Quality evaluation in resistance spot welding by analysing the weld fingerprint on metal bands by computer vision

Ruisz, Johannes; Biber, Jürgen; Loipetsberger, Mario

doi:10.1007/s00170-006-0522-6

Cited by 44 publications

(14 citation statements)

References 5 publications

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“…For example, the compactness measure was used in a comparable application to find an intensity threshold in images showing circular spot welds by Ruisz et al (2007).…”

Section: Coin Detectionmentioning

confidence: 99%

Automatic Coin Classification and Identification

Huber-Mörk¹,

Nölle²,

Rubik³

et al. 2012

Advances in Object Recognition Systems

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“…For example, the compactness measure was used in a comparable application to find an intensity threshold in images showing circular spot welds by Ruisz et al (2007).…”

Section: Coin Detectionmentioning

confidence: 99%

Automatic Coin Classification and Identification

Huber-Mörk¹,

Nölle²,

Rubik³

et al. 2012

Advances in Object Recognition Systems

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“…There has been extensive R&D over the years to investigate non-destructive methods to evaluate RSW quality. Various technologies were studied such as monitoring dynamic electrical signals, force, electrode displacement (indentation depth) during welding [1][2][3], ultrasonic inspection [4][5], computer visualization of electrode imprints [6]. However, none of these technologies has been effectively used in automotive production lines due to certain limitations.…”

Section: Introductionmentioning

confidence: 99%

IR-based spot weld NDT in automotive applications

Chen

Feng

2015

SPIE Proceedings

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Today's auto industry primarily relies on destructive teardown evaluation to ensure the quality of the resistance spot welds (RSWs) due to their criticality in crash resistance and performance of vehicles. The destructive teardown evaluation is labor intensive and costly. The very nature of the destructive test means only a few selected welds will be sampled for quality. Most of the welds in a car are never checked. There are significant costs and risks associated with reworking and scrapping the defective welded parts made between the teardown tests.IR thermography as a non-destructive testing (NDT) tool has its distinct advantage -its non-intrusive and non-contact nature. This makes the IR based NDT especially attractive for the highly automated assembly lines. IR for weld quality inspection has been explored in the past, mostly limited to the offline post-processing manner in a laboratory environment. No online real-time RSW inspection using IR thermography has been reported. Typically for postprocessing inspection, a short-pulse heating via xenon flash lamp light (in a few milliseconds) is applied to the surface of a spot weld. However, applications in the auto industry have been unsuccessful, largely due to a critical drawback that cannot be implemented in the high-volume production line -the prerequisite of painting the weld surface to eliminate surface reflection and other environmental interference. This is due to the low signal-to-noise ratio resulting from the low/unknown surface emissivity and the very small temperature changes (typically on the order of 0.1°C) induced by the flash lamp method.An integrated approach consisting of innovations in both data analysis algorithms and hardware apparatus that effectively solved the key technical barriers for IR NDT. The system can be used for both real-time (during welding) and post-processing inspections (after welds have been made). First, we developed a special IR thermal image processing method that utilizes the relative IR intensity change, so that the influence of surface reflection and environment interference can be reduced. Second, for the post-processing inspection, a special induction heater is used to replace the flash lamp, resulting in temperature changes on the order of 10°C. As a result, the signal-to-noise ratio increased by several orders of magnitudes with no surface painting needed, and the inspection results are more accurate and reliable. For real-time inspection, the heat from welding (with temperature exceeding 1000°C) was utilized. Third, "thermal signatures" were identified to uniquely correlate to different weld quality attributes through computational modeling of heat transfer and extensive testing of specially designed ranges of welding conditions. Novel IR image analysis algorithms that automatically and intelligently identify the "thermal signatures" from the IR images and positively determine the weld quality in less than a second were developed.

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“…Traditionally, destructive test methods can only ensure the qualities of the specific weld specimens, and they are time-consuming and costly. Therefore, nondestructive quality evaluation methods based on the feature analysis of the dynamic signals during RSW process attract special attentions [1][2][3][4]. The dynamic resistance and electrode displacement are the most commonly used parameters or variables for weld quality estimation [5,6].…”

Section: Introductionmentioning

confidence: 99%

A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

Zhang

Hou²,

Zhang

et al. 2014

Int J Adv Manuf Technol

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To develop an effective nondestructive evaluation method for the welding quality of the resistance spot welding, the electrode displacement signal during the resistance spot welding process is monitored, and the acquisition data of the signal are innovatively presented as the radar chart format. Some geometric features of the radar charts are extracted to reflect the welding quality. The decision tree classification technique is adopted to build a classifier and to provide a visible and intuitive diagnostic procedure for welding quality assessment. Test results of the decision tree classifier show that it is feasible and reliable to evaluate weld quality based on the graphics features of the radar chart. The features and the weld quality are closely related, and their extraction avoids complex algorithm. Meanwhile, when there are small samples, the decision tree classifier can identify good or bad weld accurately and rapidly, even though the weld is from an abnormal welding process, such as expulsion, current shunting, greasy surface, and small edge distance condition.

show abstract

Quality evaluation in resistance spot welding by analysing the weld fingerprint on metal bands by computer vision

Cited by 44 publications

References 5 publications

Automatic Coin Classification and Identification

Automatic Coin Classification and Identification

IR-based spot weld NDT in automotive applications

A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

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