A Multi-Sensor Data Fusion System for Laser Welding Process Monitoring

Fuqin Deng,Song Lu,Jianmin Zhang,Fengbin Xia,Yongshen Huang,Yong Yang,Junjie Hu,Hua Feng,Tin Lun Lam,Yingying Chen,Jia Chen

doi:10.1109/access.2020.3015529

Abstract

Most existing laser welding process monitoring (LWPM) technologies focus on detecting post-process defects. However, in sheet metal laser welding applications such as welding of electronic consumer products during mass production, in-process defect detection is more important. In this article, a compact LWPM system using multi-sensor data fusion to detect in-process defects has been built. This system can collect the time series of plasma intensity, light intensity and temperature data for feature analysis. To verify the system's effectiveness, a plasma-light-temperature dataset has been compiled, which consists of 5,836 samples of nine classes, including one positive class and eight negative classes of typical in-process defects. A multi-sensor data fusion network based on a convolution neural network for in-process defect detection, called IDDNet, has also been proposed. Experimental results have demonstrated that IDDNet can achieve better multi-classification results than the support vector machine, with an overall accuracy of 97.57%. In particular, considering this monitoring process as a binary classification problem, IDDNet can achieve a 99.42% accuracy. Moreover, IDDNet can reach an average speed of 0.79ms per sample on a single GTX 1080ti graphics card, which meets the real-time requirement for industrial production. The proposed LWPM system has been successfully verified in real applications of sheet metal laser welding.

Highlights

Laser welding is widely used in electronic consumer products, automobiles and other industries due to the advantages of contactless processing, high precision and high speed [1]-[3]
The quality of laser welding is affected by in-process defects such as poor process parameters, surface impurities of materials, operation errors [4]
Experimental results have demonstrated that IDDNet achieved better multi-classification results than support vector machine (SVM), with an overall accuracy of 97.57%

Summary

Introduction

Laser welding is widely used in electronic consumer products, automobiles and other industries due to the advantages of contactless processing, high precision and high speed [1]-[3]. The quality of laser welding is affected by in-process defects such as poor process parameters, surface impurities of materials, operation errors [4]. In-process defects may cause post-process defects such as pores and lack of fusion. Defocusing distance is one of the main process parameters during laser welding. Incorrect defocusing distance often leads to pores and lack of fusion [5]. These defects could potentially affect the safety performance of the welded components [6]

Methods

Results

Conclusion