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Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.
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Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
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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- Award ID(s):
- 1937128
- PAR ID:
- 10542305
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 545
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Convolutional neural network (CNN), a type of deep learning algorithm, is a powerful tool for analyzing visual images. It has been actively investigated to monitor metal additive manufacturing (AM) processes for quality control and has been proven effective. However, typical CNN algorithms inherently have two issues when used in metal AM processes. First, in many cases, acquiring datasets with sufficient quantity and quality, as well as necessary information, is challenging because of technical difficulties and/or cost intensiveness. Second, determining a near-optimal CNN model takes considerable effort and is time-consuming. This is because the types and quality of datasets can be significantly different with respect to different AM processes and materials. The study proposes a novel concatenated ensemble learning method to obtain a flexible and robust algorithm for in-situ anomaly detection in wire + arc additive manufacturing (WAAM), a type of wire-based direct energy deposition (DED) process. For this, data, as well as machine learning models, were seamlessly integrated to overcome the limitations and difficulties in acquiring sufficient data and finding a near-optimal machine learning model. Using inexpensively obtainable and comprehensive datasets from the WAAM process, the proposed method was investigated and validated. In contrast to the one-dimensional and two-dimensional CNN models’ accuracies of 81.6 % and 88.6 %, respectively, the proposed concatenated ensemble model achieved an accuracy of 98 %.more » « less
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Abstract Deep learning has impacted defect prediction in additive manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogeneous datasets—remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hamper data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This article introduces an FL framework to predict section-wise heat emission during laser powder bed fusion (LPBF), a vital process signature. It incorporates a customized long short-term memory (LSTM) model for each client, capturing the dynamic AM process's time-series properties without sharing sensitive information. Three advanced FL algorithms are integrated—federated averaging (FedAvg), FedProx, and FedAvgM—to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app10020545
