Bipul Das

Monitoring of friction stir welding process using weld image information

Monitoring friction stir welding process based on weld images is attempted in this research. Well-known fractal theory is applied to images of the welds and extracted features in terms of fractal dimension are correlated to ultimate tensile strength (UTS) of the joints. The correlation shows a decreasing trend that can be an indicator towards monitoring of weld quality. Apart from fractal theory, wavelet transform is also applied to the acquired images and an indicator is proposed relating the information gathered after the decomposition. Interestingly, the proposed indicator also describes a decreasing trend of UTS with the increase in its value. The proposed approaches can be effectively applied in real-time monitoring of the process with appreciable accuracy.

Machine vision system based monitoring approach for friction stir welding process

Development of intelligent systems for the monitoring of manufacturing processes is always in great demand. In this study, an approach based on image processing through 2D wavelet transform is proposed for monitoring of weld quality in friction stir welding process. Welding experiments are conducted over three ranges of two most influencing process parameters; tool rotational speed and welding speed. Top surface images of the welds are captured and converted to binary images for the analysis using wavelet transformation. The technique of peak signal to noise ratio is executed to find the best mother wavelet function out of 53 available functions for optimum decomposition. All the images are decomposed to second level and the coefficients from the approximations of the images are computed. In this work, a novel indicator based on double logarithmic values of average root mean square of the approximation coefficients of the image is introduced to study the behaviour of weld joint strength. It is observed that, with the increase in the proposed indicator, ultimate tensile strength of the joints is found to follow a decreasing trend. The proposed methodology can be implemented as a tool based on machine vision system for the monitoring of the process with less human intervention.

Monitoring of Weld Quality in Friction Stir Welding Based on Spindle Speed and Motor Current Signals

The process of friction stir welding passed more than two decades since its invention in the year 1991 in TWI, UK. It involves complex physics and has not been explored fully to understand its physical behavior. Due to the lack of precise mathematical modeling and too many influencing factors that govern the welding process, difficulty arises in direct monitoring of the process based on the process parameters only. Moreover, the influencing parameters are so correlated that the effect of one on the weld quality cannot be isolated from the others for effective monitoring of the process. Thus, a need is realized to develop different methods for the efficacious monitoring of the process with the acquired signals during welding for better control over the outcome of the process. In this study, effectiveness of spindle speed and main motor current signals is investigated for the development of tools which will lead to real-time weld quality prediction.