Dr. Jian Chen Profile

Dr. Jian Chen

at Oak Ridge National Lab

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Author

Publications (3)

Proceedings Article | 3 April 2018 Presentation

Online welding process monitoring using multi-optical sensing techniques (Conference Presentation)

Jian Chen, Zongyao Chen, Zhili Feng

Proceedings Volume 10599, 105990X (2018) https://doi.org/10.1117/12.2297282

KEYWORDS: Digital image correlation, Feedback control, Sensing systems, Digital cameras, Optical filters, Imaging systems, Visualization, Distortion, Speckle

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Proceedings Article | 8 May 2017 Paper

Online resistance spot weld NDE using infrared thermography

Jian Chen, Zhili Feng

Proceedings Volume 10169, 101690K (2017) https://doi.org/10.1117/12.2260896

KEYWORDS: Boron, Image processing, Thermography, Nondestructive evaluation, Infrared imaging, Inspection, Infrared cameras, Resistance, Infrared radiation, Cameras, Coating, Infrared signatures

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Proceedings Article | 12 May 2015 Paper

IR-based spot weld NDT in automotive applications

Jian Chen, Zhili Feng

Proceedings Volume 9485, 948513 (2015) https://doi.org/10.1117/12.2177124

KEYWORDS: Nondestructive evaluation, Inspection, Thermography, Infrared imaging, Infrared signatures, Databases, Infrared cameras, Resistance, Lamps, Image quality

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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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