IJAT Vol.16 No.3 pp. 309-319
doi: 10.20965/ijat.2022.p0309


Energy Consumption Rate Evaluation Method Considering Occurrence of Defective Products and Misjudgment of Inspection Machine in Production Line

Hironori Hibino*,†, Takamasa Horikawa**, Syungo Arai**, and Makoto Yamaguchi***

*College of Economics, Nihon University
1-3-2 Misaki-cho, Chiyoda-ku, Tokyo 101-8360, Japan

Corresponding author

**Department of Industrial Administration, Graduate School of Science and Technology, Tokyo University of Science, Noda, Japan

***Department of Systems Design Engineering, Graduate School of Engineering Science, Akita University, Akita, Japan

December 28, 2021
February 24, 2022
May 5, 2022
manufacturing system, productivity, energy consumption per unit production throughput, defective product, misjudgment

In recent years, reducing energy consumption has become a key issue in the industrial world. Therefore, industrial corporations must develop methods of pre-evaluation and production management for reducing their energy consumption while maintaining productivity. Moreover, production lines occasionally generate defective products, reducing the productivity and wasting energy, which affects the energy consumption per unit of production. These production lines require inspection machines to exclude defective products. The layout and configuration of inspection machines change when defective products are excluded, which affects the energy consumption per product. However, no methods have been developed for evaluating the energy consumption per product by considering the number of defective products and the layout and configuration of the inspection machines. In this study, we formulated the energy consumption rate of a production line that generates defective products as the production planning and management method. Specifically, we developed a formula for the energy consumption rate of a production line by considering the defect rate of its production machines and the layout and configuration of the inspection machines. A simulation involving a semiconductor manufacturing line was conducted to validate the proposed theory.

Cite this article as:
H. Hibino, T. Horikawa, S. Arai, and M. Yamaguchi, “Energy Consumption Rate Evaluation Method Considering Occurrence of Defective Products and Misjudgment of Inspection Machine in Production Line,” Int. J. Automation Technol., Vol.16 No.3, pp. 309-319, 2022.
Data files:
  1. [1] Agency for Natural Resources and Energy, “Act of the Rational Use of Energy” (in Japanese). [Accessed June 17, 2019]
  2. [2] United Nations Framework Convention on Climate Change, “Adoption of the Paris agreement.” [Accessed June 17, 2019].
  3. [3] Ministry of Economy, Trade and Industry, “Innovative Energy Strategy” (in Japanese). [Accessed June 18, 2019]
  4. [4] H. Kim, S. S. Lee, J. H. Park, and J. G. Lee, “A model for a simulation-based shipbuilding system in a shipyard manufacturing process,” Int. J. of Computer Integrated Manufacturing, Vol.18, No.6, pp. 427-441, 2005.
  5. [5] J. Wilson, A. Arokiam, H. Belaidi, and J. Ladbrook, “A simple energy usage toolkit from manufacturing simulation data,” J. of Cleaner Production, Vol.122, pp. 266-276, 2016.
  6. [6] Y. He, B. Liu, X. Zhang, H. Gao, and X. Liu, “A modeling method of task-oriented energy consumption for machining manufacturing system,” J. of Cleaner Production, Vol.23, No.1, pp. 167-174, 2012.
  7. [7] C. Gahm, F. Denz, M. Dirr, and A. Tuma, “Energy-efficient scheduling in manufacturing companies: a review and research framework,” Eur. J. Oper. Res., Vol.248, No.3, pp. 744-757, 2016.
  8. [8] M. Yabuuchi, T. Kaihara, N. Fujii, D. Kokuryo, S. Sakajo, and Y. Nishita, “A Basic Study on Scheduling Method for Electric Power Saving of Production Machine,” APMS 2020: Advances in Production Management Systems, pp. 524-530, 2020.
  9. [9] H. Hibino, T. Sakuma, and M. Yamaguchi, “Manufacturing system simulation for evaluation of productivity and energy consumption,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.8, No.2, JAMDSM0014, doi: 10.1299 /jamdsm.2014jamdsm0014, 2014.
  10. [10] M. Yamaguchi, T. Kobayashi, and H. Hibino, “Manufacturing system simulation to evaluate energy productivity (Formulation of relationship between productivity and energy consumption),” Trans. of the JSME, Vol.82, No.835, 15-00495, doi: 10.1299/transjsme.15-00495, 2016 (in Japanese).
  11. [11] H. Hibino, T. Horikawa, and M. Yamaguchi, “A study on lot-size dependence of the energy consumption per unit of production throughput concerning variable lot-size,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.13, No.3, JAMDSM0062, 2019.
  12. [12] K. Amalesh, K, Jayanta, and K. Shyamal, “Imperfect production inventory model with production rate dependent defective rate and advertisement dependent demand,” J. of Computer & Industrial Engineering, Vol.104, pp. 9-22, 2017.
  13. [13] W. Singa, “Production lot size problem with failure in repair and backlogging derived without derivatives,” European J. of Operational Research, Vol.188, No.2, pp. 610-615, 2008.
  14. [14] P. Yuan-Shyi, L. Hong-Dar, and C. Huei-Hsin, “Mathematical modeling for solving manufacturing run time problem with defective rate and random machine breakdown,” J. of Computer & Industrial Engineering, Vol.60, No.4, pp. 576-584, 2011.
  15. [15] M. Rezaei-Malek, M. Mohammadi, J. Dantan, A. Siadat, and R. Tavakkoli-Moghaddam, “A review on optimisation of part quality inspection planning in a multi-stage manufacturing system,” Int. J. of Production Research, Vol.57, pp. 4880-4897, 2019.
  16. [16] G. Genta, M. Galetto, and F. Franceschini, “Inspection procedures in manufacturing processes: recent studies and research perspectives.” Int. J. of Production Research, Vol.58, pp. 4767-4788, 2020.
  17. [17] A. Ait-El-Cadi, A. Gharbi, K. Dhouib, and A. Artiba, “Integrated production, maintenance and quality control policy for unreliable manufacturing systems under dynamic inspection,” Int. J. of Production Economics, Vol.236, pp. 1-20, 2021.
  18. [18] J. Chen, Z. Zhang, and F. Wu, “A data-driven method for enhancing the image based automatic inspection of IC wire bonding defects,” Int. J. of Production Research, Vol.59, pp. 4779-4793, 2021.
  19. [19] B. Huang, S. Ma, P. Wang, H. Wang, J. Yang, X. Guo, W. Zhang, and H. Wang, “Research and implementation of machine vision technologies for empty bottle inspection systems,” Engineering Science and Technology, Int. J., Vol.21, No.1, pp. 159-169, 2018.
  20. [20] P. Martineza, R. Ahmadb, and M. Al-Husseina, “A vision-based system for pre-inspection of steel frame manufacturing.” Automation in Construction, Vol.97, pp. 151-163, 2019.
  21. [21] Agency for Natural Resources and Energy, “Strategic Energy Plan” (in Japanese). [Accessed June 17, 2019]
  22. [22] ITOCHU Techno-Solutions Corporation, “WITNESS,” ITOCHU Techno-Solutions Corp. Science&Engineering Systems Division, (in Japanese). [Accessed June 18, 2019].

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024