Ultrasonic test of resistance spot welds based on wavelet package analysis

Liu, Jing; Xu, Guangchen; Gu, Xiaopeng; Zhou, Guanghao

doi:10.1016/j.ultras.2014.10.013

Cited by 32 publications

(8 citation statements)

References 10 publications

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“…Current ultrasonic nondestructive testing technologies in weld quality testing are mainly based on A-scan and C-scan. A-scan which uses a single ultrasonic probe can judge the welds quality according to detecting oscillogram [4]. C-scan, using a focused probe, can obtain the image of spot welds [5].…”

Section: Introductionmentioning

confidence: 99%

Research on the influence of spot welding quality with positioning errors of the probe based on ultrasonic A-scan

Liu¹,

Pan²,

Chen³

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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Resistance spot welding (RSW) is the leading process of automobile body welding, and its quality is related to the reliability and safety of the whole vehicle. The effectively and automatically ultrasonic nondestructive testing is the development direction which can judge the spot welding quality, and then the positioning error of probe has an important impact on the test results. From this, conventional ultrasonic A-scan is adopted. Firstly, this paper presents the nugget diameter mathematic model of two-layer plates with different thickness when the probe positioning error is present. Then, the ultrasonic nondestructive testing simulation models of the spot welds are established, and the influence of the probe positioning error on the calculation result of the nugget diameter is studied. Finally, this paper carries out ultrasonic nondestructive testing of spot welding of two-layer plates with different thickness when the probe has positioning error. The experimental results verify the correctness of the theoretical analysis and simulation.

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Section: Introductionmentioning

confidence: 99%

Research on the influence of spot welding quality with positioning errors of the probe based on ultrasonic A-scan

Liu¹,

Pan²,

Chen³

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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“…These changes depend on the grain size and the formation of the Martensite phase in the material. Liu et al have analyzed UT images of the two-sheet spot-welded joint of T304 stainless steel with similar thicknesses and Fourier series have been used to extract the nugget diameter of RSWs [21].…”

Section: Introductionmentioning

confidence: 99%

Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

Amiri

Farrahi

Kashyzadeh

et al. 2020

Journal of Manufacturing Processes

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Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions is unknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.

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“…In recent days, various Meta heuristic algorithm like PSO (Particle Swarm Optimization), GA (Genetic Algorithm), ANN (Artificial Neural Network) and BPNN (Back Propagation Neural Network) have also been applied to find desired process parameters. Currently robotic welding systems are becoming increasingly popular and tend to substitute more and more mechanized and automated welding [17][18][19][20][21][22][23][24][25]. Robotic welding equipment can be programmed for different weldments.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

Ravichandran¹,

Meenakshipriya²,

Parameshwaran³

et al. 2020

Preprint

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The superiority and profile of the weld obtained through Gas Metal Arc Welding (GMAW) are not only depends on the chemical configuration of the flux, but also on the choice of welding parameters. Since variety of process parameters influence the results, a proper empathetic of process performance and identification of suitable welding conditions (i.e. optimum setting of process parameters) are indeed essential to enhance quality. The present work highlights the application and comparison of single-response optimization using Response Surface Methodology (RSM) with Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). The experimental analysis is conducted by optimizing the input parameters like Current Rating (Amp), Feed Rate (m/min), Welding Speed (mm/sec) and Gas Flow (l/m). An attempt has been made in the present research work by taking AISI: 430 stainless steel specimens to compare and analyse the performance in terms of weld bead geometry (Bead Width (mm), Bead Height (mm) and Depth of Penetration (mm)), Hardness (VHN) and Tensile Strength (N/mm²) using IRB 1410 Industrial manipulator. The effect of process parameters on ferritic stainless steel of series 400 (AISI: 430) grade has been analysed using Response Surface Methodology (RSM) method. Further, Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) have been developed further to minimize the bead width, bead height and maximize the depth of penetration. While fairly similar results were achieved with the implementation of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) were computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the quality prediction with proposed optimization results are better for the GMAW process by IRB 1410 Industrial manipulator.

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Ultrasonic test of resistance spot welds based on wavelet package analysis

Cited by 32 publications

References 10 publications

Research on the influence of spot welding quality with positioning errors of the probe based on ultrasonic A-scan

Research on the influence of spot welding quality with positioning errors of the probe based on ultrasonic A-scan

Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

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