IJAT Vol.17 No.2 pp. 128-135
doi: 10.20965/ijat.2023.p0128

Research Paper:

Acquisition of Skills for Process Planning Through Eye Tracking When Understanding Mechanical Drawings

Takumu Yoshikawa*, Fumihiro Nakamura*, Eisuke Sogabe**, and Keiichi Nakamoto*,†

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

Corresponding author

**Okuma Corporation, Oguchi, Japan

August 1, 2022
October 21, 2022
March 5, 2023
process planning, mechanical drawing, machining skill, eye tracking, mechanical part

In parts machining, process planning is typically conducted by skillful operators. The quality of machining is highly dependent on process planning, which determines the operation parameters, such as the operation sequence and cutting tool. To achieve high-quality machining without depending on the skill level of the operators, standardization of process planning is desired. Therefore, it is necessary to extract and generalize skills related to process planning. Furthermore, eye tracking technology is expected to visualize unconscious human behavior. In this study, eye tracking technology is adopted to detect the movement of the operator’s eyes and gather gaze data when understanding mechanical drawings. Gaze data are analyzed using a heat map and bubble chart to identify differences in eye movement according to skill level. The analyzed heat maps indicate that the gazes of the skillful operator are gathered because the operator focuses on the area that is strongly related to the quality of machining. The analyzed bubble charts also indicate that the skillful operator considers the machining process by checking annotations, then understands the shape, and finally verifies the numerical values of the annotations. From the results of interviews performed based on the analysis, the individual skill could be effectively extracted in detail, particularly the skill regarding the operation sequence. Furthermore, the acquired skills are incorporated into a computer-aided process planning system developed in a previous study. The operation sequence is modified to reflect the acquired skills. Machining experiments confirmed the effectiveness of adopting operators’ skills in process planning.

Cite this article as:
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.
Data files:
  1. [1] S. Ibaraki, A. Matsubara, and M. Murozumi, “Efficiency comparison of cutting strategies for end milling processes under feedrate scheduling,” Int. J. Automation Technol., Vol.2, No.5, pp. 377-383, 2008.
  2. [2] 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.
  3. [3] H. Kodama, T. Hirogaki, E. Aoyama, and K. Ogawa, “Investigation of end-milling condition decision methodology based on data mining for tool catalog database,” Int. J. Automation Technol., Vol.6, No.1, pp. 61-74, 2012.
  4. [4] 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.
  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.
  6. [6] T. Sawa, “Automating the mold-material grinding process,” Int. J. Automation Technol., Vol.13, No.6, pp. 722-727, 2019.
  7. [7] P. A. Punde, M. E. Jadhav, and R. R. Manza, “A Study of Eye Tracking Technology and Its Applications,” Proc. of the 1st Int. Conf. on Intelligent Systems and Information Management, pp. 86-90, 2017.
  8. [8] B. T. Carter and S. G. Luke, “Best practices in eye tracking research,” Int. J. of Psychophysiology, Vol.155, pp. 49-62, 2020.
  9. [9] K. Rayner, “Eye movements and attention in reading, scene perception, and visual research,” Quarterly J. of Experimental Psychology, Vol.62, No.8, pp. 1457-1506, 2009.
  10. [10] M. Rolfs, “Attention in active vision: a perspective on perceptual continuity across saccades,” Perception, Vol.44, Nos.8-9, pp. 900-919, 2015.
  11. [11] Y. Takeo and W. Nastu, “Research on influence of worker’s skill proficiency on tool wear and surface waviness during tool feed stop in lathe work,” J. of the Japan Society for Abrasive Technology, Vol.57, No.9, pp. 588-593, 2013 (in Japanese).
  12. [12] 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.
  13. [13] J. Dou, L. Zhang, Q. Zhao, Q. Pei, and J. Qin, “Human-machine interface evaluation of CNC machine control panel through multidimensional experimental data synchronous testing analysis method,” Int. J. of Performability Engineering, Vol.13, No.8, pp. 1195-1205, 2017.
  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, 081011, 2018.
  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.
  16. [16] J. Nakamura, S. Nagayoshi, and N. Komiya, “Effects of anticipation in manufacturing processes: towards visual search modeling in human factors,” Proc. of the 8th Multidisciplinary Int. Social Networks Conf., pp. 15-20, 2021.,
  17. [17] T. Ihara and Y. Ito, “A new concept of CAPP based on flair of experienced engineers – analyses of decision-making processes of experienced process engineers,” CIRP Annals, Vol.40, No.1, pp. 437-440, 1991.
  18. [18] K. Yamaguchi, M. Yamaguchi, Y. Kondo, and S. Sakamoto, “Analysis of turning process relative to machining technician’s skills,” Advanced Materials Research, Vols.655-657, pp. 2152-2155, 2013.
  19. [19] Q. Lohmeyer and M. Meboldt, “How we understand engineering drawings: an eye tracking study investigating skimming and scrutinizing sequences,” Proc. of the Int. Conf. on Engineering Design, pp. 359-368, 2015.
  20. [20] 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, 2020.
  21. [21] Y. Watanabe and K. Nakamoto, “Proposal of a machining features recognition method for 5-Axis index milling on multi-tasking machine tools,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.14, No.7, 2020.

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

Last updated on Mar. 19, 2023