Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning ca...
Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.
Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.
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가설 설정
Thus, an accuracy test was performed using 60 new input images showing crack, porosity, and hole defects. In the analysis of Case 5, the test accuracy can be determined as shown in Figure 4. For three labels, if a probability exceeds 50 %, the labeling is successful. If the judgment criterion is not satisfied, the labeling is failed.
제안 방법
To increase model accuracy, large amounts of training data were made from missing images using the data augmentation method, and the CNN image labeling algorithm was trained with augmented training data. Afterward, to find the optimal condition of the labeling algorithm, a case study was performed by combining the number of the fully connected layers, the loss function, and the algorithm optimizer. As a result, the optimal model in the case study was able to obtain a model accuracy of 99.
Defects were labeled as “crack”, “hole”, and “porosity”, and studies were conducted to classify them more accurately.
Finally, this study made it possible to classify the defects based on images, and confirmed that post-processing can be performed continuously if the solution is introduced according to the class of the defect. If in the future the image labeling algorithm is applied to the automation process, it is expected that the system will be able to find the defects and perform the follow-up process by itself, and consequently improve the quality of the final result.
To improve the accuracy of the algorithm, the case study was performed considering the structures of the optimizer, the loss function, and the fully connected layer. Furthermore, in an attempt to analyze problems such as overfitting or underfitting, the model accuracy derived from the image labeling algorithm was compared with the accuracy of the classification test using new images.
Moreover, to prevent overfitting from occurring in the algorithm training process, drop-out layers that arbitrarily throw away nodes in the network are added[14]. In addition, the case study was conducted in terms of the training speed of convergence and the accuracy of the labeling algorithm according to the number of fully connected layers, the type of optimizer, and the loss function of the algorithm.
Following the purpose of use, the fully-connected layer of classification, segmentation, and object detection is applied[13]. In this paper, an image labeling algorithm that extracts and classifies the characteristics of images and labels is constructed (Figure 2b). Moreover, to prevent overfitting from occurring in the algorithm training process, drop-out layers that arbitrarily throw away nodes in the network are added[14].
In this paper, to detect defects and repair them in a process worked layer-by-layer as in AM, defects in the cross-section image were classified and automatic restoration was suggested using the CNN algorithm. To increase model accuracy, large amounts of training data were made from missing images using the data augmentation method, and the CNN image labeling algorithm was trained with augmented training data.
[6] found metal defects during a powder redistribution process based on monitoring images. The causes of these defects were classified into six categories: recoater hopping, recoater streaking, debris, super-elevation, part damage, and incomplete spreading.
From a variance and bias point of view, the accuracy of the image labeling model needs to be confirmed whether it is underfitting or overfitting. Thus, an accuracy test was performed using 60 new input images showing crack, porosity, and hole defects. In the analysis of Case 5, the test accuracy can be determined as shown in Figure 4.
Additionally, in this paper, a numerical basis was needed to determine whether algorithm learning was occurring correctly. Thus, the reliability of the learning results was identified by comparing them with the accuracy of the algorithm by performing labeling with a new image to evaluate the accuracy of the test. Additionally, the advantage of the image labeling CNN algorithm is that it allows an easy classification of different types of defects by understanding the characteristic features of the images.
In the process of preparing the training data, the method of augmenting a small number of data using augmentation is presented, and the process of converting images into binary data is described. To improve the accuracy of the algorithm, the case study was performed considering the structures of the optimizer, the loss function, and the fully connected layer. Furthermore, in an attempt to analyze problems such as overfitting or underfitting, the model accuracy derived from the image labeling algorithm was compared with the accuracy of the classification test using new images.
이론/모형
Furthermore, data processing speed has been improved with the advancement of computing power, accelerating the development of deep learning algorithms[5]. In this paper, the classification of metal cross-sectional defects is performed using the convolution neural network (CNN) algorithm[4], which is specialized in extracting and classifying image characteristics. Defects in typical metal additive manufacturing processes are common problems in powder bed fusion (PBF) and direct metal deposition (DMD); therefore, studies have been conducted to solve them as follows.
성능/효과
Afterward, to find the optimal condition of the labeling algorithm, a case study was performed by combining the number of the fully connected layers, the loss function, and the algorithm optimizer. As a result, the optimal model in the case study was able to obtain a model accuracy of 99.7 % using algorithms learned from a probability distribution close to the training data.
Thus, the test accuracy was confirmed to check and compare it with the model accuracy. As a result, the test accuracy of Case 5 was 98 % compared to 99.7 % of the model accuracy and it was selected as the best labeling algorithm.
The generated binary data contains pixel data and image label data. In conclusion, 25,920 (80 %) and 6,480 (20 %) image data sets were organized for training and validation.
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