Improving Stability of Welding Model with ME-ELM

Ye, Jianxiong; Ye, Han; Li, Zhigang; Peng, Xingling; Zhou, Jinlan; Guo, Bo

doi:10.1007/978-981-10-7043-3_4

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Su et al [25] built an automatic defect identification system for solder joints by extracting the texture features of weld defects. Han et al [26] combined ELM and M-estimation and proposed a new ME-ELM algorithm. This algorithm can effectively improve the anti-interference and robustness of the model, and it provides high accuracy in predicting welding defects.…”

Section: State-of-the-artmentioning

confidence: 99%

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

et al. 2021

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Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

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Section: State-of-the-artmentioning

confidence: 99%

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

et al. 2021

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“…In order to verify the prediction accuracy of the proposed TL-MobileNet model, the TL-MobileNet model was compared with other models for welding defect detection. The other models include: back propagation (BP) [4], Knearest neighbors (KNN) [4], Extreme Learning Machine [18], Histogram of Oriented Gridients (HOG) [19], Convolutional neural networks (CNN) [31], artificial neural network (ANN) [11], support vector machines with principal component analysis (PCA-SVM) [32] and Extreme learning machine [17]). Table 6 represents the comparisons between the accuracy of the proposed method and that of other researchers in the prediction of welding defects (the input image size 96×96).…”

Section: ) the Influence Of Image Size M On Prediction Accuracymentioning

confidence: 99%

“…Su et al [17] established an automatic defect identification system for solder joints by extracting texture features of welding defects. Han et al [18] combined M-estimation with ELM and proposed a new ME-ELM algorithm, the algorithm can effectively improve the anti-interference and robustness of the model, and has high accuracy in the prediction of welding defects. Usually, these shallow machine learning methods are combined with the feature extraction process, which ultimately affects the machine learning prediction results.…”

Section: Introductionmentioning

confidence: 99%

A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects

et al. 2020

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Welding quality directly affects the welding structure's service performance and life. Hence, the effective monitoring welding defects is essential to ensure the quality of the weld structure. Owing to the non-uniformity of the shape, position and size of welding defects, it is a complicated task to analyze and evaluate the acquired welding defects images manually. Fortunately, deep learning has been successfully applied to image analysis and target recognition. However, the use of deep learning to identify welding defects is time-consuming and less accurate due to the lack of adequate training data samples, which easily cause redundancy into the classifier. In this situation, we proposed a new transfer learning model based on MobileNet as a welding defect feature extractor. By using the ImageNet dataset (non-welding defect data) to pre-train a MobileNet model, migrate the MobileNet model to the welding defects classification field. This article suggested a new TL-MobileNet structure by adding a new Full Connection layer (FC-128) and a Softmax classifier into a traditional model called MobileNet. The entire training process of TL-MobileNet model has been successfully optimized by the DropBlock technology and Global average pooling (GAP) method. They can effectively accelerate the convergence rate and improve the classification network generalization. By testing the proposed TL-MobileNet on the welding defects dataset, it turned out our model prediction accuracy has arrived at 97.69%. The experimental results show that in several aspects, TL-MobileNet have better performance than other transfer learning models and traditional neural network methods.

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“…Because to the complex interrelationship between these welding parameters, it is hard to deduce an appropriate physics model in continuous welding process with changing parameters [ 5 ]. In recent decades, researchers have applied various mathematical models to build the relationship between multi-input and multi-output parameters, e.g., factorial design, linear and nonlinear regression, response surface methodology, and artificial neural network (ANN) [ 4 , 6 , 7 , 8 , 9 , 10 , 11 ]. These design of experiments (DOE) techniques apply to different areas according to the complex relationship between input and output parameters, and they achieve high accuracy and efficiency in modeling.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Dong

Gao

2021

Materials

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Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

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Improving Stability of Welding Model with ME-ELM

Cited by 3 publications

References 18 publications

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

A New Image Recognition and Classification Method Combining Transfer Learning Algorithm and MobileNet Model for Welding Defects

Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

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