A High-Performance Deep Learning Algorithm for the Automated Optical Inspection of Laser Welding

Abstract

The battery industry has been growing fast because of strong demand from electric vehicle and power storage applications.Laser welding is a key process in battery manufacturing. To control the production quality, the industry has a great desire for defect inspection of automated laser welding. Recently, Convolutional Neural Networks (CNNs) have been applied with great success for detection, recognition, and classification. In this paper, using transfer learning theory and pre-training approach in Visual Geometry Group (VGG) model, we proposed the optimized VGG model to improve the efficiency of defect classification. Our model was applied on an industrial computer with images taken from a battery manufacturing production line and achieved a testing accuracy of 99.87%. The main contributions of this study are as follows: (1) Proved that the optimized VGG model, which was trained on a large image database, can be used for the defect classification of laser welding. (2) Demonstrated that the pre-trained VGG model has small model size, lower fault positive rate, shorter training time, and prediction time; so, it is more suitable for quality inspection in an industrial environment. Additionally, we visualized the convolutional layer and max-pooling layer to make it easy to view and optimize the model.

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

“…To solve this common problem, Yang et al [219] developed a promising and robust method as virtual defect rendering, that can solve the problem of small datasets. In a recent study [182], Yang et al developed a DCNN based system to detect and classify defects that can occur during laser welding in battery manufacturing. Besides that, they proposed a novel model called Visual Geometry Group (VGG) model to improve the efficiency of defect classification.…”

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

“…However, most of the traditional detection methods still need to rely on manual assistance to complete, especially when a certain amount of instrument debugging is required before testing, and the equipment development cost is high, which is not highly adaptable and limited by the equipment life and manufacturing accuracy. Innovative defect-detection techniques, particularly machine vision and deep-learning methods [ 54 , 55 , 56 ], have become the most popular in recent years and are one of the key technologies for automating defect detection due to their versatility and lack of reliance on human assistance. Compared to traditional defect detection methods, the new technologies offer better inspection results and lower costs, but still rely on large amounts of learned data to drive model updates and improve inspection accuracy.…”

Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs

“…Advanced technologies like AI and CV are also employed for inspection, such as: using machine vision for spur gears parameters measurement [11], using CV to detect gear tooth number [12], using artificial vision for quality control of spur gears [13], inspection of gear faults using support vector machines (SVMs) and artificial neural networks (ANNs) [14], determining fine-pitch gears centers using machine vision [15], gear faults with convolutional neural networks (CNNs) [16], gears diagnosis using CNNs [17] and inspection of plastic gears using ANN and SVM based method [18]. AI and CV are also used for other AI inspection related application like: dimensions inspection with machine vision [19], detection of defects in products [20], sugarcane varieties inspection [21], welding inspection [22] inspection of optical laser welding [23] and inspection of aerospace components [24]. Vibration signals were the source information in most of the gears related literature mentioned above.…”