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Bead Width

The Experts below are selected from a list of 1959 Experts worldwide ranked by ideXlab platform

M. Vasudevan – 1st expert on this subject based on the ideXlab platform

  • Optimization of A-TIG welding process parameters for RAFM steel using response surface methodology:
    Proceedings of the Institution of Mechanical Engineers Part L: Journal of Materials: Design and Applications, 2020
    Co-Authors: P. Vasantharaja, M. Vasudevan

    Abstract:

    In the present work, the optimization of Activated TIG (A-TIG) welding process parameters to achieve the desired weld Bead shape parameters such as depth of penetration, Bead Width, and heat-affect…

  • Intelligent modeling for estimating weld Bead Width and depth of penetration from infra-red thermal images of the weld pool
    Journal of Intelligent Manufacturing, 2015
    Co-Authors: N. Chandrasekhar, M. Vasudevan, A. K. Bhaduri, T. Jayakumar

    Abstract:

    In order to develop remote welding process methodologies, it is first important to develop computational methodologies employing soft computing techniques for predicting weld Bead Width and depth of penetration using a real time vision sensor during welding. Welding being a thermal processing method, sensing using infra-red (IR) camera is most extensively employed for monitoring and control of welding process. In the present work, attempt has been made to develop predictive methodologies using hybrid soft computing techniques for accurately estimating the weld Bead Width and depth of penetration from the IR thermal image of the weld pool. IR thermal images have been recorded in real time during A-TIG welding of 6 mm thick type 316 LN stainless steel weld joints with varying current values to produce different depth of penetration. From the acquired IR images, hot spot was identified by image segmentation using the cellular automata image processing algorithm for the first time. The current and the four extracted features from the hot spot of the IR thermal images were used as inputs while the measured Bead Width and depth of penetration were chosen as the output of the respective adaptive neuro fuzzy inference system and artificial neural network based models. Independent models were developed for estimating weld Bead Width and depth of penetration respectively. There was good correlation between the measured and estimated values of Bead Width and depth of penetration using the developed models.

  • predicting the depth of penetration and weld Bead Width from the infra red thermal image of the weld pool using artificial neural network modeling
    Journal of Intelligent Manufacturing, 2012
    Co-Authors: S Chokkalingham, N. Chandrasekhar, M. Vasudevan

    Abstract:

    It is necessary to estimate the weld Bead Width and depth of penetration using suitable sensors during welding to monitor weld quality. Among the vision sensors, infra red sensing is the natural choice for monitoring welding processes as welding is inherently a thermal processing method. An attempt has been made to estimate the weld Bead Width and depth of penetration from the infra red thermal image of the weld pool using artificial neural network models during A-TIG welding of 3 mm thick type 316 LN stainless steel plates. Real time infra red images were captured using IR camera for the entire weld length during A-TIG welding at various current values. The image features such as length and Width of the hot spot, peak temperature, and other features using line scan analysis are extracted using image processing techniques corresponding to particular locations of the weld joint. These parameters along with their respective current values are used as inputs while the measured weld Bead Width and depth of penetration are used as output of the neural network models. Accurate ANN models predicting weld Bead Width (9-11-1) and depth of penetration (9-9-1) have been developed. The correlation coefficient values obtained were 0.98862 and 0.99184 between the measured and predicted values of weld Bead Width and depth of penetration respectively.

Yuming Zhang – 2nd expert on this subject based on the ideXlab platform

  • Machine learning of weld joint penetration from weld pool surface using support vector regression
    Journal of Manufacturing Processes, 2019
    Co-Authors: Rong Liang, Rui Yu, Yuming Zhang

    Abstract:

    Abstract Skilled human welders can control the weld joint penetration through observing the molten pool. This suggests that a model may be developed to predict the backside Bead Width, that quantitively measures the weld joint penetration, from the weld pool surface. However, the weld pool surface is specular and subject to the radiation of the arc such that its measurement is challenging. At the University of Kentucky, the weld pool surface is measured using an innovative a 3-D vision sensor that can overcome the challenges caused by the specular nature and arc radiation; and the measured surface is characterized by three parameters. Because of the lack of physics based model, neural networks would typically be used to approximate the unknown correction, which is nonlinear in general, between the backside Bead Width and the characteristics parameters. Unfortunately, neural networks require large amount of data to train for adequate model accuracy. While the weld pool surface can be measured using the innovative 3D sensor, the ground truth for the backside Bead Width needs to be measured off-line after the experiment and to this end the work-work needs to appropriately cleaned/processed. Large amount of training data needed may not be easily obtained. To improve the critical ability to accurately predict the backside Bead Width, models need to be established from relatively small amount of training data. To this end, the authors propose to use the support vector regression (SVR) method and hypothesize that a SVR model trained using the small amount of the training data available would perform better than that a multi-layer perceptron (MLP) artificial neural network model trained using the same data. Modeling results show that for the relatively small training data available, the optimized SVR model provides a more accurate prediction to the backside Bead Width. As such, the authors systematically advanced the ability to accurately predict the weld joint penetration. The use of the innovative 3D sensor to obtain the 3D weld pool surface and the proposed use of the support vector method to address the small data issue played crucial roles.

  • model based predictive control of weld penetration in gas tungsten arc welding
    IEEE Transactions on Control Systems and Technology, 2014
    Co-Authors: Yuming Zhang

    Abstract:

    Skilled welders can estimate and control the weld joint penetration, which is primarily measured by the backside Bead Width, based on weld pool observation. This suggests that an advanced control system could be developed to control the weld joint penetration by emulating the estimation and decisionmaking process of the human welder. In this paper an innovative 3-D vision sensing system is used to measure the characteristic parameters of the weld pool in real-time in gas tungsten arc welding. The measured characteristic parameters are used to estimate the backside Bead Width, using an adaptive neuro-fuzzy inference system (ANFIS) as an emulation of skilled welder. Dynamic experiments are conducted to establish the model that relates the backside Bead Width to the welding current and speed. The dynamic linear model is first constructed and the modeling result is analyzed. The linear model is then improved by incorporating a nonlinear operating point modeled by an ANFIS. Because the weld pool needs to gradually change, being controlled by a skilled welder, a model predictive control is used to follow a trajectory to reach the desired backside Bead Width and the control increment is penalized. Because the weld pool is not supposed to change in an extremely large range, the resultant model predictive control is actually linear and an analytical solution is derived. Welding experiments confirm that the developed control system is effective in achieving the desired weld joint penetration under various disturbances and initial conditions.

N. Chandrasekhar – 3rd expert on this subject based on the ideXlab platform

  • Intelligent modeling for estimating weld Bead Width and depth of penetration from infra-red thermal images of the weld pool
    Journal of Intelligent Manufacturing, 2015
    Co-Authors: N. Chandrasekhar, M. Vasudevan, A. K. Bhaduri, T. Jayakumar

    Abstract:

    In order to develop remote welding process methodologies, it is first important to develop computational methodologies employing soft computing techniques for predicting weld Bead Width and depth of penetration using a real time vision sensor during welding. Welding being a thermal processing method, sensing using infra-red (IR) camera is most extensively employed for monitoring and control of welding process. In the present work, attempt has been made to develop predictive methodologies using hybrid soft computing techniques for accurately estimating the weld Bead Width and depth of penetration from the IR thermal image of the weld pool. IR thermal images have been recorded in real time during A-TIG welding of 6 mm thick type 316 LN stainless steel weld joints with varying current values to produce different depth of penetration. From the acquired IR images, hot spot was identified by image segmentation using the cellular automata image processing algorithm for the first time. The current and the four extracted features from the hot spot of the IR thermal images were used as inputs while the measured Bead Width and depth of penetration were chosen as the output of the respective adaptive neuro fuzzy inference system and artificial neural network based models. Independent models were developed for estimating weld Bead Width and depth of penetration respectively. There was good correlation between the measured and estimated values of Bead Width and depth of penetration using the developed models.

  • ima ge segmentation using various algorithms for identifying hotspot in infrared thermal images of weld pool and for estimation of weld Bead Width
    Quantitative InfraRed Thermography, 2015
    Co-Authors: N. Chandrasekhar, M Vasudevan

    Abstract:

    It is necessary to develop intelligent feedback control system to monitor and control weld Bead geometry during TIG welding in real-time to facilitate remote repair welding in nuclear reactors. The experiments were carried out using IR camera on stainless steel plates and 30 samples were cut and Bead Width (BW) was measured. For identifying hot spot in the weld pool, Cellular Automata (CA) image segmentation algorithm is developed and BW was measured from the images. The performance is compared with K-Means and Fuzzy C-Means algorithms. The RMS error was found to be lowest for CA algorithm. Thus CA algorithm is proposed for IR thermal image processing. The TIG welding is widely employed for fabrication of structural components in the nuclear reactors. Most of the weld defects can be avoided during welding if correct weld Bead geometry is ensured during welding. In order to ensure desired weld Bead geometry, real time monitoring and control of weld Bead geometry is essential during welding. The approach of monitoring, control and maintaining quality of weld Bead is normally known as adaptive welding or intelligent welding. The intelligent welding as the name implies is that integrating welding machine, smart weld parameter sensing and control system using computational programming. In the part of developing intelligent welding system, sensing and control methodology process plays an important role. However, several sensing techniques have been adapted for monitoring and control of weld Bead geometry which include acoustic sensor, weld pool oscillation sensor, optical sensor, ultrasonic sensor and vision sensor. Kovacevic et.al [1] is acquired IR thermal images with high shutter speed, image features were extracted and analysed with complicated image processing for oxidised weld pool surface area. The proposed image processing is to detect the weld pool boundary with accuracy of less than 100 ms. Hence, investigator proved that, the above methodology can overcome the influences caused by various disturbances during welding for processing thermal images. Ghanty et.al [2] have been used IR camera as a vision sensor, captured thermal images and developed a fuzzy rule based system for predicting weld Bead geometry during TIG welding from the segmented thermal images. Vasudevan et.al [3,4] have been proposed a methodology for estimating weld BW and DOP from the IR thermal images of weld pool and the identification of various weld defects from the recorded IR images. Chen and Chin [5] have employed IR thermography for adaptive control of DOP and BW during Gas Metal Arc welding and found linear relationship between full Width half maximum and BW. One of the investigator has developed soft computing techniques using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System for estimating weld BW and DOP from the I R thermal images of the weld pool [6]. Bowen Duan et.al [7] developed an effective segmentation approach based on ce llular automata principle for blurry boundary, complicate structure and high speckle noise. The image features were ex tracted using energy function information of global image comparison and local image. The experiment results d emonstrated to handle blurry boundaries in insensitive conditions. S. Wolframs [8] studied the fundamental behavior of CA and its local rule The objective of the present work is for the comparison of performance of the various image segmentation algorithms such as KM, FCM with in-house developed CA algorithm for identifying hot spot region in the IR thermal image of the weld pool. From the segmented image, physical weld BW is computed and performance is compared. This technique will be to implemented by feed-back monitoring and control system in real-time during TIG welding of austenitic stainless steel. For this study, welding experiments were carried out to record IR thermal images of weld pool in real time at various TIG welding process parameters. The recorded IR thermal images were further processed using image processing algorithms for extracting features and for comparison of their performance by estimating weld BW.

  • predicting the depth of penetration and weld Bead Width from the infra red thermal image of the weld pool using artificial neural network modeling
    Journal of Intelligent Manufacturing, 2012
    Co-Authors: S Chokkalingham, N. Chandrasekhar, M. Vasudevan

    Abstract:

    It is necessary to estimate the weld Bead Width and depth of penetration using suitable sensors during welding to monitor weld quality. Among the vision sensors, infra red sensing is the natural choice for monitoring welding processes as welding is inherently a thermal processing method. An attempt has been made to estimate the weld Bead Width and depth of penetration from the infra red thermal image of the weld pool using artificial neural network models during A-TIG welding of 3 mm thick type 316 LN stainless steel plates. Real time infra red images were captured using IR camera for the entire weld length during A-TIG welding at various current values. The image features such as length and Width of the hot spot, peak temperature, and other features using line scan analysis are extracted using image processing techniques corresponding to particular locations of the weld joint. These parameters along with their respective current values are used as inputs while the measured weld Bead Width and depth of penetration are used as output of the neural network models. Accurate ANN models predicting weld Bead Width (9-11-1) and depth of penetration (9-9-1) have been developed. The correlation coefficient values obtained were 0.98862 and 0.99184 between the measured and predicted values of weld Bead Width and depth of penetration respectively.