Monitoring the quality of welding based on welding current and ste analysis

Afidatusshimah, Mazlan; Daniyal, Hamdan; Mohamed, Amir Izzani; Ishak, M.; Hadi, A.

doi:10.1088/1757-899x/257/1/012043

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…This variation is assumed to be within ±20% of their average values. It should be noted, however, that this uncertainty could be larger than what is assumed in this work [21].…”

Section: Voltage and Currentcontrasting

confidence: 54%

A probabilistic model of weld penetration depth based on process parameters

Mansour

Zhu

Edgren

et al. 2019

Int J Adv Manuf Technol

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In welded structures using robotized metal active gas (MAG) welding, unwanted variation in penetration depth is typically observed. This is due to uncertainties in the process parameters which cannot be fully controlled. In this work, an analytical probabilistic model is developed to predict the probability of satisfying a target penetration, in the presence of these uncertainties. The proposed probabilistic model incorporates both aleatory process parameter uncertainties and epistemic measurement uncertainties. The latter is evaluated using a novel digital tool for weld penetration measurement. The applicability of the model is demonstrated on fillet welds based on an experimental investigation. The studied input process parameters are voltage, current, travel speed, and torch travel angle. The uncertainties in these parameters are modelled using adequate probability distributions and a statistical correlation based on the volt-ampere characteristic of the power source.Using the proposed probabilistic model, it is shown that a traditional deterministic approach in setting the input process parameters typically results in only a 50% probability of satisfying a target penetration level. It is also shown that, using the proposed expressions, process parameter set-ups satisfying a desired probability level can be simply identified. Furthermore, the contribution of the input uncertainties to the variation of weld penetration is quantified. This work paves the way to make effective use of the robotic welding, by targeting a specified probability of satisfying a desired weld penetration depth as well as predicting its variation.

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“…This variation is assumed to be within ±20% of their average values. It should be noted, however, that this uncertainty could be larger than what is assumed in this work [21].…”

Section: Voltage and Currentcontrasting

confidence: 54%

A probabilistic model of weld penetration depth based on process parameters

Mansour

Zhu

Edgren

et al. 2019

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Additionally, active vision systems are often complex solutions because an external excitation is demanded in their case. Sensitivity and applicability of the welding acoustic signal was also studied [ 28 ], and acoustic sensing has a growing interest of many research teams (ultrasound and acoustic bands) [ 29 , 30 , 31 ]. There were special features extracted from the acoustic pressure signals.…”

Section: Introductionmentioning

confidence: 99%

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

2021

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Welding is an important process in terms of manufacturing components for various types of machines and structures. One of the vital and still unsolved issues is determining the quality and properties welded joint in an online manner. In this paper, a technique for prediction of joint hardness based on the sequence of thermogram acquired during welding process is proposed. First, the correspondence between temperature, welding linear energy and hardness was revealed and confirmed using correlation analysis. Using a linear regression model, relations between temperature and hardness were described. According to obtained results in the joint area, prediction error was as low as 1.25%, while for HAZ it exceeded 15%. Future work on optimizing model and input data for HAZ hardness prediction are planned.

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“…In addition, a 100 KHz sample rate is a sufficient value for data acquisition using a DSO during SMAW. Mazlan et al [ 131 ] successfully combined a DSO and a Short Time Energy (STE) method to detect welding defects in GMAW. The acquired welding current using a DSO was applied as input data for the STE analysis method conducted in MATLAB/Simulink.…”

Section: Defect Detection By Monitoring Waam and Fusion Welding Proce...mentioning

confidence: 99%

A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes

et al. 2022

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In Wire and Arc Additive Manufacturing (WAAM) and fusion welding, various defects such as porosity, cracks, deformation and lack of fusion can occur during the fabrication process. These have a strong impact on the mechanical properties and can also lead to failure of the manufactured parts during service. These defects can be recognized using non-destructive testing (NDT) methods so that the examined workpiece is not harmed. This paper provides a comprehensive overview of various NDT techniques for WAAM and fusion welding, including laser-ultrasonic, acoustic emission with an airborne optical microphone, optical emission spectroscopy, laser-induced breakdown spectroscopy, laser opto-ultrasonic dual detection, thermography and also in-process defect detection via weld current monitoring with an oscilloscope. In addition, the novel research conducted, its operating principle and the equipment required to perform these techniques are presented. The minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies. The use of these techniques in WAAM and fusion welding applications makes it possible to detect defects and to take a step towards the production of high-quality final components.

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Monitoring the quality of welding based on welding current and ste analysis

Cited by 10 publications

References 6 publications

A probabilistic model of weld penetration depth based on process parameters

A probabilistic model of weld penetration depth based on process parameters

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes

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