• Journal of Welding and Joining
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• v.33 no.3
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• pp.40-46
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• 2015

Welding is one of the most fundamental and necessary work in the industry that demand sophistication of skilled workers. This study is to introduce welding simulator as a training tool, to verify its effectiveness and to measure satisfaction of the trainees. A group of freshman students at a Korea Polytechnics College in their twenties with less experience of welding participated in the study. They were divided into two groups and took a traditional training course (comparison group) and a training course with welding simulator applied reality/haptic technology (experimental group) for same hours respectively. To evaluate training effect, a national certificate test and a survey based on Phillips' ROI (Return on Investment) methodology were conducted by the students and the college respectively. And satisfaction survey among the students based on Kirkpatrick's Four-Level Evaluation Model was also carried out. The results showed that all students in the experimental group passed the national certificate test and the ROI of the experimental group for five years were 110% higher than the comparison group. Furthermore, 25% more students in the experimental group replied "very satisfied" about the overall training course and 75% more students in the same group found that the simulation was very similar to the real welding.

• Journal of Welding and Joining
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• v.25 no.6
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• pp.84-95
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• 2007

The International System for Education and Qualification of Welding Personnel has been implemented based on the harmonized European System for education and qualification of welding personnel. This paper gives an overview of the International System focusing on the training guidelines and the quality assurance system developed. Systems for harmonization of Certification of Welding Personnel and for supporting companies using welding to implement ISO 3834 have been developed by EWF and are presently being transferred to IIW in line with the EWF/IIW agreement established in 2000.

• Journal of Welding and Joining
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• v.31 no.5
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• pp.35-40
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• 2013

In this paper, we propose the Tangible Virtual Reality Representation Method to using haptic device and feature to morphology of created bead from Flux Cored Arc Welding. The virtual reality was started to rising for reduce to consumable materials and welding training risk. And, we will expected maximize virtual reality from virtual welding training. In this paper proposed method is get the database to changing the input factor such as work angle, travelling angle, speed, CTWD. And, it is visualization to bead from extract to optimal morphological feature information to using the Neural Network algorithm. The database was building without error to extract data from automatic robot welder. Also, the Neural Network algorithm was set a dataset of the highest accuracy from verification process in many times. The bead was created in virtual reality from extract to morphological feature information. We were implementation to final shape of bead and overlapped in process by time to using bead generation algorithm and calibration algorithm for generate to same bead shape to real database in process of generating bead. The best advantage of virtual welding training, it can be get the many data to training evaluation. In this paper, we were representation bead to similar shape from generated bead to Flux Cored Arc Welding. Therefore, we were reduce the gap to virtual welding training and real welding training. In addition, we were confirmed be able to maximize the performance of education from more effective evaluation system.

• Journal of Welding and Joining
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• v.34 no.1
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• pp.54-58
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• 2016

In this research paper, suggest method of generate same bead as an actual measurement data in virtual welding conditions, exploit morphology information of the bead that acquired through robot welding. It has many multiple risk factors to Beginners welding training, by we make possible to train welding in virtual reality, we can reduce welding training risk and welding material to exploit bead visualization algorithm that we suggest so it will be expected to achieve educational, environmental and economical effect. The proposed method is acquire data to each case performing robot welding by set the voltage, current, working angle, process angle, speed and arc length of welding condition value. As Welding condition value is most important thing in decide bead form, we would selected one of baseline each item and then acquired metal followed another factors change. Welding type is FCAW, SMAW and TIG. When welding trainee perform the training, it's difficult to save all of changed information into database likewise working angle, process angle, speed and arc length. So not saving data into database are applying the method to infer the form of bead using a neural network algorithm. The way of bead's visualization is applying the spline algorithm. To accurately represent Morphological information of the bead, requires much of morphological information, so it can occur problem to save into database that is why we using the spline algorithm. By applying the spline algorithm, it can make simplified data and generate accurate bead shape. Through the research paper, the shape of bead generated by the virtual reality was able to improve the accuracy when compared using the form of bead generated by the robot welding to using the morphological information of the bead generated through the robot welding. By express the accurate shape of bead and so can reduce the difference of the actual welding training and virtual welding, it was confirmed that it can be performed safety and high effective virtual welding education.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.430-432
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• 2002

Some confusion in the terminology concerning the weld quality and its assurance seems caused by the different practises currently in use around the world. Qualified welding personnel are not automatically certified personnel. Education and training are the tools to obtain qualification. Flexibility in training and education seems logical and the most cost-effective way to obtain qualified personnel. A third party seems essential for issuing recognised Certificates. Manufacturers of welded products continue to face increased demands and concerns regarding weld quality. The following are the main conditions influencing weld quality: $\bigcirc$ Establishing reliable productions procedures and tests that meet the requirements of established codes and standards. $\bigcirc$ Finding qualified welding personnel capable of reliably carrying out established welding procedures. The issue of hiring and keeping skilled welding personnel has been a crucial consideration for manufacturers worldwide for the past few decades. It will continue to be a concern for decades to come.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.479-481
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• 2019

In recent years, training systems in various industrial fields have been made using virtual reality (VR) technology and widely used in education. Virtual reality based training system is safe, there is no waste of material, and there are many advantages to be able to practice anytime and anywhere. For example, virtual reality welding training simulation system is widely used for field worker because it can perform actual joining of steel plate in immersive environment. At this time, realistic representation of the steel plate joint is important to maximize the effectiveness of the training, but existing techniques have limited the natural expression of the effect. In this study, we propose a method of visualizing joint effect based on shader in order to construct welding training system. The results of this study can be applied to the welding training system to improve the weld training effect to provide the user with high-quality visualization.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.82-88
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• 2002

Simulation systems allow a close inspection of the relation between welding parameters and the resulting weld seam. These systems are very useful in education of weld staff as well as production and planning. In training the influence of variations of parameters can be investigated without the need for real welding experiments. In the design phase requirements of the welding process can be taken into account without several iteration cycles. By estimating a good parameter set for the given welding task the set up phase for a new production cycle can be reduced

• Journal of Welding and Joining
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• v.32 no.2
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• pp.43-47
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• 2014

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN's system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.47-56
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• 2019

A deep neural network (DNN) model was proposed to predict the upset in the inertia friction welding process using a database comprising results from a series of FEM analyses. For the database, the upset length, upset beginning time, and upset completion time were extracted from the results of the FEM analyses obtained with various of axial pressure and initial rotational speed. A total of 35 training sets were constructed to train the proposed DNN with 4 hidden layers and 512 neurons in each layer, which can relate the input parameters to the welding results. The mean of the summation of squared error between the predicted results and the true results can be constrained to within 1.0e-4 after the training. Further, the network model was tested with another 10 sets of welding input parameters and results for comparison with FEM. The test showed that the relative error of DNN was within 2.8% for the prediction of upset. The results of DNN application revealed that the model could effectively provide welding results with respect to the exactness and cost for each combination of the welding input parameters.