Advertisement

Ontology-based cutting tool configuration considering carbon emissions

  • Guanghui Zhou
  • Qi Lu
  • Zhongdong Xiao
  • Ce Zhou
  • Shengze Yuan
  • Chao Zhang
Regular Paper

Abstract

In order to improve the precision and efficiency of cutting tool configuration and reduce carbon emissions during manufacturing process, an ontology-based cutting tool configuration process considering carbon emissions is put forward in the paper. Firstly, the architecture of ontology-based cutting tool configuration is established and key functional modules are described. Secondly, ontology is applied to describe the complex knowledge of cutting tool configuration and the Semantic Web Rule Language (SWRL) is used to build inference rules to reason feasible cutting tool configuration schemes according to machining requirements. Thirdly, taking carbon emissions as the objective, an evaluation method based on the c-PBOM-T (carbon emissions-Process Bill of Material for cutting Tools) table is studied to decide an optimal cutting tool configuration scheme from the feasible ones in the previous step for part machining. Finally, the proposed method is applied to a vortex shell workpiece to demonstrate its feasibility. The results show that the proposed method can improve the cutting tool configuration and reduce carbon emissions effectively for the machining processes. The presented method provides a valuable insight into the intelligent cutting tool configuration to support low-carbon manufacturing.

Keywords

Cutting tools Configuration process Ontology Carbon emissions Low-carbon manufacturing 

Nomenclature

CT1, CT2, CT3

Cutting tool names

tCT1, tCT2, tCT3

Cutting time (s) of CT1, CT2 and CT3

TCT1, TCT2, TCT3

Cutting tool life (s) of CT1, CT2 and CT3

R

Times the cutting tools will be sharpened

FCT1, FCT2, FCT3

Carbon emissions factor (kgCO2/kg) of CT1, CT2 and CT3

WCT1, WCT2, WCT3

Mass (kg) of CT1, CT2 and CT3

ap

Cutting depth (mm) of cutting tools

f

Feed rate (mm/min) of cutting tools

Vc

Spindle speed (m/min) of cutting tools

Fe

Carbon emission factor of electricity (kgCO2/kWh)

FwCT1, FwCT2, FwCT3

Carbon emission factor (kgCO2/kg) of waste cutting tool disposal of CT1, CT2 and CT3

L

Feature length to be processed (mm)

Δ

Machining allowance (mm)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brenner, D., Kleinert, F., Imiela, J., and Westkämper, E., “Life Cycle Management of Cutting Tools: Comprehensive Acquisition and Aggregation of Tool Life Data,” Procedia CIRP, Vol. 61, pp. 311–316, 2017.CrossRefGoogle Scholar
  2. 2.
    Mookherjee, R. and Bhattacharyya, B., “Development of an Expert System for Turning and Rotating Tool Selection in a Dynamic Environment,” Journal of Materials Processing Technology, Vol. 113, No. 1, pp. 306–311, 2001.CrossRefGoogle Scholar
  3. 3.
    Oral, A. and Cakir, M.C., “Automated Cutting Tool Selection and Cutting Tool Sequence Optimisation for Rotational Parts,” Robotics and Computer-Integrated Manufacturing, Vol. 20, No. 2, pp. 127–141, 2004.CrossRefGoogle Scholar
  4. 4.
    Lin, A. C. and Wei, C.-L., “Automated Selection of Cutting Tools Based on Solid Models,” Journal of Materials Processing Technology, Vol. 72, No. 2, pp. 317–329, 1997.CrossRefGoogle Scholar
  5. 5.
    Avram, O., Stroud, I., and Xirouchakis, P., “A Multi-Criteria Decision Method for Sustainability Assessment of the Use Phase of Machine Tool Systems,” The International Journal of Advanced Manufacturing Technology, Vol. 53, No. 5, pp. 811–828, 2011.CrossRefGoogle Scholar
  6. 6.
    Yan, W., Zanni-Merk, C., Rousselot, F., Cavallucci, D., and Collet, P., “Ontology-Based Knowledge Modeling for Using Physical Effects,” Procedia Engineering, Vol. 131, pp. 601–615, 2015.CrossRefGoogle Scholar
  7. 7.
    Chen, R.-C., Huang, Y.-H., Bau, C.-T., and Chen, S.-M., “A Recommendation System Based on Domain Ontology and SWRL for Anti-Diabetic Drugs Selection,” Expert Systems with Applications, Vol. 39, No. 4, pp. 3995–4006, 2012.CrossRefGoogle Scholar
  8. 8.
    Li, A., Zhao, J., Gong, Z., and Lin, F., “Optimal Selection of Cutting Tool Materials Based on Multi-Criteria Decision-Making Methods in Machining Al-Si Piston Alloy,” The International Journal of Advanced Manufacturing Technology, Vol. 86, Nos. 1-4, pp. 1055–1062, 2016.CrossRefGoogle Scholar
  9. 9.
    Campatelli, G., Scippa, A., and Lorenzini, L., “Workpiece Orientation and Tooling Selection to Reduce the Environmental Impact of Milling Operations,” Procedia CIRP, Vol. 14, pp. 575–580, 2014.CrossRefGoogle Scholar
  10. 10.
    Zhou, D. and Dai, X., “A Granulation Analysis Method for Cutting Tool Material Selection Using Granular Computing,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 230, No. 13, pp. 2323–2336, 2016.Google Scholar
  11. 11.
    Liu, Z.-J., Sun, D.-P., Lin, C.-X., Zhao, X.-Q., and Yang, Y., “Multi-Objective Optimization of the Operating Conditions in a Cutting Process Based on Low Carbon Emission Costs,” Journal of Cleaner Production, Vol. 124, pp. 266–275, 2016.CrossRefGoogle Scholar
  12. 12.
    Buyurgan, N., Saygin, C., and Kilic, S. E., “Tool Allocation in Flexible Manufacturing Systems with Tool Alternatives,” Robotics and Computer-Integrated Manufacturing, Vol. 20, No. 4, pp. 341–349, 2004.CrossRefGoogle Scholar
  13. 13.
    Saranya, K., Jegaraj, J. J. R., Kumar, K. R., and Rao, G. V., “Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations,” Journal of the Institution of Engineers (India): Series C, pp. 1–12, 2016. (DOI: 10.1007/s40032-016-0264-7)Google Scholar
  14. 14.
    Gjelaj, A., Balic, J., and Ficko, M., “Intelligent Optimal Tool Selections for CNC Programming of Machine Tools,” Transactions of FAMENA, Vol. 37, No. 3, pp. 31–40, 2013.Google Scholar
  15. 15.
    Arunachalam, A. P. S., Idapalapati, S., and Subbiah, S., “Multi-Criteria Decision Making Techniques for Compliant Polishing Tool Selection,” The International Journal of Advanced Manufacturing Technology, Vol. 79, Nos. 1-4, pp. 519–530, 2015.CrossRefGoogle Scholar
  16. 16.
    Mejia-Ugalde, M., Trejo-Hernandez, M., Dominguez-Gonzalez, A., Osornio-Rios, R. A., and Benitez-Rangel, J. P., “Directional Morphological Approaches from Image Processing Applied to Automatic Tool Selection in Computer Numerical Control Milling Machine,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 227, No. 11, pp. 1607–1619, 2013.CrossRefGoogle Scholar
  17. 17.
    Tan, C. F., Ranjit, S. S. S., and Kher, V. K., “An Expert Carbide Cutting Tools Selection System for CNC Lathe Machine,” International Review of Mechanical Engineering, Vol. 6, No. 7, pp. 1402–1405, 2012.Google Scholar
  18. 18.
    Wu, X., Feng, G., and Wu, T., “Intelligent Service Platform of Manufacturing Process and Tool Based on Data Warehouse,” Procedia CIRP, Vol. 56, pp. 338–343, 2016.CrossRefGoogle Scholar
  19. 19.
    Zhang, Y., Luo, X., Zhang, H., and Sutherland, J. W., “A Knowledge Representation for Unit Manufacturing Processes,” The International Journal of Advanced Manufacturing Technology, Vol. 73, Nos. 5-8, pp. 1011–1031, 2014.CrossRefGoogle Scholar
  20. 20.
    Lemaignan, S., Siadat, A., Dantan, J.-Y., and Semenenko, A., “Mason: A Proposal for an Ontology of Manufacturing Domain,” Proc. of IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications, pp. 195–200, 2006.Google Scholar
  21. 21.
    Rehage, G. and Gausemeier, J., “Ontology-Based Determination of Alternative CNC Machines for a Flexible Resource Allocation,” Procedia CIRP, Vol. 31, pp. 47–52, 2015.CrossRefGoogle Scholar
  22. 22.
    Eum, K., Kang, M., Kim, G., Park, M. W., and Kim, J. K., “Ontology-Based Modeling of Process Selection Knowledge for Machining Feature,” Int. J. Precis. Eng. Manuf., Vol. 14, No. 10, pp. 1719–1726, 2013.CrossRefGoogle Scholar
  23. 23.
    Ramesh, S., Palanikumar, K., Elangovan, K., and Karunamoorthy, L., “Machining Titanium Alloy with Pulsed Injecting Coolant Technique to Improve a Eco-Friendly Enviornment in Industries,” Proc. of the 19th AeroMat Conference and Exposition, 2008.Google Scholar
  24. 24.
    Thiede, S., Seow, Y., Andersson, J., and Johansson, B., “Environmental Aspects in Manufacturing System Modelling And Simulation -State of the Art and Research Perspectives,” CIRP Journal of Manufacturing Science and Technology, Vol. 6, No. 1, pp. 78–87, 2013.CrossRefGoogle Scholar
  25. 25.
    Duflou, J. R., Kellens, K., and Dewulf, W., “Unit Process Impact Assessment for Discrete Part Manufacturing: A State of the Art,” CIRP Journal of Manufacturing Science and Technology, Vol. 4, No. 2, pp. 129–135, 2011.CrossRefGoogle Scholar
  26. 26.
    Song, J.-S. and Lee, K.-M., “Development of a Low-Carbon Product Design System Based on Embedded GHG Emissions,” Resources, Conservation and Recycling, Vol. 54, No. 9, pp. 547–556, 2010.CrossRefGoogle Scholar
  27. 27.
    Li, C., Tang, Y., Cui, L., and Li, P., “A Quantitative Approach to Analyze Carbon Emissions of CNC-Based Machining Systems,” Journal of Intelligent Manufacturing, Vol. 26, No. 5, pp. 911–922, 2015.CrossRefGoogle Scholar
  28. 28.
    Lin, W., Yu, D., Zhang, C., Zhang, S., Tian, Y., Liu, S., and Luo, M., “Multi-Objective Optimization of Machining Parameters in Multi-Pass Turning Operations for Low-Carbon Manufacturing,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 231, No. 13, pp.2372–2383, 2016. (DOI:10.1177/0954405416629098)CrossRefGoogle Scholar
  29. 29.
    Yi, Q., Li, C., Tang, Y., and Chen, X., “Multi-Objective Parameter Optimization of CNC Machining for Low Carbon Manufacturing,” Journal of Cleaner Production, Vol. 95, pp. 256–264, 2015.CrossRefGoogle Scholar
  30. 30.
    Kara, S. and Li, W., “Unit Process Energy Consumption Models for Material Removal Processes,” CIRP Annals-Manufacturing Technology, Vol. 60, No. 1, pp. 37–40, 2011.CrossRefGoogle Scholar
  31. 31.
    Mativenga, P. T. and Rajemi, M. F., “Calculation of Optimum Cutting Parameters Based on Minimum Energy Footprint,” CIRP Annals-Manufacturing Technology, Vol. 60, No. 1, pp. 149–152, 2011.CrossRefGoogle Scholar
  32. 32.
    Tan, X. C., Liu, F., Cao, H. J., and Zhang, H., “A Decision-Making Framework Model of Cutting Fluid Selection for Green Manufacturing and a Case Study,” Journal of Materials Processing Technology, Vol. 129, No. 1, pp. 467–470, 2002.CrossRefGoogle Scholar
  33. 33.
    Tan, X. C., Liu, F., Cao, H. J., Zhang, P., “A Decision-Making Framework Model of Tool Selection for Green Manufacturing and Its Applications,” Journal of Chongqing University, Vol. 26, No. 3, pp. 117–121, 2003.Google Scholar
  34. 34.
    Zhou, G., Zhou, C., Lu, Q., Tian, C., and Xiao, Z., “Feature-Based Carbon Emission Quantitation Strategy for the Part Machining Process,” International Journal of Computer Integrated Manufacturing, 2017. (DOI:10.1080/0951192X.2017.1328561)Google Scholar
  35. 35.
    National Development and Reform Commission,” Baseline Emission Factor of Regional Power Grids of China in 2015,” http://cdm. ccchina.gov.cn/Detail.aspx?newsId=61599&TId=19 (Accessed 17 OCT 2017) (in Chinese)Google Scholar
  36. 36.
    Narita, H., Desmira, N., and Fujimoto, H., “Environmental Burden Analysis for Machining Operation Using LCA Method,” in: Manufacturing Systems and Technologies for the New Frontier: The 41st CIRP Conference on Manufacturing System, Mitsuishi, M., Ueda, K., Kimura, F., (Eds.), pp. 65–68, 2008.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Guanghui Zhou
    • 1
    • 2
  • Qi Lu
    • 2
  • Zhongdong Xiao
    • 3
  • Ce Zhou
    • 2
  • Shengze Yuan
    • 2
  • Chao Zhang
    • 2
  1. 1.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityYantaChina
  2. 2.School of Mechanical EngineeringXi’an Jiaotong UniversityBeilinChina
  3. 3.School of ManagementXi’an Jiaotong UniversityBeilinChina

Personalised recommendations