, T., & Mönch, L. (2012). Heuristic approaches for master planning in semiconductor manufacturing. Computers and Operations Research, 39(3), 479–491. doi:10.1016/j.cor.2011.06.009.

    Google Scholar 

  • Reuter, M. (2007). Methodik der Werkstoffauswahl, Der systematische Weg zum richtigen Material. München: Carl Hanser-Verlag.

    Google Scholar 

  • Rohweder, J. P., Kasten, G., Malzahn, D., Piro, A., & Schmid, J. (2011). Informationsqualität—Definitionen, Dimensionen und Begriffe. In K. Hildebrand, M. Gebauer, H. Hinrichs, & M. Mielke (Eds.), Daten-und Informationsqualität (pp. 25–45). Wiesbaden: Vieweg+Teubner. doi:10.1007/978-3-8348-9953-8.

    Google Scholar 

  • Rosemann, M., & Schütte, R. (1997). Grundsätze ordnungsmäßiger Referenzmodellierung. In J. Becker, M. Rosemann, R. Schütte (Hrsg.), Entwicklungsstand und Entwicklungsperspektiven der Referenzmodellierung (16–33). Münster: Arbeitsberichte des Instituts für Wirtschaftsinformatik.

    Google Scholar 

  • Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal, 3(3), 210–229.

    Article  Google Scholar 

  • Sarich, M., Schutte, C., & Vanden-Eijden, E. (2010). Optimal fuzzy aggregation of networks. Multiscale Modeling and Simulation, 8(4), 1535–1561.

    Article  MATH  MathSciNet  Google Scholar 

  • Schatt, W., & Worch, H. (2003). Werkstoffwissenschaft 9, Auflage. Weinheim: Wiley.

    Google Scholar 

  • Schöning, U. (2001). Theoretische Informatik—kurzgefasst. Heidelberg, Berlin: Spektrum, Akademischer Verlag.

    Google Scholar 

  • Seidel, W. W., & Hahn, F. (2010). Werkstofftechnik 8, neu (bearbeitete ed.). München: Carl Hanser-Verlag.

    Google Scholar 

  • Shetwan, A. G., Vitanov, V. I., & Tjahjono, B. (2011). Allocation of quality control stations in multistage manufacturing systems. Computers and Industrial Engineering, 60(4), 473–484. doi:10.1016/j.cie.2010.12.022.

    Article  Google Scholar 

  • Simon, H. A. (1983). Why should machines learn? In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 25–38). Charlotte: Tioga Press.

    Google Scholar 

  • Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to machine learning. Cambridge: Cambridge University Press.

    Google Scholar 

  • Söhner, J. (2003). Beitrag zur Simulation zerspanungstechnologischer Vorgänge mit Hilfe der Finite-Elemente-Methode. Dissertation, Universität Karlsruhe.

    Google Scholar 

  • Sölter, J. (2010). Ursachen und Wirkmechanismen der Entstehung von Verzug infolge spanender Bearbeitung. Dissertation Universität Bremen, Shaker Verlag, Aachen.

    Google Scholar 

  • Sölter, J., & Brinksmeier, E. (2008). Modelling and simulation of ring deformation due to clamping. In International Conference on Distortion Engineering. September 17–19, 2008 (pp. 143–151). Bremen, Germany.

    Google Scholar 

  • Sonnenberg, F., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338. doi:10.1177/0272989X9301300409.

    Article  Google Scholar 

  • Srdoč, A., Bratko, I., & Sluga, A. (2007). Machine learning applied to quality management—a study in ship repair domain. Computers in Industry, 58(5), 464–473. doi:10.1016/j.compind.2006.09.013.

    Article  Google Scholar 

  • Stoumbos, Z., & Sullivan, J. (2002). Robustness to non-normality of the multivariate EWMA control chart. Journal of Quality Technology, 34(3), 260–276.

    Google Scholar 

  • Suh, N. P. (2005). Complexity in engineering. CIRP Annals—Manufacturing Technology, 54(2), 46–63. doi:10.1016/S0007-8506(07)60019-5.

    Article  Google Scholar 

  • Surm, H. (2011). Identifikation der verzugsbestimmenden Einflussgrößen beim Austenitisieren am Beispiel von Ringen aus dem Wälzlagerstahl 100Cr6. Dissertation, University of Bremen.

    Google Scholar 

  • Surm, H., & Rath, J. (2012). Mechanismen der Verzugsentstehung bei Wälzlagerringen aus 100Cr6 (Distortion Mechanisms in the Process Chain Bearing Ring). Journal of Heat Treatment Materials, 67(5), 1–13.

    Google Scholar 

  • Taisch, M., Cammarino, B. P., & Cassina, J. (2011). Life cycle data management: First step towards a new product lifecycle management standard. International Journal of Computer Integrated Manufacturing, 24(12), 1117–1135. doi:10.1080/0951192X.2011.608719.

    Article  Google Scholar 

  • Tiwari, V., Patterson, J. H., & Mabert, V. (2009). Scheduling projects with heterogeneous resources to meet time and quality objectives. European Journal of Operational Research, 193(3), 780–790. doi:10.1016/j.ejor.2007.11.005.

    Article  MATH  MathSciNet  Google Scholar 

  • Tönshoff, H. K., & Denkena, B. (2013). Basics of cutting and abrasive processes. Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-33257-9.

    Book  Google Scholar 

  • Udo, G. J. (1992). Neural networks applications in manufacturing processes. Computers and Industrial Engineering, 23(1–4), 97–100.

    Article  Google Scholar 

  • Universität Bremen. (2007). Promotionsordnung der Universität Bremen für die mathematischen, natur- und ingenieurwissenschaftlichen Fachbereiche vom 14. März 2007. Retrieved August 13, 2013, from http://www.math.uni-bremen.de/cms/media.php/59/PromO%20FB%202-5%20_14%203%2007_.6442.pdf.

  • Verstraete, F., Wolf, M. M., & Cirac, J. I. (2007). Matrix product state representations. Retrieved from http://arxiv.org/pdf/quant-ph/0608197.pdf.

  • Wang, K., & Tsung, F. (2007). Run-to-run process adjustment using categorical observations. Journal of Quality Technology, 39(4), 312–325.

    MathSciNet  Google Scholar 

  • Weisstein, E. W. (2011). Bipartite graph. Retrieved May 10, 2013, from http://mathworld.wolfram.com/BipartiteGraph.html.

  • Westkämper, E., & Warnecke, H. (2010). Einführung in die Fertigungstechnik (8th ed.). Wiesbaden: Vieweg+Teubner-Verlag.

    Book  Google Scholar 

  • Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574. doi:10.1016/j.ymssp.2006.12.007.

    Article  Google Scholar 

  • Wuest, T., Klein, D., & Thoben, K.-D. (2011a). State of steel products in industrial production processes. Procedia Engineering, 10(2011), 2220–2225.

    Google Scholar 

  • Wuest, T., Klein D., Seifert, M., & Thoben, K.-D. (2011b). Challenges for grown engineering SMEs with a diverse product portfolio on information management and product tracking and tracing. In J. Frick (Eds.), Value Networks: Innovation, Technologies and Management. Proceedings of the APMS 2011 International Conference of Advances in Production Management Systems, September 26–28, 2011. Stavanger, Norway. ISBN 978-82-7644-461-2.

    Google Scholar 

  • Wuest, T., & Thoben, K.-D. (2012a). Exploitation of material property potentials to reduce rare raw material waste—a product state based concept for manufacturing process improvement. Journal of Mining World Express (MWE), 1(1), 13–20.

    Google Scholar 

  • Wuest, T., Klein, D., Seifert, M., & Thoben, K.-D. (2012b). Method to describe interdependencies of state characteristics related to distortion. Materialwissenschaft und Werkstofftechnik, 43(1–2), 186–191. doi:10.1002/mawe.201100908.

    Google Scholar 

  • Wuest, T., Irgens, C., & Thoben, K.-D. (2013a). An approach to monitoring quality in manufacturing using supervised machine learning on product state data. Journal of Intelligent Manufacturing, 25(5), 1167–1180.

    Google Scholar 

  • Wuest, T., Werthmann, D., & Thoben, K. (2013b). Towards an approach to identify the optimal instant of time for information capturing in supply chains. In V. Prabhu, M. Taisch & D. Kiritsis (Eds.), APMS 2013, Part I, IFIP AICT 414, IFIP International Federation for Information Processing (pp. 3–12).

    Google Scholar 

  • Wuest, T., Knoke, B., & Thoben, K.-D. (2014a). Applying graph theory and the product state concept in manufacturing. Procedia Technology, 15(2014), 349-358. doi: 10.1016/j.protcy.2014.09.089.

    Google Scholar 

  • Wuest, T., Liu, A., Lu, S. C.-Y., & Thoben, K.-D. (2014b). Application of the stage gate model in production supporting quality management. Procedia CIRP, 17, 32-37. doi: 10.1016/j.procir.2014.01.071.

    Google Scholar 

  • Yang, K., & Trewn, J. (2004). Multivariate statistical methods in quality management. New York: McGraw-Hill.

    Google Scholar 

  • Zhang, J., & Wang, H. (2009). A minimized zero mean entropy approach to networked control systems. In Proceedings of the 48th IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, IEEE (pp. 6876–6881). Shanghai, China. doi:10.1109/CDC.2009.5400679.

  • Zoch, H.-W. (2012). Distortion engineering—interim results after one decade research within the collaborative research center. Materialwissenschaft und Werkstofftechnik, 43(1–2), 9–15. doi:10.1002/mawe.201100881.

    Article  Google Scholar 

  • Zoch, H.-W. & Lübben, T. (2011). Verzugsbeherrschung—Systemorientierter Ansatz als wesentliche Voraussetzung für den Erfolg. Stahl Strukturen. In W. Bleck (Ed.), Industrie-, Forschungs-, Mikro- und Bauteilstrukturen—Tagungsband zum 26. Aachener Stahlkolloquium, 19./20.05.2011. Verlagshaus Mainz, ISBN 3-86130-258-6.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ICT Applications for Production, BIBA—Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany

    Thorsten Wuest

  2. Department of Production Engineering, University of Bremen, Bremen, Germany

    Thorsten Wuest

Authors
  1. Thorsten Wuest
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thorsten Wuest .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Wuest, T. (2015). Development of the Product State Concept. In: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-17611-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17611-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17610-9

  • Online ISBN: 978-3-319-17611-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation