An Evaluation Method of Acceptable and Failed Spot Welding Products Based on Image Classification with Transfer Learning Technique

The1 spot welding technique is widely used in the industrial production line, but it suffers inconsistent quality. Therefore, the evaluation of the spot-welding product is of great importance for the industrial production. Many destructive and nondestructive methods have been used in the product evaluation, but they are inefficient and hard to be applied in the mass production. In recent year, machine vision method has been used to differentiate the acceptable and failed spot welding products according to their solder joint images. This opened new opportunities for the spot welding product quality evaluation using digital image technique. However, this method cannot achieve general performance on different spot-welding products as well as ideal classification accuracy. In this work, a novel method which based on the transfer learning technique was proposed to classify the spot-welding products according to their solder joint images. The GoogLeNet was used to extract the features of the solder joint image, which is pretrained on the ImageNet. Then a multilayer perceptron (MLP) was used to classify these images. Our method achieved a final classification accuracy of 96.99% on a testing set included 334 images.

[1]  Yuan Zhang,et al.  Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function , 2018, Neurocomputing.

[2]  Pierluigi Carcagnì,et al.  Automatic visual monitoring of welding procedure in stainless steel kegs , 2017 .

[3]  Z D Ishaya,et al.  Destructive testing and production system integrity , 2013 .

[4]  Rodney W. Johnson,et al.  Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy , 1980, IEEE Trans. Inf. Theory.

[5]  Fujun Wang,et al.  A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier , 2015 .

[6]  Jian Cheng,et al.  Additive Margin Softmax for Face Verification , 2018, IEEE Signal Processing Letters.

[7]  Chee Khiang Pang,et al.  Quality monitoring of spot welding with advanced signal processing and data-driven techniques , 2018 .

[8]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[9]  Feng Zhou,et al.  A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning , 2018, Pattern Recognit..

[10]  Samo Simon.  Image-based electrode tip displacement in resistance spot welding , 2012 .

[11]  Fabio Del Frate,et al.  Use of Neural Networks for Automatic Classification From High-Resolution Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Mohamed Batouche,et al.  Investigation on deep learning for off-line handwritten Arabic character recognition , 2017, Cognitive Systems Research.

[13]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[14]  Touradj Ebrahimi,et al.  Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model , 2016, MADiMa @ ACM Multimedia.

[15]  Yan Yan,et al.  Multi-label learning based deep transfer neural network for facial attribute classification , 2018, Pattern Recognit..

[16]  Ye Wei,et al.  Automatic Detection of Welding Defects using Deep Neural Network , 2018 .

[17]  Dawei Zhao,et al.  Quality Estimation in Small Scale Resistance Spot Welding of Titanium Alloy Based on Dynamic Electrical Signals , 2018 .

[18]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Lianwen Jin,et al.  High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[20]  Hayder Radha,et al.  Deep learning algorithm for autonomous driving using GoogLeNet , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[21]  M. Mohammed Thaha,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2019, Journal of Medical Systems.