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A Systematic Approach of Process Planning and Scheduling Optimization for Sustainable Machining

  • S. Wang
  • X. Lu
  • X. X. Li
  • W. D. LiEmail author
Chapter

Abstract

The implementation of sustainability in manufacturing companies, whose activities are usually characterized by high variety and low volume, has been crippled by the lack of effective process planning and scheduling solutions for sustainable management of manufacturing shop floors. To address the challenge, an innovative and systematic approach for machining process planning and scheduling optimization has been developed. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for machining a part are optimized adaptively to meet multiple objectives and constraints, i.e., energy efficiency of the machining process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features, operations and scheduling for producing multiple parts on different machines are optimized. Artificial neural networks are used for establishing the complex nonlinear relationships between the key process parameters and measured data sets of energy consumption and surface quality. Intelligent algorithms, including pattern search, genetic algorithm, and simulated annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potentials of research applicability in practice.

Keywords

Sustainable manufacturing Computer numerical control machining Process planning Process scheduling Intelligent algorithm Machining feature 

References

  1. 1.
    Jovane, F., Yoshikawa, H., Alting, L., Boer, C. R., Westkamper, E., Williams, D., et al. (2008). The incoming global technological and industrial revolution towards competitive sustainable manufacturing. CIRP Annals, 75, 641–659.CrossRefGoogle Scholar
  2. 2.
    Mayers, C. K. (2007). Strategic, financial, and design implications of extended producer responsibility in Europe: A producer case study. Journal of Industrial Ecology, 11, 113–131.CrossRefGoogle Scholar
  3. 3.
    O’Driscoll, E., & O’Donnell, G. (2013). Industrial power and energy metering—a state-of-the-art review. Journal of Cleaner Production, 41, 53–64.CrossRefGoogle Scholar
  4. 4.
    Bunse, K., Vodicka, M., Schonsleben, P., Brulhart, M., & Ernst, F. O. (2011). Integrating energy efficiency performance in production management—gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19, 667–679.CrossRefGoogle Scholar
  5. 5.
    Tolio, T., Ceglarek, D., ElMaragphy, H. A., Fischer, A., Hu, S. J., Laperriere, L., et al. (2011). SPECIES—co-evolution of products, processes and production systems. CIRP Annals—Manufacturing Technology, 59, 672–693.CrossRefGoogle Scholar
  6. 6.
    Wang, L. H., & Shen, W. M. (2010). Process planning and scheduling for distributed manufacturing. Berlin: Springer.Google Scholar
  7. 7.
    Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., et al. (2012). Towards energy and resource efficient manufacturing: A process and system approach. CIRP Annals—Manufacturing Technology, 61, 587–609.CrossRefGoogle Scholar
  8. 8.
    Abele, A., Anderl, R., & Birkhofer, H. (2005). Environmentally-friendly product development—methods and tools. ISBN: 1-85233-903-9 (Chap. 3).Google Scholar
  9. 9.
    Gutowski, T., Dahmus, J., & Thiriez, A. (2006). Electrical energy requirements for manufacturing processes. In Proceedings of the 13th CIRP International Conference of Life Cycle Engineering. Lueven, May 31st—June 2nd.Google Scholar
  10. 10.
    Mori, M., Fujishima, M., Inamasu, Y., & Oda, Y. (2011). A study on energy efficiency improvement for machine tools. CIRP Annals—Manufacturing Technology, 60(1), 145–148.CrossRefGoogle Scholar
  11. 11.
    Newman, S. T., Nassehi, A., Imani-Asrai, R., & Dhokia, V. (2012). Energy efficient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 5, 127–136.CrossRefGoogle Scholar
  12. 12.
    Hu, S. H., Liu, F., He, Y., & Hu, T. (2012). An on-line approach for energy efficiency monitoring of machine tools. Journal of Cleaner Production, 27, 133–140.CrossRefGoogle Scholar
  13. 13.
    Camposeco-Negrete, C. (2013). Optimizing of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 53, 195–203.CrossRefGoogle Scholar
  14. 14.
    Yan, J., & Li, L. (2013). Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 52, 462–471.CrossRefGoogle Scholar
  15. 15.
    Winter, M., Li, W., Kara, S., & Hermann, C. (2014). Determining optimal process parameters to increase the eco-efficiency of grinding processes. Journal of Cleaner Production, 66, 644–654.CrossRefGoogle Scholar
  16. 16.
    Avram, O. I., & Xirouchakis, P. (2011). Evaluating the use phase energy requirements of a machine tool system. Journal of Cleaner Production, 19, 699–711.CrossRefGoogle Scholar
  17. 17.
    Kong, D., Choi, S., Yasui, Y., Pavanaskar, S., Dornfeld, D., & Wright, P. (2011). Software-based tool path evaluation for environmental sustainability. Journal of Manufacturing Systems., 30, 241–247.CrossRefGoogle Scholar
  18. 18.
    Balogun, V. A., & Mativenga, P. T. (2013). Modelling of direct energy requirements in mechanical machining processes. Journal of Cleaner Production, 41, 179–186.CrossRefGoogle Scholar
  19. 19.
    Wang, Q., Liu, F., & Li, C. (2013). An integrated method for assessing the energy efficiency of machining workshop. Journal of Cleaner Production, 52, 122–133.CrossRefGoogle Scholar
  20. 20.
    Dai, M., Tang, D. B., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418–429.CrossRefGoogle Scholar
  21. 21.
    Aramcharoen, A., & Mativenga, P. T. (2014). Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy. Journal of Cleaner Production, 78, 63–74.CrossRefGoogle Scholar
  22. 22.
    Li, W., & Kara, S. (2011). An empirical model for predicting energy consumption of manufacturing processes: A case of turning process. Proceedings of Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225, 1636–1646.CrossRefGoogle Scholar
  23. 23.
    Kara, S., & Li, W. (2011). Unit process energy consumption models for material removal processes. CIRP Annals—Manufacturing Technology, 60(1), 37–40.CrossRefGoogle Scholar
  24. 24.
    Li, W., Winter, M., Kara, S., & Herrmann, C. (2012). Eco-efficiency of manufacturing processes: A grinding case. CIRP Annals—Manufacturing Technology, 61(1), 59–62.CrossRefGoogle Scholar
  25. 25.
    Diaz, N., Redelsheimer, E., Dornfeld, D. (2011). Energy consumption characterization and reduction strategies for milling machine tool use. In Proceedings of the 18th CIRP International Conference on Life Cycle Engineering. Braunschweig, May 2nd–4th, pp. 263–267.CrossRefGoogle Scholar
  26. 26.
    Li, L., Yan, J., & Xing, Z. (2013). Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. Journal of Cleaner Production, 52, 113–121.CrossRefGoogle Scholar
  27. 27.
    He, Y., Liu, F., Wu, T., Zhong, F. P., & Peng, B. (2012). Analysis and estimation of energy consumption for numerical control machining. Proceedings of Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture., 226, 255–266.CrossRefGoogle Scholar
  28. 28.
    Li, W. D., Ong, S. K., & Nee, A. Y. C. (2006). Integrated and collaborative product development environment—technologies and implementation. World Scientific Publisher.Google Scholar
  29. 29.
    Li, W. D., & McMahon, C. A. (2007). A simulated annealing-based optimization approach for integrated process planning and scheduling. International Journal of Computer Integrated Manufacturing, 20(1), 80–95.CrossRefGoogle Scholar
  30. 30.
    Tzeng, C. J., Lin, Y. H., Yang, Y. K., & Jeng, M. C. (2009). Optimization of turning operations with multiple performance characteristics using the Taguchi method and grey analysis. Journal of Materials Processing Technology, 209, 2753–2759.CrossRefGoogle Scholar
  31. 31.
    Mausser, H. (2006). Normalization and other topics in multi-objective optimization. In Proceedings of the Fields–MITACS Industrial Problems Workshop. pp. 89–101.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Engineering, Environment and ComputingCoventry UniversityCoventryUK

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