The Experts below are selected from a list of 1959 Experts worldwide ranked by ideXlab platform
M. Vasudevan - One of the best experts on this subject based on the ideXlab platform.
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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, 2020Co-Authors: P. Vasantharaja, M. VasudevanAbstract: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...
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Intelligent modeling for estimating weld Bead Width and depth of penetration from infra-red thermal images of the weld pool
Journal of Intelligent Manufacturing, 2015Co-Authors: N. Chandrasekhar, M. Vasudevan, A. K. Bhaduri, T. JayakumarAbstract: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.
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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, 2012Co-Authors: S Chokkalingham, N. Chandrasekhar, M. VasudevanAbstract: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.
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adaptive neuro fuzzy inference system anfis based models for predicting the weld Bead Width and depth of penetration from the infrared thermal image of the weld pool
Metallurgical and Materials Transactions B-process Metallurgy and Materials Processing Science, 2012Co-Authors: L. Subashini, M. VasudevanAbstract:Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld Bead Width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, Width of the hot spot, thermal area determined from the Gaussian fit, and thermal Bead Width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld Bead Width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld Bead Width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld Bead Width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.
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Adaptive neuro-fuzzy inference system (ANFIS)-based models for predicting the weld Bead Width and depth of penetration from the infrared thermal image of the weld pool
Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science, 2012Co-Authors: L. Subashini, M. VasudevanAbstract:Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld Bead Width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, Width of the hot spot, thermal area determined from the Gaussian fit, and thermal Bead Width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld Bead Width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld Bead Width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld Bead Width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models. © The Minerals, Metals & Materials Society and ASM International 2011.
Yuming Zhang - One of the best experts on this subject based on the ideXlab platform.
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Machine learning of weld joint penetration from weld pool surface using support vector regression
Journal of Manufacturing Processes, 2019Co-Authors: Rong Liang, Rui Yu, Yuming ZhangAbstract: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.
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model based predictive control of weld penetration in gas tungsten arc welding
IEEE Transactions on Control Systems and Technology, 2014Co-Authors: Yuming ZhangAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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Intelligent modeling for estimating weld Bead Width and depth of penetration from infra-red thermal images of the weld pool
Journal of Intelligent Manufacturing, 2015Co-Authors: N. Chandrasekhar, M. Vasudevan, A. K. Bhaduri, T. JayakumarAbstract: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.
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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, 2015Co-Authors: N. Chandrasekhar, M VasudevanAbstract: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.
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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, 2012Co-Authors: S Chokkalingham, N. Chandrasekhar, M. VasudevanAbstract: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.
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Intelligent Modeling for Optimization of A-TIG Welding Process
Materials and Manufacturing Processes, 2010Co-Authors: N. Chandrasekhar, M. VasudevanAbstract:An intelligent model combining artificial neural network (ANN) and genetic algorithm (GA) has been developed for determining the optimum process parameters for achieving the desired depth of penetration and weld Bead Width during Activated Flux Tungsten Inert Gas Welding (A-TIG) welding of type 316LN and 304LN stainless steels. First, ANN models correlating process parameters with depth of penetration and weld Bead Width have been developed. There was good correlation between the measured and models predicted depth of penetration as well as the weld Bead Width for both training and test data. A GA code was developed in MATLAB in which the objective function was evaluated using the ANN models. The optimized values for GA parameters such as crossover rate, population size, and mutation probability were identified. The developed GA model produced multiple outputs such as current, torch speed, voltage, and arc gap for the same target depth of penetration and Bead Width, and validation was carried out by exper...
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artificial neural network modeling for estimating the depth of penetration and weld Bead Width from the infra red thermal image of the weld pool during a tig welding
Simulated Evolution and Learning, 2010Co-Authors: S Chokkalingham, N. Chandrasekhar, M. VasudevanAbstract:It is necessary to estimate the weld Bead Width and depth of penetration using suitable sensors during welding to monitor weld quality. 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 Bead Width and depth of penetration from the infra red thermal image of the weld pool using artificial neural network models. Real time infra red images were captured using IR camera during A-TIG welding. The image features such as length and Width of the hot spot, peak temperature and other features are extracted using image processing techniques. These parameters along with their respective current values are used as inputs while the measured Bead Width and depth of penetration are used as output of the neural network models. Accurate ANN models predicting weld Bead Width and depth of penetration have been developed.
Yongzhe Li - One of the best experts on this subject based on the ideXlab platform.
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Online Control of Deposited Geometry of Multi-layer Multi-Bead Structure for Wire and Arc Additive Manufacturing
Transactions on Intelligent Welding Manufacturing, 2018Co-Authors: Qinglin Han, Yongzhe Li, Guangjun ZhangAbstract:A robotic wire and arc additive manufacturing (WAAM) system with active vision sensing capability was implemented to improve the geometry accuracy of multi-layer multi-Bead structures. Width and height of the deposited Bead were online detected and the accuracies of the designed sensing approaches are 0.25 mm and 0.1 mm, respectively. A double-input-double-output controller with two subsystems was designed to ensure the uniformity of Bead Width and Bead height. The Bead Width was adjusted by a single neuron self-learning PI controller, while the Bead height was controlled based on a rule-based engine. The experimental results indicated that (i) accuracy of the deposited Bead Width is controlled in 0.5 mm, (ii) accumulation effect of the Bead height deviations has disappeared when multiple layers are overlapped, and (iii) the surface finish of each layer was controlled to be flat as well.
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vision sensing and Bead Width control of a single Bead multi layer part material and energy savings in gmaw based rapid manufacturing
Journal of Cleaner Production, 2013Co-Authors: Jun Xiong, Guangjun Zhang, Yongzhe LiAbstract:Rapid manufacturing technologies have made it possible to reduce material wastes and to remanufacture valuable dies and tools. This paper focuses on reasonable utilization of materials and energies in gas metal arc welding (GMAW) for rapid manufacturing. During the weld-based additive manufacturing process, geometries of the deposited weld Beads should be monitored and controlled. Using a composite filtering technique, a computer vision-sensing system was designed. Features of the weld Bead image were analyzed. A corresponding image processing technology was used to extract parameters of the deposited weld Beads. An on-line control of the deposited Beads was realized based on a segmented neuron self-learning controller. The results show that the proposed control system is capable of keeping the deposited Bead Width of a thin-walled part consistent, making an efficient use of materials and energies possible.
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Vision-sensing and Bead Width control of a single-Bead multi-layer part: Material and energy savings in GMAW-based rapid manufacturing
Journal of Cleaner Production, 2013Co-Authors: Jun Xiong, Zhilong Qiu, Guangjun Zhang, Yongzhe LiAbstract:Rapid manufacturing technologies have made it possible to reduce material wastes and to remanufacture valuable dies and tools. This paper focuses on reasonable utilization of materials and energies in gas metal arc welding (GMAW) for rapid manufacturing. During the weld-based additive manufacturing process, geometries of the deposited weld Beads should be monitored and controlled. Using a composite filtering technique, a computer vision-sensing system was designed. Features of the weld Bead image were analyzed. A corresponding image processing technology was used to extract parameters of the deposited weld Beads. An on-line control of the deposited Beads was realized based on a segmented neuron self-learning controller. The results show that the proposed control system is capable of keeping the deposited Bead Width of a thin-walled part consistent, making an efficient use of materials and energies possible. © 2012 Elsevier Ltd. All rights reserved.
Jun Xiong - One of the best experts on this subject based on the ideXlab platform.
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vision sensing and Bead Width control of a single Bead multi layer part material and energy savings in gmaw based rapid manufacturing
Journal of Cleaner Production, 2013Co-Authors: Jun Xiong, Guangjun Zhang, Yongzhe LiAbstract:Rapid manufacturing technologies have made it possible to reduce material wastes and to remanufacture valuable dies and tools. This paper focuses on reasonable utilization of materials and energies in gas metal arc welding (GMAW) for rapid manufacturing. During the weld-based additive manufacturing process, geometries of the deposited weld Beads should be monitored and controlled. Using a composite filtering technique, a computer vision-sensing system was designed. Features of the weld Bead image were analyzed. A corresponding image processing technology was used to extract parameters of the deposited weld Beads. An on-line control of the deposited Beads was realized based on a segmented neuron self-learning controller. The results show that the proposed control system is capable of keeping the deposited Bead Width of a thin-walled part consistent, making an efficient use of materials and energies possible.
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Vision-sensing and Bead Width control of a single-Bead multi-layer part: Material and energy savings in GMAW-based rapid manufacturing
Journal of Cleaner Production, 2013Co-Authors: Jun Xiong, Zhilong Qiu, Guangjun Zhang, Yongzhe LiAbstract:Rapid manufacturing technologies have made it possible to reduce material wastes and to remanufacture valuable dies and tools. This paper focuses on reasonable utilization of materials and energies in gas metal arc welding (GMAW) for rapid manufacturing. During the weld-based additive manufacturing process, geometries of the deposited weld Beads should be monitored and controlled. Using a composite filtering technique, a computer vision-sensing system was designed. Features of the weld Bead image were analyzed. A corresponding image processing technology was used to extract parameters of the deposited weld Beads. An on-line control of the deposited Beads was realized based on a segmented neuron self-learning controller. The results show that the proposed control system is capable of keeping the deposited Bead Width of a thin-walled part consistent, making an efficient use of materials and energies possible. © 2012 Elsevier Ltd. All rights reserved.
