A Novel Method of Using Vision System and Fuzzy Logic for Quality Estimation of Resistance Spot Welding

Alghannam, Essa; Lu, Hong; Ma, Mingtian; Cheng, Qian; González, Andrés; Zang, Yue; Li, Shuo

doi:10.3390/sym11080990

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…Moving on to the machine-vision-based approaches, a small robot paired with an optical camera given the position of a weld takes pictures of the weld's surface and extracts geometrical features [63]. Then, it uses them to determine eleven fuzzy input functions of a fuzzy inference system model.…”

Section: Inspectionmentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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Resistance spot welding is a process with high variability regarding the quality of the produced joints. This means that key performance indicators (KPIs) such as geometrical and mechanical features as well as failure modes can deviate from the initial design even if the same process parameters are used. The industry has developed quality assurance programs and quality control methods for tracking these KPIs; however, most of them are based on offline or/and destructive practices. On the other hand, state-of-the-art approaches have made online quality assessment feasible and proved its necessity if a 100% quality rate is required. However, limited attention has been given to “closing the loop” and providing feedback for preventing and correcting process anomalies that cause quality variations in real time. In this study, the main gaps between the state of practice and the state of the art are discussed in the context of quality assurance for resistance spot welding. Finally, the role and importance of digital twins by taking into consideration the entire welding ecosystem in quality assurance are discussed in order to form the prospects for the road ahead.

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Section: Inspectionmentioning

confidence: 99%

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Stavropoulos,

Sabatakakis

2024

Metals

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“…The research [18] estimates Resistance Spot Welding (RSW) quality visually and using fuzzy logic. Vision and fuzzy logic are combined in this breakthrough RSW quality assessment approach.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a fuzzy logic model for predicting the quality of micro friction stir spot welding (µFSSW) using particle swarm optimization

Arsyad,

Membala,

Widyianto

et al. 2024

EEJET

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Micro Friction Stir Spot Welding (µFSSW) is crucial in microelectronics and precision manufacturing. It requires a comprehensive understanding of the complex connections between various parameters to achieve the highest quality welds. This study aims to improve the prediction of µFSSW weld quality by incorporating advanced optimization techniques. Fuzzy Logic Optimization is used to model uncertainties, and Particle Swarm Optimization (PSO) is employed to fine-tune parameters for improved accuracy. The fuzzy logic system utilizes Gaussian functions as membership functions, organized with nine rule bases. The results clearly demonstrate that the fuzzy logic model greatly enhances accuracy when combined with Particle Swarm Optimization. The refined model improves precision for pin diameter, shoulder diameter, Thermo-Mechanically Affected Zone (TMAZ) area, and cross-tensile strength. The PSO-optimized model shows lower accuracy in predicting plunge depth and shear tensile strength. The ongoing decline in Root Mean Square Error (RMSE) values highlights the complexity of the results. The optimization significantly improves the model’s ability to predict specific weld quality metrics, as demonstrated by the pin diameter’s reduced RMSE value of 0.07. The collective results showcase an optimized Fuzzy Logic System (FLS) model adept at accurately predicting µFSSW weld quality, demonstrating adaptability across diverse conditions. The discernible increase in accuracy, reaching up to 76 % following the optimization of the fuzzy logic model with PSO, serves as a testament to the efficacy of the employed methodologies in advancing the precision and reliability of µFSSW weld quality predictions

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“…used image processing methods and training a fuzzy inference system to improve in the quality estimation of the weld nugget’s surface on the car underbody. 14 Chinnadurai et al. used adaptive neuro-fuzzy inference system (ANFIS) to investigate the weld strength of Ultrasonic Welding (USW) for PC/ABS blend.…”

Section: Introductionmentioning

confidence: 99%

A study on spot welding quality judgment based on hidden Markov model

Wang

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Aiming at the influencing factors of spot welding quality and the shortcomings of the method of judging the spot welding quality based on the monitoring waveform curves: it is impossible to deeply analyze the differences between the corresponding monitoring waveform curves and spot welding qualities. In this study, according to the monitoring waveform curves of the welding current, inter-electrode voltage, electrode pressure and other signals when the spot welder executes the process parameters, by establishing the HMM model to realize the judgment of spot welding quality. The proposed method was also applied to the experiments of spot welding quality judgment for verifying the algorithm effectiveness.

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A Novel Method of Using Vision System and Fuzzy Logic for Quality Estimation of Resistance Spot Welding

Cited by 16 publications

References 23 publications

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Quality Assurance in Resistance Spot Welding: State of Practice, State of the Art, and Prospects

Development of a fuzzy logic model for predicting the quality of micro friction stir spot welding (µFSSW) using particle swarm optimization

A study on spot welding quality judgment based on hidden Markov model

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