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Zhao D.,Huazhong University of Science and Technology | Wang Y.,Huazhong University of Science and Technology | Wang Y.,Hubei Key Laboratory for Engineering Structural Analysis and Safety Assessment | Sheng S.,Grand Master Trading Ltd | Lin Z.,Grand Master Trading Ltd
Measurement: Journal of the International Measurement Confederation | Year: 2013

Titanium and its alloys have been identified as one of the best engineering metals for application in industrial fields. Whereas, there is limited research work on monitoring and controlling the small scale resistance spot welding (SSRSW) of titanium alloy. This paper performed a systematic research on the voltage curve, which turned out to be an indication for weld quality of SSRSW. It was obtained through clipping two leads onto the electrodes during SSRSW process. As the common equipment in SSRSW, the high frequency (HF) power supply and constant current mode were employed in this study. First voltage curves at different welding parameters were analyzed and then a probabilistic neural network (PNN) model using three factors extracted from the voltage curve was employed in order to classify the weld quality, and satisfied experiment results were acquired. It was demonstrated that the dynamic voltage during a welding process could be identified as a good signature for weld quality monitoring purpose. © 2013 Elsevier Ltd.All rights reserved. Source


Zhao D.,Huazhong University of Science and Technology | Wang Y.,Huazhong University of Science and Technology | Lin Z.,Grand Master Trading Ltd | Sheng S.,Grand Master Trading Ltd
ISIJ International | Year: 2013

With the rapid development of microelectromechanical systems, small scale resistance spot welding (SSRSW is ever-increasing used in electronic and medical devices. Whereas there is limited research work dealing with quality control of SSRSW. This paper investigated a real-time and in-situ SSRSW quality monitoring method by means of taking the voltage as the monitoring signature. It was obtained through clipping two leads onto the electrodes during SSRSW. As the linear DC and the high frequency (HF resistance welding power supplies were the common equipments in SSRSW and constant current mode was used in this study, the variation of voltage with time indicated the conditions of the welding process which issued in the final weld quality. Utilizing four factors extracted from the voltage curve an artificial intelligence algorithm to estimate the weld quality was proposed. The maximum average forecast error of the trained network is about 0.15 mm, showing that the voltage curve is a reliable quality monitoring signature of SSRSW. The most prominent advantage of this method is that weld quality can be perfectly estimated with only two sensor clips compared with other methods reported for normal scale or large scale resistance spot welding (LSRSW). © 2013 ISIJ. Source


Zhao D.,Huazhong University of Science and Technology | Wang Y.,Huazhong University of Science and Technology | Lin Z.,Grand Master Trading Ltd | Sheng S.,Grand Master Trading Ltd
NDT and E International | Year: 2013

With the rapid development of microelectromechanical system technology, small scale resistance spot welding (SSRSW) is ever-increasingly used in electronic and medical devices. Whereas there is limited research work dealing with quality control of SSRSW. This paper treated the U-I (voltage-current) curve as the monitoring signature to explore a real-time and in-situ SSRSW quality monitoring method. First a systematic research on the U-I curve was performed and then five factors extracted from the U-I curve to estimate the weld quality through an artificial intelligence algorithm was proposed. The entire predictions match with their actual results well, showing that the U-I curve is a reliable quality monitoring signature for SSRSW. It can assess the weld strength and nugget diameter in small error on condition that the resistance welding power supply used during the whole welding process can provide linear direct current (DC) or high frequency (HF) current, which is commonly employed in SSRSW. © 2013 Elsevier Ltd. All rights reserved. Source


Zhao D.,Huazhong University of Science and Technology | Wang Y.,Huazhong University of Science and Technology | Sheng S.,Grand Master Trading Ltd | Lin Z.,Grand Master Trading Ltd
Journal of Intelligent Manufacturing | Year: 2014

This paper investigates the effects of welding parameters on the welding quality and optimizes them in the small scale resistance spot welding (SSRSW) process. Experiments are carried out on the basis of response surface methodology technique with different levels of welding parameters of spot welded titanium alloy sheets. Multiple quality characteristics, namely signal-to-noise (S/N) ratios of weld nugget diameter, penetration rate, tensile shear load and the failure energy, are converted into an independent quality index using principal component analysis. The mathematical model correlating process parameters and their interactions with the welding quality is established and discussed. And then this model is used to select the optimum process parameters to obtain the desired welding quality. The verification test results demonstrate that the method presented in this paper to optimize the welding parameters and enhance the welding performance is effective and feasible in the SSRSW process. © 2013, Springer Science+Business Media New York. Source

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