## 1 Introduction  
In smart manufacturing, enormous amounts of data are acquired from sensors. The complexity of data poses challenges when processing these data, such as a high computational time. Deep learning, which achieves remarkable performance for various applications, provides advanced analytical tools to overcome such issues in smart manufacturing [1]. This technique has been progressing because of many factors such as the developing graphics processing units and the increasing amounts of data that need to be processed. Therefore, studies investigating deep learning as data-driven artificial intelligence techniques are rapidly increasing. It is applied in various domains, such as optimization, control and troubleshooting, and manufacturing. Specifically, deep learning is used in applications such as visual inspection processes, fault diagnoses of induction motors and boiler tubes, crack detection, and monitoring of tool condition [2; 3; 4; 5; 6; 7; 8]. Deep learning stands out in the research domains that are related to visual systems. Since 2010, the ImageNet Large Scale Visual Recognition Challenge has been organized every year [9]. Several object recognition, object detection, and object classification algorithms are evaluated in this challenge. In 2012, a deep learning algorithm based on convolutional neural networks was developedfor object recognition, which showed significant improvements in object recognition. AlexNet won the challenge in the same year, i.e., 2012, with a top five test error rate of 15.3% for 1000 different object categories [10]. VGGNet and GoogLeNet were proposed in 2014, whereas ResNet was proposed in 2015; as time progressed, these deep learning algorithms proved to be more efficient in performing object classification than humans [11; 12; 13]. In deep learning, features are extracted via self-learning. Training the network begins with preparing the training data. We must collect enormous amounts of data to train the deep learning network and apply it to object recognition, as the classification accuracy is related to the amount of training data. One method of collecting training data involves downloading images from the Internet using image-crawling tools or by manually saving each image individually. In addition, we can utilize the existing image databases such as ImageNet and Microsoft CoCo [14; 15]. However, collecting numerous image data for image classification of the assembly of electrical components or picking and placing these components is challenging. As the images of such components for assembly or picking are generally uncommon, it is difficult to obtain numerous component images in existing databases or websites. Furthermore, it is significantly difficult to capture the parts manually to create a sufficient amount of training data. As another example, it is also challenging to collect the training images in visual inspection processes such as discriminating defective products and crack or defect detection. Because the defective rate is meager in manufacturing domains, sample images of defects are few. However, generally, to enhance the performance of deep learning, we must collect numerous training datasets. This is because the classification result is influenced by the number of training images. To compensate for the data deficiency, a pre-trained model can be retrained using the available training data. This is because the pre-trained model already contains the information of the extracted features favorably in the weights of the feature extraction layers. However, this method is limited in terms of increasing the classification accuracy, as eventually, it needs to augment the training data to improve its classification performance. Collecting extensive training datasets is another solution for overcoming the problem of data deficiency. Many researches demonstrated the relationship between the amount of the training dataset and classification accuracy. There are many data augmentation techniques to increase the number of training images.