Development an advanced neural network for recognition of machining feature

In the process of producing a part with the help of computer aided manufacturing, the required information is created for the workpieces whose design model is specified. To prepare machining instructions, the design information is expressed in a pattern called a feature. In this research an advanced artificial intelligence system has been introduced to identify machining features with the help of deep learning method. The proposed method has been prepared with the help of two-dimensional convolutional networks in deep learning. It can identify machining features from the image of a workpiece. The innovations of this research, in addition to introducing a powerful practical and new method for automatic machining features recognition in the field of computer aided process planning, is identifying features that have geometric interference in a workpiece. The previous methods of automatic machining features recognitions have not been able to solve this problem. Furthermore, the lack of need for different CAD output files and the use of an image of a workpiece to identify machining features are the capabilities of the system introduced in this research. Other capabilities of the proposed method are the ability to identify machining features with different image formats such as image with wire frame format, constructive solid geometry format, image of workpiece with different materials and taken with ordinary cameras such as mobile cellphone camera and other imaging devices. The accuracy of detecting machining features in the image of a workpiece is measured %88 and detection error is measured 0.1 using proposed method.