Comprehensive process monitoring for laser welding process optimization

Stritt, Peter; Boley, Meiko; Heider, Andreas; Fetzer, Florian; Jarwitz, Michael; Weller, Daniel; Weber, Rudolf; Berger, Peter; Graf, Thomas

doi:10.1117/12.2212814

“…Internal defect detection is possible only to a limited extent during welding to date. Online X-ray observations can provide insights into the formation of process pores during laser beam welding [23], though their application in industrial production is limited due to the complex system technology required. Although many sensor technologies have been tested, the reliable detection of cracks and detection of pores is only possible to a very limited extent.…”

Section: Online Process Monitoring In Laser Beam Weldingmentioning

confidence: 99%

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

Schmoeller

¹

,

Stadter

²

,

Kick

³

et al. 2021

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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.

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“…Internal defect detection is possible only to a limited extent during welding to date. Online Xray observations can provide insights into the formation of process pores during laser beam welding [23], though their application in industrial production is limited due to the complex system technology required. Although many sensor technologies have been tested, the reliable detection of cracks and detection of pores is only possible to a very limited extent.…”

Section: Online Process Monitoring In Laser Beam Weldingmentioning

confidence: 99%

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

Schmoeller

¹

,

Stadter

²

,

Kick

³

et al. 2021

Preprint

3

0

View full text Add to dashboard Cite

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.

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“…Various kinds of sensor methods are applied in the field, such as the use of photodiode signals [1][2][3], spectroscopy [4,5], high-speed photography [6][7][8], acoustic signals [9,10], electrical signals [11], X-rays [12], and so on. Among these sensor methods, high-speed photography, which can observe the interaction region between the laser and the workpiece directly, is a kind of non-destructive inspection technique.…”

Section: Introductionmentioning

confidence: 99%

Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine

Wang

¹

,

Chen

²

,

Gao

³

et al. 2017

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Abstract:In order to improve the prediction ability of welding quality during high-power disk laser welding, a new approach was proposed and applied in the classification of the dynamic features of metal vapor plume. Six features were extracted through the color image processing method. Three features, including the area of plume, number of spatters, and horizontal coordinate of plume centroid, were selected based on the classification accuracy rates and Pearson product-moment correlation coefficients. A support vector machine model was adopted to classify the welding quality status into two categories, good or poor. The results demonstrated that the support vector machine model established according to the selected features had satisfactory prediction and generalization ability. The classification accuracy rate was higher than 90%, and the model could be applied in the prediction of welding quality during high-power disk laser welding.

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Comprehensive process monitoring for laser welding process optimization

Cited by 7 publications

References 9 publications

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

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

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

Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine

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