single-au.php

IJAT Vol.19 No.5 pp. 931-938
doi: 10.20965/ijat.2025.p0931
(2025)

Research Paper:

Computer-Aided Process Planning Based on Acquired Skills Related to Workpiece Materials

Ryo Hamanaka*, Eisuke Sogabe**, and Keiichi Nakamoto*,† ORCID Icon

*Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

Corresponding author

**Okuma Corporation
Niwa-gun, Japan

Received:
February 7, 2025
Accepted:
May 13, 2025
Published:
September 5, 2025
Keywords:
process planning, skill acquisition, eye tracking, workpiece material, operation sequence
Abstract

The quality and time of the machining process are highly affected by process planning, which determines process parameters such as the operation sequence and machining method. However, process planning remains largely dependent on the skills of operators. Thus, currently, skill transfer is an urgent challenge because the number of skilled operators capable of process planning is decreasing rapidly. In our previous study, skills were identified from interviews by obtaining gaze data of skilled operators when examining mechanical drawings of two different shapes. However, the influence of factors such as workpiece material on process planning was not investigated and remains unresolved. Therefore, in this study, eye-tracking technology was adopted to detect the movement of the operator’s eyes and gather gaze data when understanding mechanical drawings with different workpiece materials. The gaze data were analyzed using a heat map to identify the areas where skilled operators focus on compared with unskilled operators. From the results of interviews with skilled operators based on the gaze data analysis, skills related to workpiece material could be appeared in detail and acquired regarding the operation sequence. The skills were generalized according to the mechanical properties of the workpiece material. The generalized skills were incorporated into a computer-aided process planning system developed in the previous study. Based on the identified skills incorporated, the operation sequence was allocated by referring to the workpiece material. Furthermore, machining experiments confirmed that the allocated operation sequence effectively improves the quality of the machining process.

Cite this article as:
R. Hamanaka, E. Sogabe, and K. Nakamoto, “Computer-Aided Process Planning Based on Acquired Skills Related to Workpiece Materials,” Int. J. Automation Technol., Vol.19 No.5, pp. 931-938, 2025.
Data files:
References
  1. [1] S. K. Freire, S. S. Panicker, S. R. Arenas, Z. Rusak, and E. Niforatos, “A cognitive assistant for operators: AI-powered knowledge sharing on complex systems,” IEEE Pervasive Computing, Vol.22, No.1, pp. 50-58, 2022. https://doi.org/10.1109/MPRV.2022.3218600
  2. [2] C. Gabellieri, F. Angelini, V. Arapi, A. Palleschi, M. G. Catalano, G. Grioli, L. Pallottino, A. Bicchi, M. Bianchi, and M. Garabini, “Grasp it like a pro: Grasp of unknown objects with robotic hands based on skilled human expertise,” IEEE Robotics and Automation Letters, Vol.5, No.2, pp. 2808-2815, 2020. https://doi.org/10.1109/LRA.2020.2974391
  3. [3] W. Sakarinto, H. Narazaki, and K. Shirase, “A decision support system for capturing CNC operator knowledge,” Int. J. Automation Technol., Vol.5, No.5, pp. 655-662, 2011. https://doi.org/10.20965/ijat.2011.p0655
  4. [4] S. Webel, U. Bockholt, T. Engelke, N. Gavish, M. Olbrich, and C. Preusche, “An augmented reality training platform for assembly and maintenance skills,” Robotics and Autonomous Systems, Vol.61, No.4, pp. 398-403, 2013. https://doi.org/10.1016/j.robot.2012.09.013
  5. [5] M. Sugi, I. Matsumura, Y. Tamura, T. Arai, and J. Ota, “Usability analysis of information on worker’s hands in animated assembly manuals,” Int. J. Automation Technol., Vol.12, No.4, pp. 524-532, 2018. https://doi.org/10.20965/ijat.2018.p0524
  6. [6] C. Wu, J. Cha, J. Sulek, T. Zhou, C. P. Sundaram, J. Wachs, and D. Yu, “Eye-tracking metrics predict perceived workload in robotic surgical skills training,” The J. of the Human Factors and Ergonomics Society, Vol.62, No.8, pp. 1365-1386, 2019. https://doi.org/10.1177/0018720819874544
  7. [7] C. Lounis, V. Peysakhovich, and M. Causse, “Visual scanning strategies in the cockpit are modulated by pilots’ expertise: A flight simulator study,” PLOS ONE, Vol.16, No.2, Article No.e0247061, 2021. https://doi.org/10.1371/journal.pone.0247061
  8. [8] R. J. Dzeng, C. T. Lin, and Y. C. Fang, “Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification,” Safety Science, Vol.82, pp. 56-67, 2016. https://doi.org/10.1016/j.ssci.2015.08.008
  9. [9] H. Fu, Y. Tan, Z. Xia, K. Feng, and X. Guo, “Effects of construction workers’ safety knowledge on hazard-identification performance via eye-movement modeling examples training,” Safety Science, Vol.180, Article No.106653, 2024. https://doi.org/10.1016/j.ssci.2024.106653
  10. [10] A. Nasri, A. Farsi, S. P. Hernandez, and S. Alboghebeish, “Impact of observational modeling on quiet eye duration and free-throw performance in basketball,” Learning and Motivation, Vol.89, Article No.102070, 2025. https://doi.org/10.1016/j.lmot.2024.102070
  11. [11] S. Taki and S. Yonezawa, “Motion analysis of lathe machining work using a digital position display device,” Int. J. Automation Technol., Vol.16, No.5, pp. 625-633, 2022. https://doi.org/10.20965/ijat.2022.p0625
  12. [12] K. Jarosz, Y. T. Chen, and R. Liu, “Investigating the differences in human behavior between conventional machining and CNC machining for future workforce development: A case study,” J. of Manufacturing Processes, Vol.96, pp. 176-192, 2023. https://doi.org/10.1016/j.jmapro.2023.04.037
  13. [13] M. Lušić, C. Fischer, K. S. Braz, M. Alam, R. Hornfeck, and J. Franke, “Static versus dynamic provision of worker information in manual assembly: A comparative study using eye tracking to investigate the impact on productivity and added value based on industrial case examples,” Procedia CIRP, Vol.57, pp. 504-509, 2016. https://doi.org/10.1016/j.procir.2016.11.087
  14. [14] J. Das, G. L. Bales, Z. Kong, and B. Linke, “Integrating operator information for manual grinding and characterization of process performance based on operator profile,” J. of Manufacturing Science and Engineering, Vol.140, No.8, Article No.081011, 2018. https://doi.org/10.1115/1.4040266
  15. [15] J. Niemann, A. Basson, C. Fussenecker, K. Kruger, M. Schlösser, S. Turek, and H. U. Amarnath, “Implementation of eye-tracking technology in holonic manufacturing systems,” Procedia – Social and Behavioral Sciences, Vol.238, pp. 37-45, 2018. https://doi.org/10.1016/j.sbspro.2018.03.005
  16. [16] I. Nishida and K. Shirase, “Automated process planning system for end-milling operation by cad model in stl format,” Int. J. Automation Technol., Vol.15, No.2, pp. 149-157, 2021. https://doi.org/10.20965/ijat.2021.p0149
  17. [17] S. Kanai, T. Shibata, and T. Kawashima, “Feature-based 3D process planning for MEMS fabrication,” Int. J. Automation Technol., Vol.8, No.3, pp. 406-419, 2014. https://doi.org/10.20965/ijat.2014.p0406
  18. [18] D. Dietrich, M. Neubauer, A. Lechler, and A. Verl, “Automated manufacturing toolchain using skill-based digital twins,” Procedia CIRP, Vol.128, pp. 923-928, 2024. https://doi.org/10.1016/j.procir.2024.06.045
  19. [19] K. Dwijayanti and H. Aoyama, “Basic study on process planning for turning-milling center based on machining feature recognition,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.8, No.4, Article No.JAMDSM0058, 2014. https://doi.org/10.1299/jamdsm.2014jamdsm0058
  20. [20] N. Komura, K. Matsumoto, S. Igari, T. Ogawa, S. Fujita, and K. Nakamoto, “Computer aided process planning for rough machining based on machine learning with certainty evaluation of inferred results,” Int. J. Automation Technol., Vol.17, No.2, pp. 120-127, 2023. https://doi.org/10.20965/ijat.2023.p0120
  21. [21] T. Yoshikawa, F. Nakamura, E. Sogabe, and K. Nakamoto, “Acquisition of skills for process planning through eye tracking when understanding mechanical drawings,” Int. J. Automation Technol., Vol.17, No.2, pp. 128-135, 2023. https://doi.org/10.20965/ijat.2023.p0128
  22. [22] Y. Inoue and K. Nakamoto, “Development of a CAPP system for multi-tasking machine tools to deal with complicated machining operations,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.14, No.1, Article No.JAMDSM0006, 2020. https://doi.org/10.1299/jamdsm.2020jamdsm0006
  23. [23] X. Xu, L. Wang, and S. T. Newman, “Computer-aided process planning – A critical review of recent developments and future trends,” Int. J. of Computer Integrated Manufacturing, Vol.24, No.1, pp. 1-31, 2011. https://doi.org/10.1080/0951192X.2010.518632

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

Last updated on Sep. 05, 2025