Name
Title | ||
|---|---|---|
A Lightweight Deep Learning Algorithm For Inspection Of Laser Welding Defects On Safety Vent Of Power Battery |
Abstract | ||
|---|---|---|
A B S T R A C T With the wide applications of power battery in the automobile industries, the safety of power battery is becoming an increasingly prominent problem. At present, a safety vent welded on the battery plate could prevent unpredictable explosions. To perform quality control, the inspection of laser welding defects on safety vent is a critical issue. In this paper, based on the theory of convolutional neural network (CNN) and the technique of transfer learning, a pre-trained SqueezeNet model with small model size and low computation complexity was proposed. We totally collected 34537 images from the production line, and built a 2-classifications dataset and a 7-classifications dataset respectively. It proves that our proposed model achieved better accuracy than the other six contrastive CNN models in these two classification tasks. Specifically, it obtained an accuracy of 99.57 % in the 2-classifications task and an accuracy of 95.58 % in the 7-classifications task. Besides, the model features lightweight and high-speed with only 1.2 MB model size and 4.9 ms average test time; so, it is more suitable for welding quality inspection of safety vent in an industrial production line. Additionally, we ported our pre-trained SqueezeNet and the other four contrastive models to Raspberry Pi embedded system to evaluate their response time. Our proposed model has achieved well response time of less than 330 ms. Our experiments indicate that the proper CNN model can help to conduct the laser welding quality inspection tasks that are usually performed by humans. (c) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compind.2020.103306 | COMPUTERS IN INDUSTRY |
Keywords | DocType | Volume |
Automatic optical inspection, Convolutional neural network (CNN), Image classification, Laser welding defects, SqueezeNet model | Journal | 123 |
ISSN | Citations | PageRank |
0166-3615 | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yatao Yang | 1 | 2 | 3.07 |
| Runze Yang | 2 | 1 | 0.35 |
| Longhui Pan | 3 | 1 | 0.35 |
| Junxian Ma | 4 | 2 | 1.04 |
| Yishuang Zhu | 5 | 1 | 0.35 |
| Tao Diao | 6 | 1 | 0.35 |
| Li Zhang | 7 | 1 | 0.35 |