Porosity prediction: Supervised-learning of thermal history for direct laser deposition

Khanzadeh, Mojtaba; Chowdhury, Sudipta; Marufuzzaman, Mohammad; Tschopp, Mark A.; Bian, Linkan

doi:10.1016/j.jmsy.2018.04.001

Cited by 213 publications

(59 citation statements)

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“…Previous studies have shown that machine learning can be deployed for defect detection in the L-PBF process, using various techniques like acoustic sensors [34], thermal [36], grayscale [32], or high-resolution imaging [31]. While the current benchmarks range from 77 to 98% accuracy in detecting errors, they rely on either extensive data preprocessing or upon additional imaging techniques.…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy using raw data is at 70% and goes up to 93% after performing a fast Fourier transformation. Khanzadeh et al [36] used melt pool monitoring images as a source of comparison for different supervised ML techniques. The best tested algorithm was k-nearest neighbor with an accuracy of about 98% for the detection of melt pool anomalies and potentially microstructure anomalies in real time.…”

Section: Related Workmentioning

confidence: 99%

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A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

et al. 2020

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Additive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal parameter settings are usually detected after the process. To detect these defects during the printing, different process monitoring techniques such as melt pool monitoring or off-axis infrared monitoring have been proposed. In this work, we used a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures, to detect printing defects. For the network training, a k-fold cross validation and a hold-out cross validation were used. With these techniques, defects such as delamination and splatter can be recognized with an accuracy of 96.80%. In addition, the model was evaluated with computing class activation heatmaps. The architecture is very small and has low computing costs, which means that it is suitable to operate in real time even on less powerful hardware.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

et al. 2020

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“…For different types of modeling methods, machine learning can be classified as unsupervised learning, semi-supervised learning, and supervised learning [9][10][11]. The focus of this paper is on supervised learning methods.…”

Section: Introductionmentioning

confidence: 99%

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

2019

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In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.

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“…Gaja 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 Khanzadeh [25]. Porosity-related features were extracted from the melt-pool thermal images and then converted into vectors by transformation and rescaling.…”

mentioning

confidence: 99%

“…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]. Gobert collected the layerwise images using a high-resolution digital single-lens reflex camera [26].…”

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confidence: 99%

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Cui

Zhang

et al. 2020

Applied Sciences

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Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

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Porosity prediction: Supervised-learning of thermal history for direct laser deposition

Cited by 213 publications

References 50 publications

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

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