I.S. Kim

Sensitivity analysis for process parameters influencing weld quality in robotic GMA welding process

Abstract Generally, the quality of a weld joint is strongly influenced by process parameters during the welding process. In order to achieve high quality welds, mathematical models that can predict the bead geometry to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on development of mathematical models for the selection of process parameters and the prediction of bead geometry (bead width, bead height, and penetration) in robotic gas metal arc (GMA) welding. A sensitivity analysis has also been conducted and compared with the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

Comparison of multiple regression and back propagation neural network approaches in modelling top bead height of multipass gas metal arc welds

Abstract With the advanced developments and automation of the welding process, the use of process optimisation techniques has increased. The objective of the present paper is to describe process optimisation techniques for the gas metal arc (GMA) welding process, based on experimental results generated by the process. Back propagation (BP) neural network and multiple regression methods are employed to study relationships between process parameters and top bead height for robotic multipass welding process, and to select a suitable model that provides the weld final configuration and properties as output and employs the process parameters as input. The process parameters, namely pass number, arc current, welding voltage and welding speed are optimised to produce the required top bead height. These techniques have achieved good agreement with the experimental data and yielded satisfactory results. Also, the BP neural network that was developed was compared to the empirical equations for predicting top bead height through additional experiments, and it was evident that the BP neural network was considerably more accurate than multiple regression techniques.

A study on the prediction of bead geometry in the robotic welding system

The gas metal are (GMA) welding is one of the most widely-used processes in metal joining process that involves the melting and solidification of the joined materials. To solve this problem, we have carried out the sequential experiment based on a Taguchi method and identified the various problems that result from the robotic GMA welding process to characterize the GMA welding process and establish guidelines for the most effective joint design. Also using multiple regression analysis with the help of a standard statistical package program, SPSS, on an IBM-compatible PC, three empirical models (linear, interaction, quadratic model) have been developed for off-line control which studies the influence of welding parameters on bead width and compares their influences on the bead width to check which process parameter is most affecting. These models developed have been employed for the prediction of optimal welding parameters and assisted in the generation of process control algorithms.

Effect of shielding gas composition on low temperature toughness of Al5083–O gas metal arc welds

Abstract There is an ever increasing range of shielding gases, which vary from the pure gases to complex mixtures based on argon, helium, oxygen, and carbon dioxide. The commercially available gas mixtures should be considered in terms of their suitability for ensuring arc and metal transfer stability, performance, and weld quality. The objective of the present paper is to study the toughness of Al5083–O aluminium alloy, to evaluate the variation of welding zone toughness as a function of the shielding gas composition and the testing temperature. To achieve these objectives, gas metal arc welding was performed with four different shielding gas compositions (100%Ar−0%He, 67%Ar+33%He, 50%Ar−50%He, and 33%Ar+67%He), and tests were carried out at four different temperatures, namely,+25°C (+77°F), −30°C (−22°F), −85°C (−121°F), and −196°C (−321°F). The welding zone was divided into four subzones for analysis, namely, weld metal, fusion line, heat affected zone, and base metal according to the notch position. Tensile and yield strengths did not show a great effect of testing temperature at +25°C to −85°C, but increased greatly at −196°C. Also, strain tended to increase as test temperature decreased. Shielding gas composition does not have a great influence on mechanical properties. The size and number of defects were least in the 33%Ar−67%He mixture. This shows that the higher the helium gas content, the lower the number of defects detected via radiographic inspection. In the impact test, the maximum load was lowest in the weld metal and highest in the base metal at room temperature, and the maximum load and displacement were higher and lower respectively at −196°C than those at other test temperatures, showing that the lower the test temperature, the higher the maximum load, without any special features related to the phase composition being observed in the load–deflection response. The absorbed energy of the weld metal notched specimens did not depend significantly on test temperature and shielding gas mixture. Conversely, the other specimens showed that as temperature was decreased, absorption energy increased slightly up to a maximum at −85°C, but then decreased markedly at −196°C.

Material Property Research of High Performance Concrete on its early Age by Fiber Bragg Grating Sensor

In this paper, properties of HPC beam during the early curing period are studied by FBG embedded strain sensors. Since FBG senses both strain and temperature simultaneously, FBG temperature sensors are used to compensate temperature effect of FBG strain sensors for accurate measurement of strain. Meanwhile FBG temperature sensors can monitor inner temperature changes of concrete. Simultaneous measuring of strain and temperature has been realized here. The results show that early age autogenous shrinkages of HPC concrete beams during the early curing period are pretty high. Also the content of water reducing agent on autogenous shrinkage has been analyzed.

Structural Stability Analyses of Composite Laminate Wind Turbine

The paper describes a structural stability analysis of fiber reinforced 10kW composite laminate wind turbine blades by using finite element method. The E-glass/epoxy orthotropic materials DB300、DBL850、L900 were employed for construction of a composite laminate shell structure. The composite laminate sheel structures were constructed by two types of lamination method. The rotating effect of wind blade was considered using the linear and the nonlinear static analysis. The results of the nonlinear analysis of displacement and stress show much lower than the linear analysis, because of the geometry nonlinear effect. From the contours of stress and displacement, the maximum stress appeared at the root of the blade, and maximum deformation occurred at the tip of the blade. Finally, the modal properties of the wind blade was investigated, including the natural frequency, modeshaps, and the centrifugal effect.

On-Line Empirical Models for Control of Bead Geometry in Robotic Arc Welding Process

Recently, not only robotic welders have replaced human welders in many welding applications, but also reasonable seam tracking systems are commercially available. However, fully adequate process control systems have not been developed due to a lack of reliable sensors and mathematical models that correlate welding parameters to the bead geometry for the automated welding process. Especially, real-time quality control in automated welding process is an important factor contributing to higher productivity, lower costs and greater reliability of the bead geometry. In this paper, on-line empirical models with experimental results are proposed in order to be applicable for the prediction of bead geometry. For development of the proposed predicting model, an attempt has been made to apply for a several methods. For the more accurate prediction, the prediction variables are first used to the surface temperatures measured using infrared thermometers with the welding parameters (welding current, arc voltage, CTWD and gas flow rate) because the surface temperature are strongly related to the formation of the bead geometry. And the developed model has been carried out a learning each time data acquired.

A Study on Magnetostrictive Sensor for Detecting the Weld Defects

Industries such as gas, oil, petrochemical, chemical, and electric power have generally employed for the operation and used to enlarge the equipment or structures that require a high capital investment. In order to meet these requirements, the industries are increasingly moved toward saving the experimental verifications and computer simulation. Therefore industries to reduce the maintenance costs without compromising operational safety have been forced on finding better and more efficient methods to inspect their equipment and structures. It was motivated to meet the industrial needs and to secure and maintain the institutes technical initiative and leadership in the development of this new and exciting technology. Also, the system with many sensors could be detected the weld defects, and was useful for real-time monitoring. This paper is focused on the development of the real-time non-contract monitoring system as an efficient tool for the experimental study of weld defects based on the relationship between the measured voltage and input parameters. The monitoring technology involves the use of Ms S (Magnetostrictive Sensors) for the generation and detection of the guided waves. The developed system was employed to the experimental study in order to fine the weld defects for steel object with artificial defects used in the welding field.

An Evaluation Approach for Prediction of Process Parameters with Genetic Algorithm

Gas Metal Arc (GMA) welding process has widely been employed due to the wide range of applications, cheap consumables and easy handling. A suitable mathematical model to achieve a high level of welding performance and quality should be required to study the characteristics for the effects of process parameters on the bead geometry in the GMA welding process. The objective of this paper is to present development of three empirical models (linear, curvilinear and intelligent model) based on full factorial design with two replications to estimate process parameters on top-bead width in robotic GMA welding process. Regression analysis was employed for optimization of the coefficients of linear and curvilinear models, but Genetic Algorithm (GA) was utilized to estimate the coefficients of intelligent model. ANOVA analysis using experimental data were carried out representation of main and interaction effects between process parameters on top-bead width. Resulting solutions and graphical representation showed that the developed intelligent model can be used for prediction on top-bead width in robotic GMA welding process