A Convolution Neural Network for Parts Recognition Using Data Augmentation
In order to solve the problem of classifying different kinds of parts automatically, a simplified convolutional neural network was designed. Firstly, image data of the parts that were going to be classified were collected, and were divided into training dataset and testing dataset. Then, a simplified convolutional neural network that was suitable for part recognition was designed. A data augmentation method was raised to augment both train data and test data. The result of the prediction was got by voting. The accuracy was highly improved after the data augmentation was used. The method in this paper classified 29 different sizes of screws, nuts and washers successfully, and validates that data augmentation can improve the performance of the network strongly when the dataset is not big enough.
We present a computer simulation study of thin crystalline multilayers constructed from two FCC solids with differing lattice constants and binding energies. Both materials are described by Lennard-Jones interatomic potentials and initially have the same orientation and coherent interfaces. We have studied systems in which interfaces are perpendicular to the common [100] and [111] directions, respectively. A novel technique for analyzing local atomic ordering, Common Neighbor Analysis, is used to identify structural characteristics in these systems. We have found several structural changes in the layers of smaller atoms, including an FCC to HCP transition. In a system with (111) texture, a coherent interface to incoherent interface transformation is observed. Calculations of elastic constants of these multilayer structures show that elastic anomalies are associated with the structural variations.