Study on Process Design Based on Language Analysis and Image Discrimination Using CNN Deep Learning
Akio Hayashi and Yoshitaka Morimoto
Kanazawa Institute of Technology
7-1 Ohgigaoka, Nonoichi, Ishikawa 924-8501, Japan
At present, machining with numerically controlled (NC) machine tools is mostly performed by NC programs generated by computer-aided design and computer-aided manufacturing (CAD/CAM) systems. However, even if the machining shape to be machined is the same, there are numerous machining processes involving a series of operations such as determining the machining area, machining order, and machining conditions. These are entrusted to the user, and automation is difficult. In addition, these tasks depend on the experience and know-how of skilled engineers, and it is very difficult to convert them into algorithms and reflect them in the creation of NC programs. Therefore, in this study, artificial intelligence (AI) was used for the process design of multi-tasking machine tools, with the goal of determining and automating the process design using shape examples. We propose a shape recognition method that includes image analysis by AI. This image analysis makes it possible to determine the characteristics of the machining shape, and the machining operator can easily judge the machining process based on the CAD model. Furthermore, because there are shapes that cannot be determined from image data alone, shape features are also extracted from the STEP file of the CAD model. A language analysis of the STEP file can find the characteristic components and their numerical information to determine the coordinates of the shape features. By combining image analysis and language analysis, the method can easily judge the process based on the information in the CAD model. Finally, using the generated learning model and analysis program, we conducted a test to determine whether a multitasking machine tool is necessary for machining.
-  Y. Lei, S. Vyas, S. Gupta, and M. Shabaz, “AI based study on product development and process design,” Int. J. Syst. Assur. Eng. Manag., Vol.13, pp. 305-311, 2022.
-  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.
-  K. Koremura, Y. Inoue, and K. Nakamoto, “Machining Process Evaluation Indices for Developing a Computer Aided Process Planning System,” Int. J. Automation Technol., Vol.11, No.2, pp. 242-250, 2017.
-  K. Nakamoto, K. Shirase, H. Wakamatsu, A. Tsumaya, and E. Arai, “Automatic Production Planning System to Achieve Flexible Direct Machining,” JSME Int. J., Series C, Vol.47, No.1, pp. 136-143, 2004.
-  Y. Woo, E. Wang, Y. S. Kim, and H. M. Rho, “A Hybrid Feature Recognizer for Machining Process Planning Systems,” CIRP Annals – Manufacturing Technology, Vol.54, No.1, pp. 397-400, 2005.
-  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.
-  I. Nishida, S. Adachi, and K. Shirase, “Automated Process Planning System for End Milling Operation Constrained by Geometric Dimensioning and Tolerancing (GD&T),” Int. J. Automation Technol., Vol.13, No.6, pp. 825-833, 2019.
-  I. Nishida and K. Shirase, “Automatic determination of cutting conditions for NC program generation by reusing machining case data based on geometric properties of removal volume,” J. of Advanced Mechanical Design, Systems, and Manufacturing, Vol.12, No.4, JAMDSM0093, 2018.
-  Y. Shinoki, M. M. Isnaini, R. Sato, and K. Shirase, “Machining operation planning system which utilize past machining operation data to generate new NC program,” Trans. of the JSME, Vol.81, No.832, 2015 (in Japanese).
-  E. Arai, H. Akasaka, H. Wakamatsu, and K. Shirase, “Description Model of Designers’ Intention in CAD System and Application for Redesign Process, JSME Int. J. Series C Mechanical Systems,” Machine Elements and Manufacturing, Vol.43, No.1, pp. 177-182, 2000.
-  S. Igari, F. Tanaka, and M. Onosato, “Computer-aided operation planning for an actual machine tool based on updatable machining database and database-oriented planning algorithm,” Int. J. Automation Technol., Vol.6, No.6, pp. 717-723, 2012.
-  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.
-  F. Tanaka, H. Abe, S. Igari, and M. Onosato, “Integrated Information Model for Design, Machining, and Measuring Using Annotated, Features,” Int. J. Automation Technol., Vol.8, No.3, pp. 388-395, 2014.
-  M. El-Mehalawi and R. A. Miller, “A database system of mechanical components based on geometric and topological similarity. Part I: representation,” Computer-Aided Design, Vol.35, No.1, pp. 83-94, 2003.
-  M. El-Mehalawi and R. A. Miller, “A database system of mechanical components based on geometric and topological similarity. Part II: indexing, retrieval, matching and similarity assessment,” Computer-Aided Design, Vol.35, No.1, pp. 95-105, 2003.
-  W. Liu, L. Ma, and M. Cui, “Learning-Based Stereoscopic View Synthesis with Cascaded Deep Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.3, pp. 393-406, 2022.
-  H. Sasatake, R. Tasaki, T. Yamashita, and N. Uchiyama, “Imitation Learning System Design with Small Training Data for Flexible Tool Manipulation,” Int. J. Automation Technol., Vol.15, No.5, pp. 669-677, 2021.
-  Y. Umehara, Y. Tsukada, K. Nakamura, S. Tanaka, and K. Nakahata, “Research on Identification of Road Features from Point Cloud Data Using Deep Learning,” Int. J. Automation Technol., Vol.15, No.3, pp. 274-289, 2021.
-  D. Kato, K. Yoshitsugu, T. Hirogaki, E. Aoyama, and K. Takahashi, “Predicting Positioning Error and Finding Features for Large Industrial Robots Based on Deep Learning,” Int. J. Automation Technol., Vol.15, No.2, pp. 206-214, 2021.
-  D. Kanda, S. Kawai, and H. Nobuhara, “Visualization Method Corresponding to Regression Problems and Its Application to Deep Learning-Based Gaze Estimation Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 676-684, 2020.
-  K. Ikeda and A. Kamimura, “Hammering Acoustic Analysis Using Machine Learning Techniques for Piping Inspection,” J. Robot. Mechatron., Vol.32, No.4, pp. 789-797, 2020.
-  X. Liu, X. Yin, M. Wang, Y. Cai, and G. Qi, “Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.5, pp. 883-890, 2019.
-  T. Mizoguchi, “Evaluation of Classification Performance of Pole-Like Objects from MMS Images Using Convolutional Neural Network and Image Super Resolution,” Int. J. Automation Technol., Vol.12, No.3, pp. 369-375, 2018.
-  Sony Network Communications Inc., “Neural Network Console.” https://dl.sony.com/ja/ [Accessed September 1, 2022]
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.