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The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion.

This paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects.

Thermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process.

In this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

Overall, selection maps about the extent of the eutectic growth projects the solidification velocities leading to given microstructures. This is because of limitations of most of the set of results when obtained for single thermal gradients within the experimental spectrum. In these cases, associations only with the solidification velocity could give the false impression that reaching a given velocity would be enough to reproduce a result. However, that velocity must necessarily be accompanied by a specific thermal gradient during transient solidification. Therefore, the purpose of this paper is to not only project velocity but also include the gradients acting for each velocity.

Compilation of solidification velocity, v, thermal gradient, G, and cooling rate, Ṫ, data for Sn-Cu and Sn-Bi solder alloys of interest is presented. These data are placed in the form of coupled growth zones according to the correlated microstructures in the literature. In addition, results generated in this work for Sn-(0.5, 0.7, 2.0, 2.8)% Cu and Sn-(34, 52, 58)% Bi alloys solidified under non-stationary conditions are added.

When analyzing the cooling rate (Ṫ = G.v) and velocity separately, in or around the eutectic composition, a consensus cannot be reached on the resulting microstructure. The (v vs. G) + cooling rate diagrams allow comprehensive analyzes of the combined v and G effects on the subsequent microstructure of the Sn-Cu and Sn-Bi alloys.

The present paper is devoted to the establishment of (v vs. G) + cooling rate diagrams. These plots may allow comprehensive analyses of the combined v and G effects on the subsequent microstructure of the Sn-Cu and Sn-Bi alloys. This microstructure-processing mapping approach is promising to predict phase competition and resulting microstructures in soldering of Sn-Cu and Sn-Bi alloys. These two classes of alloys are of interest to the soldering industry, whereas manipulation of their microstructures is considered of utmost importance for the metallurgical quality of the product.