Survey of infrared sensing techniques for welding process monitoring and control

Wikle, Howard C.; Chen, Fuhu; Nagarajan, S.; Chin, Bryan A.

doi:10.1080/02533839.1998.9670426

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The contribution of the atmosphere and the environment can be neglected. Then, the thermographic device measures a radiance Rad as a function of the body temperature T in the spectral band [l min , l max ] according to equation (3) and displays a digital level (DL) according to equation ( 4)…”

Section: Introductionmentioning

confidence: 99%

“…2 A survey of IR sensing techniques for welding applications was made by Wikle et al in 1998. 3 More recently, IR cameras were used in non-destructive testing in polyethylene weld joints, 4 steel weld joints 5 and steel resistance spot weld. 6 Infrared cameras for real time monitoring of temperature in welding processes were used in laser welding of polymer, 7, 8 laser and hybrid welding of aluminium 9 and magnesium, 10 laser brazing of steel/aluminium assembly, 11 friction stir welding of aluminium, 12 pad welding of Inconel and CrMo steel 13 and arc and laser welding of steel 14 or arc welding of magnesium alloy.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative infrared analysis of welding processes: temperature measurement during RSW and CMT-MIG welding

Frappier

Benoit

Paillard

et al. 2014

Science and Technology of Welding and Joining

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This article deals with temperature characterisation by infrared thermography during welding. To demonstrate the versatility of this technique, two welding processes were investigated with different materials: cold metal transfer metal inert gas welding of al alloy and resistance spot welding of galvanised steel. Two experimental methods were used to characterise temperature without requiring implementation of thermocouples. The validity of these methods is demonstrated by metallurgical characterisations. The error on the measured values of temperature is assessed regarding the initial uncertainty on the apparent surface emissivity. The results show that it is possible to obtain absolute temperature values, cooling rates and informative visualisation of temperature gradients with a reasonable uncertainty.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative infrared analysis of welding processes: temperature measurement during RSW and CMT-MIG welding

Frappier

Benoit

Paillard

et al. 2014

Science and Technology of Welding and Joining

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show abstract

“…A copper tube induction coil of 3.0 mm in outer diameter and 0.5 mm wall thickness was bent around the work piece. A specified shut-off temperature at a joint surface location was monitored during the brazing process by an infrared ray sensor (Wikle et al 1998), which broke a circuit to terminate the brazing process, when the specified temperature was reached.…”

Section: Methodsmentioning

confidence: 99%

Numerical simulation and optimization of WC-SS420 induction brazing process

Chou

Tsai

Tsai³

2012

Journal of the Chinese Institute of Engineers

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Tungsten carbide (WC) is a very hard, brittle, and relatively expensive cermet. It is often joined to more economical material, such as martensitic 420 stainless steel (SS420), to form a hybrid cutting tool stock. This article summarizes a study on the optimization of an induction brazing process joining these two materials using BAg-22 as filler metal. This study uses an integrated approach consisting of finite element analysis (FEA) modeling and experimental verifications. The thermal behaviors and the metallurgical transitions during the brazing cycles of WC-BAg22-SS420 joints are studied. This study investigated process and metallurgical issues due to mismatches in thermal expansion coefficient, thermal conductivity, and electromagnetic permeability. Using the optimum process parameters derived from simulation, joint strengths exceeding 370 AE 40 MPa for shear were obtained. The hardness of WC was unaffected by the brazing heat. However, softening in the SS420 next to the fusion interface was constantly observed in the experiments. The FEA modeling results demonstrated that using a controlled heating and cooling process schedule was effective in mitigating this softening phenomenon. Experimental verification of this mitigation method is currently under study.

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“…In recent years, Nagesh reported using ANN to predicted the weld bead geometry [10] and deep penetration [4]. In addition, it also uses infrared sensing techniques to calculate the weld bead geometry [10][11][12][13][14]. Lee and Um [15] used artificial neural network and fuzzy logic to predict the weld bead geometry, microstructure, defect and recognition.…”

Section: Introductionmentioning

confidence: 99%

Controlling the Width of Weld Bead from the Weld Pool Using Machine Vision and Artificial Neural Network

2019

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In this study, the development of artificial neural network systems was proposed to keep the width of weld bead constant by controlling the welding speed. During Gas Tungsten Arc Welding, the weld bead was observed directly using machine vision system that utilized CCD camera. Matlab software was used for image processing algorithm and training the data. In training the data, two methods were used which are training with normalization and without normalization. ANN input parameters were arc current, welding speed, number of pixel and location of weld bead. Double hidden layer was used where each one of them consists of 25 nodes, and the output parameter is new controlled welding speed. The testing data was performed using 100, 105 and 110 A with initial welding speed of 1.35, 1.40 and 1.45 mm/s. The measurement of weld bead was taken using two different methods, machine vision and manual measurement. The result showed that the width of weld bead on welding current of 105 A is close to the target of 7 mm with the average error of 0.49 mm. The best result for the machine vision and manual measurement can be achieved when the welding current is 110 A with a normalization.

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Survey of infrared sensing techniques for welding process monitoring and control

Cited by 7 publications

References 15 publications

Quantitative infrared analysis of welding processes: temperature measurement during RSW and CMT-MIG welding

Quantitative infrared analysis of welding processes: temperature measurement during RSW and CMT-MIG welding

Numerical simulation and optimization of WC-SS420 induction brazing process

Controlling the Width of Weld Bead from the Weld Pool Using Machine Vision and Artificial Neural Network

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