IJAT Vol.15 No.3 pp. 350-358
doi: 10.20965/ijat.2021.p0350


Prompt Estimation of Die and Mold Machining Time by AI Without NC Program

Hiroki Takizawa*,†, Hideki Aoyama**, and Song Cheol Won***

*School of Integrated Design Engineering, Keio University
3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa 223-8521, Japan

Corresponding author

**Department of System Design Engineering, Keio University, Yokohama, Japan

***UEL Corporation, Tokyo, Japan

November 9, 2020
January 20, 2021
May 5, 2021
machining time estimation, artificial intelligence, mold, die, no NC-data

Machining time estimation is essential for the due-date estimation of products as well as for production planning. Conventionally, machining time has been estimated by a computer aided manufacturing (CAM) system, which requires time and effort to create its numerical control (NC) program and requires machining expertise to operate it. In addition, among the problems with conventional methods, an error in the estimated machining time arises owing to the machine tool’s control characteristics. In this study, an artificial intelligence (AI)-based system capable of estimating machining time promptly and simply based on shape data without requiring any NC program is developed. The input data to the AI system are color information regarding the machined depths, which are used to estimate the rough-machining time, and color information regarding the machined surface curvature distributions to estimate the finish-machining time. Color information on the machined depths and machined surface curvature distributions is created using three-dimensional computer aided design (3D CAD) data. To build the AI system, the shape data and machining time data accumulated at the machining site are used, so that the machining time estimated reflects the machining method, machining expertise, and the machine tool characteristics employed.

Cite this article as:
Hiroki Takizawa, Hideki Aoyama, and Song Cheol Won, “Prompt Estimation of Die and Mold Machining Time by AI Without NC Program,” Int. J. Automation Technol., Vol.15, No.3, pp. 350-358, 2021.
Data files:
  1. [1] Y. Tanimizu, T. Sakaguchi, and N. Sugimura, “Genetic algorithm based reactive scheduling (1st Report, Modification of Production Schedule for Delays of Manufacturing Processes),” Trans. of the Japan Society of Mechanical Engineers, C, Vol.69, No.685, pp. 234-239, 2003.
  2. [2] G. Eguchi, “Production Scheduling of Batch Reactors with Combinatorial Optimization,” Kagaku Kogaku Ronbunshu, Vol.32, No.6, pp. 500-506, 2006.
  3. [3] E.-Y. Heo, D.-W. Kim, V.-H. Kim, and F. F. Chen, “Estimation of NC machining time using NC block distribution for sculptured surface machining,” Robotics and Computer-Integrated Manufacturing, Vol.27, Issues 5-6, pp. 437-446, 2016.
  4. [4] K. Saito, H. Aoyama, and N. Sano, “Cutter Location Generation Method for Improving Feedrate and Machining Accuracy Based on Control Characteristics of Machine Tool,” Trans. of the Japan Society of Mechanical Engineers, C, Vol.78, No.786, pp. 650-658, 2012.
  5. [5] K. Saito, H. Aoyama, and N. Sano, “Accurate estimation of cutting time based on control principle of machine tool,” Int. J. Automation Technol., Vol.10, No.3, pp. 429-437, 2016.
  6. [6] E. Y. Heo, B. H. Kim, and D. W. Kim, “Estimation of sculptured surface NC machining time,” Trans. Korean Soc. CAD/CAM Eng., Vol.8, No.4, pp. 254-261, 2003.
  7. [7] H. Siller, C. A. Rodriguez, and H. Ahuett, “Cycle time prediction in high speed milling operations for sculptured surface finishing,” J. of Materials Processing Technology, Vol.174, Issue 1-3, pp. 355-362, 2006.
  8. [8] B. S. So, Y. H. Jung, J. W. Park, and D. W. Lee, “Five-axis machining time estimation algorithm based on machine characteristics,” J. of Materials Processing Technology, Vol.187-188, pp. 37-40, 2007.
  9. [9] Y. Altintas and S. Tulsyan, “Prediction of part machining cycle times via virtual CNC,” CIRP Annals, Manufacturing Technology, Vol.64, Issue 1, pp. 361-364, 2015.
  10. [10] H. Aoyama, “State of the Art and Future Trend of 3D-CAD/CAM,” J. of the Japan Society for Precision Engineering, Vol.81, No.3, pp. 206-210, 2015.
  11. [11] H. Iwabe, K. Kikuchi, and K. Shirai, “Analysis of theoretical roughness of radius end milling (Geometrical analysis in case of contouring and scanning method and experiments),” Trans. of the Japan Society of Mechanical Engineers, C, Vol.81, No.832, 15-00289, 2015.
  12. [12] R. Sato, “Machined surface simulation techniques considering the motion errors of NC machine tools,” J. of the Japan Society for Precision Engineering, Vol.83, No.3, pp. 204-209, 2017.
  13. [13] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Proc. of the 28th Int. Conf. on Neural Information Processing Systems, pp. 91-99, 2015.
  14. [14] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 580-587, 2014.
  15. [15] T. Okatani, “Deep learning for image recognition,” J. of the Japanese Society for Artificial Intelligence, Vol.28, No.6, pp. 962-974, 2013.
  16. [16] S. Miao, Z. J. Wang, and R. Liao, “A CNN regression approach for real time 2D/3D registration,” IEEE Trans. on Medical Imaging, Vol.35, No.5, pp. 1352-1363, 2016.
  17. [17] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Proc. of the Int. Conf. on Learning Representations, pp. 1-14, 2015.
  18. [18] S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. on Knowledge and Data Engineering, Vol.22, No.10, pp. 1345-1359, 2010.
  19. [19] S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” IEEE Trans. on Knowledge and Data Engineering, Int. Conf. on Machine Learning, pp. 1-11, 2015.

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Last updated on May. 10, 2021