A VMD-BP Model to Predict Laser Welding Keyhole-Induced Pore Defect in Al Butt–Lap Joint

Author:

Wang Wei1,Dong Yang1,Liu Fuyun12,Yang Biao1,Han Xiaohui3,Wei Lianfeng4,Song Xiaoguo12,Tan Caiwang12

Affiliation:

1. State Key Laboratory of Precision Welding & Joining of Materials and Structures, Harbin Institute of Technology, Harbin 150001, China

2. Shandong Institute of Shipbuilding Technology, Weihai 264209, China

3. CRCC Qingdao Sifang Co., Ltd., Qingdao 266111, China

4. Nuclear Power Institute of China, State Key Laboratory of Advanced Nuclear Energy Technology, Chengdu 610213, China

Abstract

The detection of keyhole-induced pore positions is a critical procedure for assessing laser welding quality. Considering the detection error due to pore migration and noise interference, this research proposes a regional prediction model based on the time–frequency-domain features of the laser plume. The original plume signal was separated into several signal segments to construct the morphological sequences. To suppress the mode mixing caused by environmental noise, variational modal decomposition (VMD) was utilized to process the signals. The time–frequency features extracted from the decomposed signals were acquired as the input of a backpropagation (BP) neural network to predict the pore locations. To reduce the prediction error caused by pore migration, the effect of the length of the signal segments on the prediction accuracy was investigated. The results show that the optimal signal segment length was 0.4 mm, with an accuracy of 97.77%. The 0.2 mm signal segments failed to eliminate the negative effects of pore migration. The signal segments over 0.4 mm resulted in prediction errors of small and dense pores. This work provides more guidance for optimizing the feature extraction of welding signals to improve the accuracy of welding defect identification.

Funder

Natural Science Foundation for Excellent Young Scholars of Shandong Province

Taishan Scholars Foundation of Shandong Province

Publisher

MDPI AG

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