Reconfigurable machine tools (RMTs) are important equipment for enterprises to cope with ever-changing markets because of their flexibility. In design of such equipment, selection of appropriate modules is a very critical decision factor to effectively and efficiently satisfy manufacturing requirements. However, the selection of appropriate modules is a challenging task because it is a multi-domain mapping process relying heavily on experts’ domain knowledge, which is usually unstructured and implicit. To effectively support RMT designers, an ontology-based RMT module selection method is proposed. First, a knowledge base is built by development of an ontology to formally represent the taxonomy, properties, and causal relationships of/among three domain core concepts, namely, machining feature, machining operation, and RMT module involved in RMT design. Second, a four-step sequential procedure is established to facilitate the utilization of encoded knowledge from a knowledge base to aid in the selection of appropriate RMT modules. The procedure takes a given part family as the input, automatically infers the required machining operations as well as the RMT modules through rule-based reasoning, and eventually forms a set of RMT configurations that are capable of machining the part family as the output. Finally, the efficacy of the ontology-based RMT module selection method is demonstrated using a plate family manufacturing example. Results show that the approach is effective to support designers by appropriately and rapidly selecting modules and generating configurations in RMT design.
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Aguilar, A., Roman-Flores, A., & Huegel, J. C. (2013). Design, refinement, implementation and prototype testing of a reconfigurable lathe-mill. Journal of Manufacturing Systems,32(2), 364–371.
Ameri, F., & Patil, L. (2012). Digital manufacturing market: A semantic web-based framework for agile supply chain deployment. Journal of Intelligent Manufacturing,23(5), 1817–1832.
Baqai, A., Siadat, A., Dantan, J. Y., & Martin, P. (2008). Use of a manufacturing ontology and function-behaviour-structure approach for the design of a reconfigurable machine tool. International Journal of Product Lifecycle Management,3(2–3), 132–150.
Bi, Z. M. (2011). Development and control of a 5-axis reconfigurable machine tool. Journal of Robotics,2011(2), 583072–583081.
Bi, Z. M., & Zhang, W. J. (2001). Concurrent optimal design of modular robotic configuration. Journal of Field Robotics,18(2), 77–87.
Cao, L., Dolovich, A. T., Schwab, A. L., Herder, J. L., & Zhang, W. J. (2015). Towards a unified design approach for both compliant mechanisms and rigid-body mechanisms: Module optimization. Journal of Mechanical Design,137(12), 122301–122310.
Catalano, C. E., Camossi, E., Ferrandes, R., Cheutet, V., & Sevilmis, N. (2009). A product design ontology for enhancing shape processing in design workflows. Journal of Intelligent Manufacturing,20(5), 553–567.
Chaube, A., Benyoucef, L., & Tiwari, M. K. (2012). An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. Journal of Intelligent Manufacturing,23(4), 1141–1155.
Chen, L., Xi, F. J., & Macwan, A. (2005). Optimal module selection for preliminary design of reconfigurable machine tools. Journal of Manufacturing Science and Engineering,127(1), 104–115.
Chhim, P., Chinnam, R. B., & Sadawi, N. (2017). Product design and manufacturing process based ontology for manufacturing knowledge reuse. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-016-1290-2.
Chira, O., Chira, C., Roche, T., Tormey, D., & Brennan, A. (2006). An agent-based approach to knowledge management in distributed design. Journal of Intelligent Manufacturing,17(6), 737–750.
Dhupia, J., Powalka, B., Katz, R., & Ulsoy, A. G. (2007). Dynamics of the arch-type reconfigurable machine tool. International Journal of Machine Tools and Manufacture,47(2), 326–334.
Fan, L. X., Cai, M. Y., Lin, Y., & Zhang, W. J. (2015). Axiomatic design theory: Further notes and its guideline to applications. International Journal of Materials and Product Technology,51(4), 359–374.
Hazelrigg, G. A. (2003). Validation of engineering design alternative selection methods. Engineering Optimization,35(2), 103–120.
Hong, H., & Yin, Y. (2016). Ontology-based human–machine integrated design method for ultra-precision grinding machine spindle. Journal of Industrial Information Integration,2, 1–10. https://doi.org/10.1016/j.jii.2016.04.003.
Huang, S., Wang, G., Shang, X., & Yan, Y. (2018). Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS). Journal of Intelligent Manufacturing,29(5), 1031–1043.
Imran, M., & Young, B. (2015). The application of common logic based formal ontologies to assembly knowledge sharing. Journal of Intelligent Manufacturing,26(1), 139–158.
ISO 10303-224 (2006) Part 224: Application protocol: Mechanical product definition for process planning using machining features. Available at: https://www.iso.org/standard/36000.html.
Järvenpää, E., Siltala, N., Hylli, O., & Lanz, M. (2018). The development of an ontology for describing the capabilities of manufacturing resources. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1427-6.
Landers, R. G., Min, B. K., & Koren, Y. (2001). Reconfigurable machine tools. CIRP Annals,50(1), 269–274.
Lorenzer, T., Weikert, S., Bossoni, S., & Wegener, K. (2007). Modeling and evaluation tool for supporting decisions on the design of reconfigurable machine tools. Journal of Manufacturing Systems,26(3–4), 167–177.
Montalto, A., Graziosi, S., Bordegoni, M., Di Landro, L., & van Tooren, M. J. L. (2018). An approach to design reconfigurable manufacturing tools to manage product variability: The mass customisation of eyewear. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1436-5.
Moon, Y.-M., & Kota, S. (1998). Generalized kinematic modeling of reconfigurable machine tools. Journal of Mechanical Design,124(1), 47–51.
Mpofu, K. (2012). Machine morphology in reconfigurable machine tools. Ifac Proceedings Volumes,45(6), 391–398.
Mpofu, K., & Tlale, N. (2014). A morphology proposal in commercial-off-the-shelf reconfigurable machine tools. International Journal of Production Research,52(15), 4440–4455.
O’Connor, M. (2018). SWRLTap: A development environment for working with SWRL rules in Protégé-OWL. Available at: https://protege.stanford.edu/conference/2007/slides/08.01_OConnor.pdf.
Palmer, C., Urwin, E. N., Niknejad, A., Petrovic, D., Popplewell, K., & Young, R. I. M. (2018). An ontology supported risk assessment approach for the intelligent configuration of supply networks. Journal of Intelligent Manufacturing,29(5), 1005–1030.
Pérez, R., Molina, A., & Ramírez-Cadena, M. (2014). Development of an integrated approach to the design of reconfigurable micro/mesoscale CNC machine tools. Journal of Manufacturing Science and Engineering,136(3), 031003–031010.
Rasovska, I., Chebel-Morello, B., & Zerhouni, N. (2008). A mix method of knowledge capitalization in maintenance. Journal of Intelligent Manufacturing,19(3), 347–359.
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York, London: McGraw-Hill International Book Co.
Son, H., Choi, H.-J., & Park, H. W. (2010). Design and dynamic analysis of an arch-type desktop reconfigurable machine. International Journal of Machine Tools and Manufacture,50(6), 575–584.
Stanford University. (2018). PROTÉGÉ 5.2. Available at: https://protege.stanford.edu/.
Suh, N. P. (1990). The principles of design. New York: Oxford University Press.
Talhi, A., Fortineau, V., Huet, J.-C., & Lamouri, S. (2017). Ontology for cloud manufacturing based product lifecycle management. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-017-1376-5.
Wang, Q., Chen, X., Yin, Y., & Lu, J. (2017). Ontology-based coupled optimisation design method using state-space analysis for the spindle box system of large ultra-precision optical grinding machine. Enterprise Information Systems,11(7), 1105–1118.
Wang, G., Huang, S., Shang, X., Yan, Y., & Du, J. (2016a). Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines. Journal of Manufacturing Systems,41, 120–129. https://doi.org/10.1016/j.jmsy.2016.08.009.
Wang, J. W., Wang, H. F., Ding, J. L., Furuta, K., Kanno, T., Ip, W. H., et al. (2016b). On domain modelling of the service system with its application to enterprise information systems. Enterprise Information Systems,10(1), 1–16.
Wang, S., Yu, L., Zhou, J., Li, W., Liu, G., & Zhu, H. (1994). The design method of program module for selecting machine tools under CAPP environment. Journal of Shenyang University of Technology (3), 107–113. http://en.cnki.com.cn/Article_en/CJFDTOTAL-SYGY403.023.htm.
Xu, Z., Xi, F., Liu, L., & Chen, L. (2017). A method for design of modular reconfigurable machine tools. Machines,5(1), 1–16.
Yang, S. (2002). Manual of machining technologist. Beijing: Machinery Industry Press.
Yigit, A. S., & Allahverdi, A. (2003). Optimal selection of module instances for modular products in reconfigurable manufacturing systems. International Journal of Production Research,41(17), 4063–4074.
Yin, Y. H., Xie, J. Y., Da Xu, L., & Chen, H. (2012). Imaginal thinking-based human–machine design methodology for the configuration of reconfigurable machine tools. IEEE Transactions on Industrial Informatics,8(3), 659–668.
Zapp, M., Singh, M., Zendoia, J., & Brencsics, I. (2012). Collaborative machine tool design environment based on semantic wiki technology. In 13th European conference on knowledge management, ECKM 2012, September 6–7, 2012 (Vol. 2, pp. 1583–1586). Cartagena, Spain: Academic Conferences Limited.
Zhang, Y. (2011). Metal machining manual. Shanghai: Shanghai Science and Technology Press.
Zhang, D. (2013). Manual of CNC machining. Beijing: Chemical Industry Press.
Zhang, W. J., & van Luttervelt, C. A. (2011). Toward a resilient manufacturing system. CIRP Annals,60(1), 469–472.
Zhou, Q., Yan, P., Liu, H., & Xin, Y. (2017). A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. Journal of Intelligent Manufacturing,6, 1–23.
The authors would like to thank the anonymous reviewers for their valuable comments on this paper. The authors acknowledge financial support from the National Ministries (JCKY2014602B007), National Natural Science Foundation of China (NSFC 51805033 and 51505032), China Postdoctoral Science Foundation (3030036721802), and Beijing Natural Science Foundation (BJNSF 3172028).
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Ming, Z., Zeng, C., Wang, G. et al. Ontology-based module selection in the design of reconfigurable machine tools. J Intell Manuf 31, 301–317 (2020). https://doi.org/10.1007/s10845-018-1446-3
- Reconfigurable machine tool
- Module selection
- SWRL rule
- Knowledge base