Weld location extraction in radiographic images using fuzzy rules generating method

Time-frequency diagram is applied to estimating the stability of gas metal arc welding process. A methodology has been developed to obtain indexes based on image processing of the spectrogram in order to evaluate the relationship between the spectrogram and the stability of process. The acoustic emission (AE) signals have been processed and characterised to obtain the spectrogram of the acoustic signals generated by arc. Statistical analysis of image generated by spectrogram and temporal parameters of AE signals has been used as a tool to obtain a method for stability evaluation. As a main result of the research, it demonstrates the effectiveness of the application of image processing of the time-frequency diagram for evaluating the stability in the welding processes. The results demonstrate the validity of this method to characterise the stability using the image characterisation.

Section: Introductionmentioning

confidence: 99%

Time–frequency diagram applied to stability analysis in gas metal arc welding based on acoustic emission

Macías

Roca

Fals

et al. 2010

“…Fuzzy logic is used to extract weld location from radiographic images. 18 The design and implementation of a fuzzy logic based weld joint tracking control system for pulsed gas metal arc welding has been presented by Bingu ¨l et al 19 Zhao et al 17 have designed a double variable, self-adaptive fuzzy controller for controlling the shape of the weld. Naso et al 20 have presented a new approach to real time weld quality monitoring based on the combination of optical sensors with fuzzy logic based classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

Ghanty¹,

Vasudevan²,

Mukherjee³

et al. 2008

In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.

“…Recently, different types of artificial neural networks and fuzzy logic systems have been developed for controlling the welding process and monitoring of weld quality. 3,4,[9][10][11][12][13][14][15][16][17][18][19] Among the various sensors used for weld quality monitoring, arc sensors are the most reliable, simple and competitive. 20,21 Moreover, a large number of researchers [22][23][24][25][26][27][28][29][30] have proposed arc sensing techniques for monitoring and control of various aspects of welding processes.…”

Section: Introductionmentioning

confidence: 99%

Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process

Pal¹,

Samantaray²

2008

The monitoring of welding process is crucial for the development of a real time quality control system for the pulsed metal inert gas welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features. IntroductionPulsed metal inert gas welding (PMIGW) is one of the major joining processes in modern manufacturing industries to produce high strength or high quality welds. One of the difficult tasks during arc welding is to maintain the consistency while making part after part. This is due to the fact that same process parameter settings do not guarantee the same weld quality. 1,2 As a result, there is a need for different technologies to precisely predict the weld quality, 1-4 more importantly the weld strength and the mechanical properties, 5-8 under varying welding operating conditions. Because of the complex nature of the process, artificial intelligence based methods are well suited for this purpose. Recently, different types of artificial neural networks and fuzzy logic systems have been developed for controlling the welding process and monitoring of weld quality. 3,4,[9][10][11][12][13][14][15][16][17][18][19] Among the various sensors used for weld quality monitoring, arc sensors are the most reliable, simple and competitive. 20,21 Moreover, a large number of researchers 22-30 have proposed arc sensing techniques for monitoring and control of various aspects of welding processes. Time domain features 3,4,31 of arc signals are less informative about the process, and are affected by the noises and disturbances. Furthermore, time frequency analysis with FFT is unsuitable, because the arc signals are time varying. Recently, wavelet transform has been applied in weld quality monitoring systems, 32,33 because this could eliminate most of the disadvantages of the FFT analysis. Wavelet transform has a good resolution in frequency and time domains synchronously; but there is no available published literature on its use to monitor arc welding processes. In this work, the acquired current signal was processed using wavelet packet transform analysis. Sensitivity analysis was also performed to find the best wavelet packet features. The best wavelet packet features and the process parameters were used in an ANN model for predicting the strength of the welded joint. Experimental Specimen preparationIn the present research, two mild steel specimens with dimensions of 125610068 mm were use...