Recently, convolutional neural networks (CNNs) are used essentially to classify images as it helps to cluster them by similarity and perform recognition. In this paper, a design tool that helps to develop different deep CNNs (DCNNs) is presented. As an example, a DCNN is designed by using the developed tool to use it for vision based inspection to recognize undesirable defects such as crack, burr, protrusion and chipping which normally occur in the manufacturing process of resin molded articles. An image generator is implemented to efficiently produce many similar images for training. Similar images are easily generated by rotating, translating, scaling and transforming original images. The designed DCNN is trained by using the produced images and then tested through classification experiments. The usefulness of the design tool and the basic performance of the designed DCNN are introduced.
|Journal||IOP Conference Series: Materials Science and Engineering|
|Publication status||Published - Nov 6 2018|
|Event||2018 4th International Conference on Applied Materials and Manufacturing Technology, ICAMMT 2018 - Nanchang, China|
Duration: May 25 2018 → May 27 2018
ASJC Scopus subject areas
- Materials Science(all)