Abstract
Current manufacturing systems are incorporating digital extensions to meet the emerging trend of ‘smart’ manufacturing techniques. The underlying distinction of Smart Manufacturing Systems (SMS) is the digital network of several elements (machines, tools, auxiliary equipments, building services and people) that constitute these. The purpose of such a network is to enhance information transfer among these elements, as well as bolster the performance and efficiency of the roles that these elements are so entitled to. Such a network relies on the data collected from these elements; currently, both academicians and practitioners are delving into efficient ways to collect the data thus required. Although a plethora of models have been proposed for manufacturing systems, they seem to offer poor advice in regard to data collection from an overall system perspective. It is still not understood as to which elements should be focused and what are the efficient methods to collect data from these. To shed light on these gaps, we propose, herein, a new model for manufacturing systems, as illustrated by an orthotic shoe factory example.
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References
Davis, J. (2017). Smart Manufacturing. Encyclopedia Sustainable Technology pp. 417–427.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
Asthon, K. (2010). That ‘Internet of Things’ Thing. RFID Journal 4986.
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett., 3, 18–23.
Baheti, R., & Gill, H. (2011). Cyber-physical systems. Impact Control Technology, 12(1), 161–166.
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517–527.
Smart Manufacturing Leadership Coalition (SMLC). (2011). Implementing 21st Century Smart Manufacturing.
FoF. (2013). Impact of the factories of the future public-private partnership final report on the workshop held on.
April, W. G. (2013). “Final_report__Industrie_4.0_RecomForImplementation,” no. April.
Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing smart factory of industrie 4.0: An outlook. International Journal of Distributed Sensor Networks, 12(1), 3159805.
Siddharth, L., & Sarkar, P. (2018). A multiple-domain matrix support to capture rationale for engineering design changes. Journal of Computing and Information Science in Engineering, 18(2), 021014.
Esmaeilian, B., Behdad, S., & Wang, B. (2016). The evolution and future of manufacturing: A review. Journal of Manufacturing Systems, 39, 79–100.
Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, 40(Icc), 536–541.
Dov, D., & Hillary, S. (2017). What is a system? An ontological framework. Systems Engineering, 20(3), 207–219.
Suh, N. P., Cochran, D. S., & Lima, P. C. (1998). Manufacturing system design. CIRP Annual Manufacturing Technology, 47(2), 627–639.
Wang, R. Y., Storey, V. C., & Firth, C. P. (1995). A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering, 7(4), 623–640.
Jain, S., Triantis, K. P., & Liu, S. (2011). Manufacturing performance measurement and target setting: A data envelopment analysis approach. European Journal of Operational Research, 214(3), 616–626.
Kusiak, A. (2006). Data mining: Manufacturing and service applications. International Journal of Production Research, 44(18–19), 4175–4191.
Hutchison, D. & Mitchell, J.C. (2003). Data warehousing and knowledge discovery vol. 2737.
Leong, S., Lee, Y. T., & Riddick, F. (2006, September). A core manufacturing simulation data information model for manufacturing applications. In Simulation Interoperability Workshop, Simulation Interoperability and Standards Organization (pp. 1–7).
Barkmeyer, E. (1997). SIMA reference architecture part I: Activity models, NISTIR 5939. Gaithersburg, MD: National Institute of Standards and Technology.
Eder, W. E. (2015). Theory of technical systems. July 1988.
Schenk, M., Wirth, S., & Müller, E. (2009). Factory planning manual situation-driven production facility planning. Springer.
Hesselbach, J. (2012). “Energie- und klimaeffiziente Produktion,” Energie- und klimaeffiziente Produktion.
Hesselbach, J., Herrmann, C., Detzer, R., Martin, L., Thiede, S., & Ludemann, B. (2008). Energy efficiency through optimised coordination of production and technical building services. In LCE 2008: 15th CIRP International Conference on Life Cycle Engineering: Conference Proceedings (p. 624). CIRP.
Herrmann, C., & Thiede, S. (2009). Process chain simulation to foster energy efficiency in manufacturing. CIRP Journal of Manufacturing Science and Technology, 1(4), 221–229.
Steward, D. V. (1981). The design structure system: A method for managing the design of complex systems. IEEE Transactions on Engineering Management, EM-28(3), 71–74.
Siddharth, L., Sarkar, P., & Chakrabarti, A. (2017). Modelling and structuring the knowledge of failures using design structure matrix (DSM) for reuse during product life-cycle. In 6th International Conference on Product Lifecycle Modeling, Simulation and Synthesis—PLMSS 2017, DIAT, Pune, India, 2017 (pp. 104–115).
Groover, M. P. (2002). Automation, production systems, and computer-integrated manufacturing. Pearson Education India.
DIN 8580. (2003). Manufacturing processes—terms and definitions.
CO2PE! (Cooperative Effort on Process Emissions in Manufacturing) (2011).
Kota, S., & Chakrabarti, A. (2010). A method for Estimating the Degree of Uncertainty With Respect to Life Cycle Assessment During Design. Journal of Mechanical Design, 132(9), 091007.
Bayou, M. E., & Nachtman, J. B. (1992). Costing for manufacturing wastes. Journal of Cost Management (Summer): 53–62.
Ashby, M. F., & Ash, M. F. (2011). Materials selection mechanical design in second edition Oxford Auckland Boston Johannesburg Melbourne New Delhi in Second Edition (pp. 32–65).
U. Nations. (2008). International Standard Classification of All Economics Activity Isic.
Siddharth, L., Chakrabarti, A., Venkataraman, S. (2018). Representing complex analogues using a function model to support conceptual design, no. August, p. V01BT02A039.
Dieter, G. E., Schmidt, L. C., & Azarm, S. (2009). Engineering design.
Issues, C. (2005). Stochastic modeling of manufacturing systems.
Huang, C. C., & Kusiak, A. (1998). Manufacturing control with a push-pull approach. International Journal of Production Research, 36(1), 251–276.
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Kaushal, I., Siddharth, L., Chakrabarti, A. (2021). A Conceptual Model for Smart Manufacturing Systems. In: Chakrabarti, A., Arora, M. (eds) Industry 4.0 and Advanced Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5689-0_8
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