Early-Warning System for Copper Alloy Abnormal Molten Pool in Wire-Arc Additive Manufacturing via Convolutional Neural Network Method

Zhang, Tianyang; Wang, Lvyuan; Xu, Chen; Cheng, Jingjing; Wang, Kehong

doi:10.1007/s11665-023-07949-8

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The results show that the CNN model can effectively classify different types of melt pool images. Zhang et al [ 19 ] established a WAAM melt pool early warning system. This system not only uses a convolutional neural network to construct a melt pool image classification model, but also uses a Grad-CAM algorithm to visually analyze the model, which can classify melt pool images into splash, collapse, short overlapping distance, long overlapping distance, normal overlapping, and normal stacking categories.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Luo,

Qian,

et al. 2024

Sensors

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Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

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Section: Introductionmentioning

confidence: 99%

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Luo,

Qian,

et al. 2024

Sensors

View full text Add to dashboard Cite

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Acoustic Signal-Based Defect Identification for Directed Energy Deposition-Arc Using Wavelet Time–Frequency Diagrams

Zhang,

Wu,

Tang

et al. 2024

Sensors

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Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time–frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time–frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time.

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Early-Warning System for Copper Alloy Abnormal Molten Pool in Wire-Arc Additive Manufacturing via Convolutional Neural Network Method

Cited by 2 publications

References 31 publications

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction

Acoustic Signal-Based Defect Identification for Directed Energy Deposition-Arc Using Wavelet Time–Frequency Diagrams

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