# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
## 1 Introduction
Therefore, it is necessary to tailor the algorithm according to practical image content. The features identified by handcrafted or shallow learning techniques are not discriminative for a complex condition. In recent years, due to the advance of deep learning, in particular, the convolutional neural network (CNN) has emerged as state of the art in terms of accuracy and robust for a number of computer vision tasks such as image classification, object detection, and segmentation [13; 17; 18; 19; 20]. Based on artificial neural networks, CNN discovers the distributed representation of its input data by transforming the low-level features into a more abstract and composite representation. LeCun proposed the LeNet model back in 1998, which included convolution layers and pooling layers for digits recognition [21]. However, restricted by the computation performance of the central processing unit (CPU) and graphics processing unit (GPU), CNN fell silent for several years. With the acceleration of GPU, AlexNet was proposed by [PERSON] in 2012 [22], and it significantly increased the accuracy and computation speed of CNN for ImageNet dataset classification. The significance of utilizing CNN-based methods in AM is that it can lead the trend towards real-time monitoring and quality evaluation for AM build parts. Due to the complexity of the physical process, it is difficult to predict the whole AM process via analytical models [23]. Current process monitoring in AM has been focused on statistical learning methods. [PERSON] investigated the ability of acoustic emission to detect and identify the defects in the LMD using a logistic regression model [24]. Supervised learning methods have been utilized to predict the porosity in the LMD process by [PERSON] [25]. Porosity-related features were extracted from the melt-pool thermal images and then converted into vectors by transformation and rescaling. The vectors were processed by K-nearest neighbors (K-NN), support vector machine (SVM), decision tree (DT), and linear discriminant analysis (LDA) algorithms [25]. [PERSON] collected the layerwise images using a high-resolution digital single-lens reflex camera [26]. The visual features were extracted and evaluated by linear SVM for binary defect classification. Likewise, metallic powder micrographs were identified by the scale-invariant feature transform method and represented by principal component analysis [27]. The statistical methods mentioned above rely on handcraft features. However, CNN-based methods can learn the features from the raw data and have achieved excellent performance in the engineering area. Lots of researchers have applied CNN into the area of material informatics. [PERSON] et al. compared the classic bag of visual words [28] and CNN-based representations on the steel microstructures image classifications. The calculated image-based features were then fed to an SVM [29] classifier. The results showed that the CNN methods offered the best classification performance [30]. Similarly, [PERSON] [20] and [PERSON] [31] successfully applied the traditional computer vision and CNN algorithms to micrography recognition tasks, which turned out that CNN represented the highest classification accuracies. Besides, CNN was adopted to link experimental microstructure with ionic conductivity for yttria-stabilized zirconia samples [32]. The CNN models have been applied in surface detection in bearing rollers, aluminum parts, and steel plates [33; 34; 35; 36; 37]. It was found out that CNN-based methods had better and more robust performance compared to the SVM classifiers. The objective of this work is to explore a good CNN-based architecture with its parameters for the robust inspection of LMD fabricated parts. We will first discuss the model training and then provide performance evaluation and failure analysis.