• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.499-503
• /
• 1994

The feedback control systems of welding process using visual information can improve weld qualities. However, it is very difficult to get the visual information of weld pool since welding are is much stronger than light from weld pool. To explore the possibility of extending the capability of automatic welding machines a study of a closed loop controlled welding system consisted of a GTA welding machine, a vision system, a stepping motor system and a digital computer was undertaken. Particularly, in this system, a CCD camera with 850nm long pass filter was focused on the weld pool to give a weld pool image. Subsequently, image analysis technique has been developed to measure a weld pool width. Using this weld pool width measurement, a colsed loop control system adjusted welding speed to maintain constant weld pool width.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.1
• /
• pp.150-157
• /
• 1997

Uniform weld pool shape is important in determining the weld quality. And weld pool width is one of the most dominant factors of the seld pool shape. In order to control the weld pool width, the fuzzy logic controller, which is well adapted to the complicated nonlinear systems such as welding, was used in this study. The weld pool image was obtained through CCD camera, and the weld pool width was calculated by processing the image. Uaing the calculated width, welding speed, as a control input, was inferred by the fuzzy logic controller. An uniform weld pool width can be successfully obtained regardless of the disturbances in the system.

• Proceedings of the KWS Conference
• /
• 1995.10a
• /
• pp.3-20
• /
• 1995

Development of Real-Time Quality Evaluation of Friction Welding by Acousitc Emission : Report 1 ABSTRACT : According as the friction welding has been increasingly applied in manufacturing various machine components because of its significant economic and technical advantages, one of the important concerns is the reliable quality monitoring method for a good weld quality with both joint strength and toughness in the process of its production. However no reliable nondestructive test method is available at present to determine the weld quality particularly in process of production. So this paper presents an experimental examination and quantitative analysis for the real-time evaluation of friction weld quality by acoustic emission, as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was confirmed, through this study, that AE techniques can be reliably applied to evaluating the friction weld qualify with 100% joint strength, as the cumulative AE counts occurring during welding period were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds. Real-Time Evaluation of Automatic Production Quality Control for Friction Welding Machine : Report 2 Abstract : Both in-process quality control and high reliability of the weld is one of the major concerns in applying friction welding to the economical and qualified mass-production. No reliable nondestructive monitoring method is available at present to determine the real-time evaluation of automatic production quality control for friction welding machine. This paper, so that, presents the experimental examinations and statistical quantitative analysis of the correlation between the initial cumulative counts of acoustic emission(AE) occurring during plastic deformation period of the welding and the tensile strength of the welded joints as well as the various welding variables, as a new approach which attempts finally to develop an on-line (or real-time) quality monitoring system and a program for the process of real-time friction welding quality evaluation by initial AE cumulative counts. As one of the important results, it was well confirmed that the initial AE cumulative counts were quantitatively and cubically correlated with reliability of 95% confidence level to the joint strength of the welds, bar-to-bar (SCM4 to SUM31, SCM4 to SUM24L) and that an AE technique using initial AE counts can be reliably applied to real-time strength evaluation of the welded joints, and that such a program of the system was well developed resulting in practical possibility of real-time quality control more than 100% joint efficiency showing good weld with no micro-structural defects.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.8 no.5
• /
• pp.70-75
• /
• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.474-478
• /
• 1996

Process monitoring in laser welding is essential for automation and quality control of products. Various signals from laser welding, such as plasma, sound, optical signals, etc., are utilized for monitoring the process and detecting abnormal weld conditions. In this study, both W light from plasma formed above the weld pool and IR signal from the melting pool are detected with photodiodes and PC-based A/D board, and analyzed to give a guidance about the weld quality. Experimental results show the possibility of using the signals for predicting and evaluating the weld qualify and adapting into the system for on-line process monitoring.

• International Journal of Korean Welding Society
• /
• v.1 no.1
• /
• pp.7-12
• /
• 2001

Real time monitoring has become critical as the use of laser welding increases. Plasma and spatter are measured and used as the signal for estimating weld quality. The estimating algorithm was made using the fuzzy pattern recognition with the area of data that is beyond the tolerance boundary. Also, an algorithm that detects the spatter and the localized defect was created in order to kd the partially produced pit and the sudden loss of weld penetration. These algorithms were used in quality monitoring of the $CO_2$ laser tailored blank weld. Statistical program that can display the laser weld quality result and the signal transition was made for the first stage of the remote control system.

• Journal of Welding and Joining
• /
• v.18 no.4
• /
• pp.48-54
• /
• 2000

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor information about weld characteristics and process parameters as well; as t modify those parameters to hold weld. The objectives of this paper are to realize the mapping characteristics of bead width through the neural network and multiple regression method as well as to select the most accurate model in order to control the weld quality(bead width0. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1404-1407
• /
• 1996

A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to prove the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis. A neural network estimator is designed to estimate weld pool size from temperature informations. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size.

• Proceedings of the KWS Conference
• /
• 2002.10a
• /
• pp.307-313
• /
• 2002

This paper describes a weld pool monitoring technique, which is based on the weld pool image analysis. The proposed image analysis algorithm uses machine vision techniques to extract geometrical information from the weld pool image such as maximum weld pool width, gap width and misalignment between the joint longitudinal axis and the welding wire. These can be related to the welding parameters (welding voltage and current, wire feed speed and standoff) to produce control actions necessary to ensure that the required weld quality will be achieved. The experiments have shown that the algorithm is able to produce good estimates of the weld pool geometry; however, the adjustment of the camera parameters affects the image quality and, consequently, has a great influence over the estimation.

• Journal of Ocean Engineering and Technology
• /
• v.13 no.2 s.32
• /
• pp.74-82
• /
• 1999

Nowadays, the crank shaft motor vehicle has become essential as the important component. The machining precision was asked for manufacturing the shaft. They could be unstable in the quality by the conventional are welding. Both in-process quailty control and high reliability of the weld are the major concerns in applying friction wlding to the economical and qualified mass-production. No reliable nondestructive monitoring method is avaliable at present to determine the real-time evaluation of automatic production quality control for bar-to-bar friction welding of the crank shaft of O.D 24mm for motor vehicle. This paper, so that, presents the experimental examinations and statistical quantitative analysis of the correlation between the cumulative counts of acoustic emission(AE) during plastic deformation periods of the welding and the tensile strength and other properties of the bar-to-bar welded joints of O.D. 24mm shaft as well as the various welding variables, as a new approach which attempts finally to develop real-time quality monitoring system for friction welding, resulting in practical possiblility of real-time quality control more than 100% joint efficiency showing good weld with no micro structural defects.