Process planning is well known as the key toward achieving highly efficient machining. However, it is difficult to standardize machining skills for process planning, which depend heavily on skilled operators. Hence, in a previous study, a computer aided process planning (CAPP) system using machine learning is developed to determine the operation parameters for finish machining of dies and molds. On the other hand, in rough machining, it is assumed that some machining operations are conducted sequentially using a respective tool according to the workpiece shape, which induces a much higher complexity in process planning. Therefore, in this study, machine learning is adopted to determine operation parameters for rough machining. The developed CAPP system converts the removal volume into a voxel model and infers a machining operation for each voxel. The inferred machining operation is visualized using different colors and identified corresponding to the voxel. Finally, the removal volume is classified using three different machining operations. However, machine learning is said to have a critical problem in that it is difficult to understand the reasons for the inferred results. Hence, it is necessary for the CAPP system to demonstrate the certainty level of the determined operation parameters. Thus, this study proposes a method for calculating the degree of certainty. If an artificial neural network is trained sufficiently, similar inferred results would always be obtained. Consequently, by using the Monte Carlo dropout to delete weights at random, the certainty level is defined as the variance of the inferred results. To verify the usefulness of the CAPP system, a case study is conducted by assuming rough machining of dies and molds. The results confirm that the machining operations are inferred with high accuracy, and the proposed method is effective for evaluating the certainty of the inferred results.

1. [1] M. Yamada, T. Kondo, and K. Wakasa, “High Efficiency Machining for Integral Shaping from Simplicity Materials Using Five-Axis Machine Tools,” Int. J. Automation Technol., Vol.10, No.5, pp. 804-812, 2016.
2. [2] H. Narita, “Cutting Features between Surface Roughness in Feed Direction and Machining State of Radius End Mill Against Inclined Surfaces (In Case of Contour Machining and Five-Axis Machining with Constant Tilt Angle),” Int. J. Automation Technol., Vol.14, No.1, pp. 46-51, 2020.
3. [3] 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.
4. [4] Y. Shi, Y. Zhang, K. Xia, and R. Harik, “A Critical Review of Feature Recognition Techniques,” Computer-Aided Design and Applications, Vol.17, No.5, pp. 861-899, 2020.
5. [5] J. H. Han, M. Platt, and W. C. Regli, “Manufacturing Feature Recognition from Solid Models: A Status Report,” IEEE Trans. on Robotics and Automation, Vol.16, No.6, pp. 782-796, 2000.
6. [6] Y. S. Kim and E. Wang, “Recognition of Machining Features for Cast Then Machined Parts,” Computer-Aided Design, Vol.34, No.1, pp. 71-87, 2002.
7. [7] W. Fu, A. A. Eftekharian, and I. M. Campbell, “Automated Manufacturing Planning Approach Based on Volume Decomposition and Graph-grammars,” J. of Computing and Information Science in Engineering, Vol.13, No.2, 021010, 2013.
8. [8] E. Morinaga, T. Hara, H. Joko, H. Wakamatsu, and E. Arai, “Improvement of Computational Efficiency in Flexible Computer-Aided Process Planning,” Int. J. Automation Technol., Vol.8, No.3, pp. 396-405, 2014.
9. [9] 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, JAMDSM0006, 2020.
10. [10] 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, JAMDSM0108, 2020.
11. [11] T. Asano, Y. Watanabe, and K. Nakamoto, “Proposal of A Machining Feature Recognition Method to Reflect Product and Manufacturing Information,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.16, No.1, JAMDSM0015, 2022.
12. [12] K. V. Rao, B. S. N. Murthy, and N. M. Rao, “Prediction of Cutting Tool Wear, Surface Roughness and Vibration of Work Piece in Boring of AISI 316 Steel with Artificial Neural Network,” Measurement, Vol.51, pp. 63-70, 2014.
13. [13] M. Hashimoto and K. Nakamoto, “A Neural Network Based Process Planning System to Infer Tool Path Pattern for Complicated Surface Machining,” Int. J. Automation Technol., Vol.13, No.1, pp. 67-73, 2019.
14. [14] H. Takizawa, H. Aoyama, and S. C. 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.
15. [15] R. Uchiyama, Y. Inoue, F. Uchiyama, and T. Matsumura, “Optimization in Milling of Polymer Materials for High Quality Surfaces,” Int. J. Automation Technol., Vol.15, No.4, pp. 512-520, 2021.
16. [16] M. Hashimoto and K. Nakamoto, “Process Planning for Die and Mold Machining Based on Pattern Recognition and Deep Learning,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.15, No.2, JAMDSM0015, 2021.
17. [17] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation,” Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, pp. 424-432, 2016.
18. [18] Y. Gal and Z. Ghahramani, “Dropout as A Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” Int. Conf. on Machine Learning, pp. 1050-1059, 2016.
19. [19] T. Zhou, T. Han, and E. L. Droguett, “Towards Trustworthy Machine Fault Diagnosis: A Probabilistic Bayesian Deep Learning Framework,” Reliability Engineering & System Safety, Vol.224, 108525, 2022. https://doi.org/10.1016/j.ress.2022.108525
20. [20] X. Zhang, F. T. S. Chan, and S. Mahadevan, “Explainable Machine Learning in Image Classification Models: An Uncertainty Quantification Perspective,” Knowledge-Based Systems, Vol.243, 108418, 2022. https://doi.org/10.1016/j.knosys.2022.108418
21. [21] Y. Hiasa, Y. Otake, M. Takao, T. Ogawa, N. Sugano, and Y. Sato, “Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling,” IEEE Trans. on Medical Imaging, Vol.39, No.4, pp. 1030-1040, 2020.