A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

Abstract

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.

Exploring the Potential Application of an Innovative Post-Weld Finishing Method in Butt-Welded Joints of Stainless Steels and Aluminum Alloys

“…This study critically evaluates various approaches, elucidating their respective advantages and limitations. In paper [30], a study on non-destructive testing is presented, utilizing micro-focused X-ray tomography to comprehensively evaluate welds, including internal defects such as cracks In the subsequent stage of the investigation, the samples underwent two distinct nondestructive testing (NDT) methods: Penetrant Testing (PT) and Radiographic Testing (RT) to identify welding defects. These techniques are commonly employed in the examination of welded joints.…”

“…This study critically evaluates various approaches, elucidating their respective advantages and limitations. In paper [30], a study on non-destructive testing is presented, utilizing micro-focused X-ray tomography to comprehensively evaluate welds, including internal defects such as cracks and pores. Furthermore, paper [31] employs X-ray CT to analyze the progression of damage in extrusion welds nearing failure.…”

Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control

“…To design the ML approach for determining the weld depth based on the OCT keyhole depth measurement data (reference 5.1), the generated training data were employed. The notch depicted, with a depth and length of 1 mm, was used to generate a reference for matching the process signals with the depth obtained from the microfocus computed tomography (µCT) scans [26], in addition to the trigger signal based on the laser emission switch-on time. As a sample material, the aluminum base alloy AA2219 was chosen.…”

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

“…Weld defect detection methods are various, but in the face of specific problems, it is still necessary to adapt measures to local conditions. For UHV equipment weld quality inspection, X-ray inspection is one of the common inspection methods, and inspectors usually judge the weld quality based on X-ray inspection images [20][21][22][23]. The automatic extraction of weld defects based on radiographic inspection images is of great significance for the next step to realize automatic evaluation of weld quality.…”