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Abstract
For a low cost, there are industrial infrared monitoring systems used for imperfection detection and identification in welded joints. The key drawback that impedes real life industrial applications is the low spatial resolution, as well as the temporal resolution of low-cost infrared (IR) cameras. This is also the case in tungsten inert gas (TIG) welding. Taking into consideration the influence of voltage on the arc energy and heat input, high frequency sampled voltage was used to evaluate the interpolated temporal resolution of IR sequences. Additionally, a reflected temperature correction method was proposed to reduce the uncertainty of absolute temperature measurement with a thermographic camera. The proposed method was applied to detect several imperfection types, such as lack of or incomplete penetration as well as incorrect weld shape and size (including burnouts). Results obtained for different interpolation factors were compared. The obtained results emphasize the validity of reflected temperature correction method. For the weld defects detection task, the smallest detectable defect was found for various interpolation factors. Moreover, the correspondence of arc voltage and the joint temperature was checked. Additionally, a set of decision rules was elaborated on and applied to distinguish between various joint conditions. It was found that defects that do not have symmetrical temperature distribution with respect to the joint axis are harder to identify.
Highlights
Welding technology over past years has been employed in a wide variety of industries such as automobile, shipbuilding, aerospace, oil and gas, and many others
Based on all the datasets gathered for the samples with mounted thermocouples, a greedy search was carried out to find the best combination of reflected temperature and emissivity
It can be seen that applying both the reflected temperature correction and the proper emissivity value leads to an enhanced image, in which more details and small temperature differences are visible
Summary
Welding technology over past years has been employed in a wide variety of industries such as automobile, shipbuilding, aerospace, oil and gas, and many others. Devices, as well as regarding quality assurance in welding, including simulation and nondestructive testing (NDT) methods, have become more imminent while the manufacturing industry develops rapidly [1,2,3,4,5,6,7,8]. Welding quality is affected by many factors, including but not limited to process parameters, welding speed, arc voltage, welding current, shielding gas, welding device quality, welder skill in manual welding, and workpieces preparation quality [9,10]. There is a vital need for early detection of defects and to control the welding process to ensure the welding quality. The pre-process involves seam tracking, clamping, gap, and part geometry [11], wherein primary methods are machine and ultrasonic vision
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