1. Introduction
Welding is a manufacturing process and the technology of joining metals by heating, high temperature or high pressure. In recent years, the online inspection of welding quality has become a research highlight in the field of welding [
1,
2,
3]. Realizing the online inspection of welding quality can find welding defects in a timely manner so as to be able to quickly adjust and optimize the welding process parameters and, finally, achieve the goals of maintaining the welding quality and improving the welding processing efficiency.
In the welding process, multisource information, such as the electric signal, vision, temperature, and spectrum, can reflect the welding quality to a large extent; therefore, the welding quality can be inspected by online sensing of the multisource information. Among them, the vision sensing method is widely used in welding quality inspection. Zhang et al. [
4] studied an improved fuzzy edge detection algorithm, which can extract the weld edge from a low-contrast welded joint image, providing strong support for the detection of welding defects. Deng et al. [
5] studied a weld edge extraction algorithm based on beamlet transform, which can accurately extract weld edges from high noise welding images with high efficiency. Shen et al. [
6] studied two weld defect detection methods based on visual sensing and subsequently combined the two methods using information fusion to improve the reliability of weld defect detection. Khumaidi et al. [
7] established a classification model for welding defects using a convolutional neural network and Gaussian kernel, with the acquired weld image as input; it could achieve the high-precision classification of four types welds: good weld, over spatter, polarity, and undercut. Haffner et al. [
8] proved that a convolutional neural network is a reliable and promising evaluation method when visual sensing is used to detect welding quality. The above documents realized welding quality detection by directly imaging the weld; in order to directly avoid the impact of arc light on welding quality detection, this paper measured the temperature field distribution of the welding heat-affected zone around the molten pool region through infrared band imaging and studied the relationship between the welding temperature field and welding quality.
The weld penetration is closely related to the welding forming quality, and many scholars have carried out in-depth research on the sensing technology of weld penetration [
9,
10,
11]. Sibilano et al. [
12] studied a spectrum-based laser welding penetration sensing technology, and online sensing of the welding penetration was realized through the real-time measurement of the electron temperature of the plasma. Yang et al. [
13] studied a penetration sensing technology for aluminum alloy welding based on the weld pool vision, and they built a penetration recognition model based on an artificial neural network. Xia et al. [
14] developed a penetration evaluation model for resistance spot welding on the basis of the electrode displacement signals, and they tested its generalization ability under different welding conditions. Ren et al. [
15] established a gas tungsten arc welding (GTAW) penetration classification model for aluminum alloy based on arc sound detection and deep learning; its classification accuracy was better than many typical models. Yu et al. [
16] aimed at the problem that a single molten pool image does not contain enough information to predict the welding forming quality, and they studied a welding penetration sensing technology based on a sequential weld pool image and deep learning. Liu et al. [
17] aimed at the problem that the instability of small holes will affect the penetration of laser welding, and they studied a pulse laser welding penetration sensing technology using vision sensing and deep learning. To sum up, a change in the welding penetration will inevitably lead to a change in the multisource information features during the welding process. When connecting a variable groove weldment, the change in the penetration will also be reflected by a change in the welding temperature field distribution features; therefore, using the temperature information in the welding process to carry out online sensing of the welding penetration is a feasible research direction.
In order to measure the temperature information during the welding process, infrared detection is a feasible means, which is an important testing method in the field of nondestructive testing [
18]. Tan et al. [
19] measured the welding temperature field distribution using an infrared thermometer, modified the parameters of a double elliptical distribution heat source model, and established a finite element numerical simulation model for welding temperature field. Alfaro et al. [
20] studied a GTAW defect detection technology based on infrared sensing, and they explored the relevance between the change of welding temperature and forming defects. Kafieh et al. [
21] processed the infrared image sequence of a welded polyethylene pipe, which showed good performance in welding defect detection. Zhu et al. [
22] developed a set of infrared visual sensing systems for detecting the weld offset of swing arc narrow gap welding. Guo et al. [
23] studied an ultrasonic infrared thermal imaging technology for crack defect detection of friction stir welded joints of aluminum alloy sheets. Górka et al. [
24] studied a reflection temperature correction technology to reduce the uncertainty of the absolute temperature measurement using an infrared sensor, which was used to diagnose several typical welding defects, including nonpenetration and unsatisfactory weld formation size. To sum up, infrared detection has become an important means for welding process monitoring; after infrared images are collected by an infrared sensor, they are processed using image processing algorithms so as to extract the features strongly related to welding forming quality. By identifying these features, welding quality monitoring can be realized to a certain extent.
In order to accurately establish the relationship between the signal features and welding forming quality, the outstanding performance of artificial neural networks has attracted great attention by welding workers. An artificial neural network is an important branch of machine learning, and from the relevant research results in the welding field, artificial neural networks are widely extended to welding process parameter design, welding performance prediction, and welding forming quality monitoring and control [
25,
26,
27,
28]. Luo et al. [
29] determined the sound signal features that were strongly related to the welding quality and established a laser welding defect identification model using an artificial neural network. Hong et al. [
30] studied a prediction technology for the weld morphology of laser arc hybrid welding using an artificial neural network. Lei et al. [
31] studied a prediction technology for the geometric features of a laser welding seam using multisource information fusion, with the extracted weld pool morphological features and welding process parameters as inputs, and built a weld geometry prediction model using an artificial neural network. Li et al. [
32] used a passive visual sensing system to acquire weld pool images, and they developed a prediction model for GTAW penetration using a convolution neural network. Bacioiu et al. [
33] studied a tungsten inert gas (TIG) weld defect detection technology by means of high dynamic range imaging, and they established a welding defect identification model based on a convolutional neural network. Hartl et al. [
34] studied friction stir welding process monitoring technology, and they used various types of artificial neural networks to detect the quality of friction stir welds. To sum up, the application of artificial neural networks in the welding field has resulted in many achievements, but there is no mature algorithm for determining the parameters of a neural network at present. Improving the generalization ability of artificial neural networks is also the focus of future research.
In this paper, welding penetration sensing technology for variable groove weldments was studied, which innovatively uses the temperature field distribution of the welding heat-affected zone around the weld pool to sense the weld penetration. The temperature field distribution of the weldment was measured by means of infrared thermal imaging, the feature extraction algorithm of the temperature distribution perpendicular and parallel to the welding direction on the weldment surface was studied, the key information such as the welding thermal cycle parameters was extracted, and the feasibility of using the extracted linear temperature distribution features to identify the welding penetration of variable groove weldments was analyzed. Finally, a back propagation (BP) neural network was adopted to establish a welding penetration diagnostic model for variable groove weldments. The welding penetration sensing method proposed in this paper does not need to observe the dynamic behavior of the molten pool but only needs to detect the temperature field distribution of the welding heat-affected zone around the weld pool, and it can achieve high-precision sensing of the welding penetration for variable groove weldments.