Control of welding residual stress for dissimilar laser welded materials

The most common problem of welding dissimilar metals (DMWs) with respect to residual stresses is the differences in the coefficient of thermal expansion and heat conductivity of the two welded metals. In the present work, a CO2 continuous laser welding process was successfully applied and optimized for joining a dissimilar AISI 316 stainless steel and low carbon steel plates. The Taguchi approach with three factors (selected welding parameters) at five levels each (L3-25) was applied to find out the optimum levels of welding speed, laser power and focal position for CO2 keyhole laser welding of dissimilar butt weld. The responses outputs were the residual stresses at different depth in the heat affected zone (HAZ). The Hole-Drilling Method technique was applied to measure the residual stress of dissimilar welded components. The results were analysed using analysis of variances (ANOVA) and signal-to-noise ratios (S/N) for an effective parameters combination. Statistical models were developed to describe the influence of the input parameters on the residual stress at different specimen levels; to predict there value within the limits of the variables under investigation. The result indicates that the developed models can predict the responses satisfactorily

Using Taguchi method to optimize welding pool of dissimilar laser welded components

In the present work CO2 continuous laser welding process was successfully applied and optimized for joining a dissimilar AISI 316 stainless steel and AISI 1009 low carbon steel plates. Laser power, welding speed, and defocusing distance combinations were carefully selected with the objective of producing welded joint with complete penetration, minimum fusion zone size and acceptable welding profile. Fusion zone area and shape of dissimilar austenitic stainless steel with ferritic low carbon steel were evaluated as a function of the selected laser welding parameters. Taguchi approach was used as statistical design of experiment (DOE) technique for optimizing the selected welding parameters in terms of minimizing the fusion zone. Mathematical models were developed to describe the influence of the selected parameters on the fusion zone area and shape, to predict its value within the limits of the variables being studied. The result indicates that the developed models can predict the responses satisfactorily

Optimization of tensile strength of ferritic / austenitic laser welded components

Ferritic/Austenitic (F/A) joints are a popular dissimilar metals combination used in many applications. F/A joints are usually produced using conventional processes. Laser beam welding (LBW) has recently been successfully used for the production of F/A joints with suitable mechanical properties. In this study, a statistical design of experiment (DOE) was used to optimise selected laser beam welding parameters (laser power, welding speed, and focus length). The Taguchi approach was used for the selected factors, each having five levels (L-25; 5*3). Joint strength was determined using the notched tension strength (NTS) method. The results were analysed using analyses of variance (ANOVA) and the signal-to-noise ratios (S/N) ratio for the optimal parameters, and then compared with the base material. The experimental results indicate that the F/A laser welded joints are improved effectively by optimizing the input parameters using the Taguchi approach

Comparison of ANN and DoE for the prediction of laser machined micro-channel dimensions

This paper presents four models developed for the prediction of the dimensions of laser formed micro-channels. Artificial Neural Networks (ANNs) are often used for the development of predictive models. Three feed-forward, back-propagation ANN models varied in terms of the number and the selection of training data, were developed. These ANN models were constructed in LabVIEW coding. The performance of these ANN models was compared with a 33 statistical design of experiments (DoE) model built with the same input data. When compared with the actual results two of the ANN models showed greater prediction error than the DoE model. The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model

Methods of measuring residual stresses in components

Residual stresses occur in many manufactured structures and components. Large number of investigations have been carried out to study this phenomenon and its effect on the mechanical characteristics of these components. Over the years, different methods have been developed to measure residual stress for different types of components in order to obtain reliable assessment. The various specific methods have evolved over several decades and their practical applications have greatly benefited from the development of complementary technologies, notably in material cutting, full-field deformation measurement techniques, numerical methods and computing power. These complementary technologies have stimulated advances not only in measurement accuracy and reliability, but also in range of application; much greater detail in residual stresses measurement is now available. This paper aims to classify the different residual stresses measurement methods and to provide an overview of some of the recent advances in this area to help researchers on selecting their techniques among destructive, semi destructive and non destructive techniques depends on their application and the availabilities of those techniques. For each method scope, physical limitation, advantages and disadvantages are summarized. In the end this paper indicates some promising directions for future developments

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion. Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds