Investigation of the influences of the process parameters on the weld depth in laser beam welding of AA6082 using machine learning methods

Schmoeller, Maximilian; Stadter, Christian; Wagner, Markus; Zaeh, Michael F.

doi:10.1016/j.procir.2020.09.121

“…The procedure and the potentials are illustrated in Figure 10 using the example of the holistic utilization of OCT keyhole depth measurements. With the knowledge about the weld properties at each point of a measurement signal, the signals of the sensor can be characterized precisely and used for the further development of welding process models [26]. In addition, ray-tracing approaches that correlate the keyhole geometry with measurement signals can be validated [27].…”

Section: Discussionmentioning

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

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0

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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.

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“…Due to the transient behavior of the feed rate 𝑣 𝑤 caused by the comparatively slow reaction of the drive axes, the laser power 𝑃 𝐿 was chosen as the main manipulated variable to control the weld depth. However, Schmoeller et al [31] and Hagenlocher et al [32] showed a correlation between higher feed rates and the process stability of deep penetration laser beam welding. The feed rate 𝑣 𝑤 was therefore used to ensure stable process conditions, resulting in high seam quality.…”

Section: Inline Weld Depth Controlmentioning

confidence: 99%

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

Schmoeller

¹

,

Weiss

²

,

Goetz

³

et al. 2022

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In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now.

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“…The fixed optics was positioned with an angle of incidence of 10 • in a piercing configuration. A focal diameter of 100 µm at a focal length of 200 mm was utilized for the experiments [24]. Three synchronous servo motors were used for the feed motions, which can send their respective position to the control in real-time.…”

Section: Materials and Experimental Set-upmentioning

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

Self Cite

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 in the scope of subsequent work.

show abstract

Investigation of the influences of the process parameters on the weld depth in laser beam welding of AA6082 using machine learning methods

Cited by 6 publications

References 10 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

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

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

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