• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2007.11a
• /
• pp.247-248
• /
• 2007

• Journal of Welding and Joining
• /
• v.22 no.1
• /
• pp.50-57
• /
• 2004

This paper provides FE and experimental validation of the defect assessment method for strength mismatched welded structures, resulting from the Brite Euram SINTAP (Structural Integrity Assessment Procedures for European Industry) project. This shows that the proposed method is conservative, and that the degree of conservatism is similar to that embedded in the methods for homogeneous structures. It provides confidence in the use of the proposed SINTAP method for assessing defective weld strength mismatched structures.

• Proceedings of the KWS Conference
• /
• 2003.05a
• /
• pp.90-92
• /
• 2003

• Proceedings of the KWS Conference
• /
• 2002.05a
• /
• pp.214-217
• /
• 2002

• Proceedings of the KWS Conference
• /
• 1992.10a
• /
• pp.199-202
• /
• 1992

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.11
• /
• pp.1060-1066
• /
• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material efficiently. In this paper a detection of the welding defect of thin SUS 304 plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (20kHz) ultrasonic transducer was used to infuse the welded thin SUS 304 plates with a short pulse of sound for 280ms. The ultrasonic source has a maximum power of 2kW. The surface temperature of the area under inspection is imaged by a thermal infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the defect tip and heated up highly, are observed. From the sequence of the thermosonic images, the location of defective or inhomogeneous regions in the welded thin SUS 304 plates can be detected easily.

• Journal of Welding and Joining
• /
• v.16 no.3
• /
• pp.64-73
• /
• 1998

This paper is concerned with defects detection and evaluation of heat affected zone (HAZ) in austenitic stainless steel type 304 by ultrasonic wave and neural network. In experiment, the reflected ultrasonic defect signals from artificial defects (side hole, vertical hole, notch) of HAZ appears as beam distance of prove-defect, distance of probe-surface, depth of defect-surface on CRT. For defect classification simulation, neural network system was organized using total results of ultrasonic experiment. The organized neural network system was learned with the accuracy of 99%. Also it could be classified with the accuracy of 80% in side hole, and 100% in vertical hole, 90% in notch about ultrasonic pattern recognition. Simulation results of neural network agree fairly well with results of ultrasonic experiment. Thus were think that the constructed system (ultrasonic wave - neural network) in this work is useful for defects dection and classification such as holes and notches in HAZ of austenitic stainless steel 304.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.25 no.1
• /
• pp.48-53
• /
• 2016

An EMAT can be used to reliably detect defects as it serves as a non-contact transducer with the ability to transmit ultrasonic waves into specimens without couplant. Moreover, an EMAT can easily generate desired waves by altering the design of the coil and magnet. This study proposes an SH-EMAT to evaluate the integrity of the TIG welding part. A stainless steel was welded using the TIG welding method. The welding current was varied to create artificial defects. Both the PA-UT and the RT were applied to verify the defect size. The experimental results generated by using the EMAT were compared with those methods. The amplitude was observed to decrease with an increase in the defect size. These results confirmed that the presence of defects can be reliably detected by attenuation of signal amplitude. The results demonstrated that the proposed method is suitable for evaluating the integrity of TIG welding.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2005.10a
• /
• pp.109-112
• /
• 2005

Friction welding is welding method to use frictional heat of two material. A defect of friction welding is that create flash. The flash is part that must have cut after welding finished. But the welding part with flow gallery by friction welding can't cut flash. Therefore the welding part with flow gallery was designed with no effect in flow. In this research, decide the welding shape parameter of welding part with flow gallery and do friction welding analysis. In friction welding analysis, must input necessary S-S curve, friction coefficient by temperature change, upset pressure, RPM etc. According to analysis result, decided the optimal shape of welding part with no effect in flow.

• Journal of the Society of Naval Architects of Korea
• /
• v.56 no.6
• /
• pp.473-479
• /
• 2019

Welding is one of the most popular joining methods and most welding quality estimation methods are executed using joined material. This paper propose welding quality estimation methods using dynamic current, voltage and resistance which are obtained during welding in real time. There are many kinds of welding method. Among them, we focused on the projection welding and gathered dynamic characteristics from two different types of projection welding. For image learning, graphs are drawn using obtained current, voltage and resistance, and the graphs are converted to images. The images are labeled with two sub-categories - normal and defect. For deep learning of images obtained from welding, Convolutional Neural Network (CNN) is applied, and Tensorflow was used as a framework for deep learning. With two resistance welding test datasets, we conclude that the Convolutional Neural Network helps in predicting the welding quality.