Study on Process Design Based on Language Analysis and Image Discrimination Using CNN Deep Learning

Abstract

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.