A.K. Bhaduri

Effects of dilution on microstructure and wear behaviour of NiCr hardface deposits

Abstract The effect of dilution on the microstructure, hardness and wear properties of two nickel base NiCr hardfacing alloys deposited using the gas tungsten arc welding (GTAW) process has been studied. Dilution from the base metal altered the microstructure, volume fraction and type of precipitates in the deposit, all of which varied with the distance from the deposit/substrate interface. The microstructural variation in the deposit was accompanied by corresponding variation in the deposit hardness. A pin on disc wear test, carried out using pins with varying thickness of deposit, showed that the wear resistance of the deposit increased with increasing thickness of the deposit, indicating that the wear resistance decreases with increasing dilution from the base metal. The present study brings out the effect of dilution from the substrate material on the properties of NiCr hardface deposits and the need to ensure a minimum thickness of GTAW deposits of these hardfacing alloys for obtaining the desired wear resistance.

Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

Abstract In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.

Genetic-Algorithm-Based Computational Models for Optimizing the Process Parameters of A-TIG Welding to Achieve Target Bead Geometry in Type 304 L(N) and 316 L(N) Stainless Steels

The weld-bead geometry in 304LN and 316LN stainless steels produced by A-TIG welding plays an important role in determining the mechanical properties of the weld and its quality. Its shape parameters such as bead width, depth of penetration, and reinforcement height are decided according to the A-TIG welding process parameters such as current, voltage, torch speed, and arc gap. Identification of a suitable combination of A-TIG process parameters to produce the desired weld-bead geometry required many experiments, and the experimental optimization of the A-TIG process was indeed time consuming and costly. Therefore it becomes necessary to develop a methodology for optimizing the A-TIG process parameters to achieve the target weld-bead geometry. In the present work, genetic algorithm (GA)-based computational models have been developed to determine the optimum/near optimum process parameters to achieve the target weld-bead geometry in 304LN and 316LN stainless steel welds produced by A-TIG welding.

Delta ferrite prediction in stainless steel welds using neural network analysis and comparison with other prediction methods

The ability to predict the delta ferrite content in stainless steel welds is important for many reasons. Depending on the service requirement, manufacturers and consumers often specify delta ferrite content as an alloy specification to ensure that weld contains a desired minimum or maximum ferrite level. Recent research activities have been focused on studying the effect of various alloying elements on the delta ferrite content and controlling delta ferrite content by modifying the weld metal compositions. Over the years, a number of methods including constitution diagrams, Function Fit model, Feed-forward Back-propagation neural network model have been put forward for predicting the delta ferrite content in stainless steel welds. Among all the methods, neural network method was reported to be more accurate compared to other methods. A potential risk associated with neural network analysis is over-fitting of the training data. To avoid over-fitting, Mackay has developed a Bayesian framework to control the complexity of the neural network. Main advantages of this method are that it provides meaningful error-bars for the model predictions and also it is possible to identify automatically the input variables which are important in the non-linear regression. In the present work, Bayesian neural network (BNN) model for prediction of delta ferrite content in stainless steel weld has been developed. The effect of varying concentration of the elements on the delta ferrite content has been quantified for Type 309 austenitic stainless steel and the duplex stainless steel alloy 2205. The BNN model is found to be more accurate compared to that of the other methods for predicting delta ferrite content in stainless steel welds.

Influence of State of Stress on Dynamic Recrystallization in a Titanium-Modified Austenitic Stainless Steel

The influence of the state of stress on the microstructure and dynamic recrystallization (DRX) in a titanium-modified austenitic stainless steel is assessed by performing plane-strain and uniaxial hot compression studies. Although the state of stress does not alter the mechanisms of DRX nucleation, the kinetics of DRX is hindered during plane-strain deformation vis-à-vis uniaxial deformation.

Bayesian neural network analysis of ferrite number in stainless steel welds

Abstract Bayesian neural network (BNN) analysis has been used in the present work to develop an accurate model for predicting the ferrite content in stainless steel welds. The analysis reveals the influence of compositional variations on ferrite content for the stainless steel weld metals, and examines the significance of individual elements, in terms of their influence on ferrite content in stainless steel welds, based on the optimised neural network model. This neural network model for ferrite prediction in stainless steel welds has been developed using the database used to generate the WRC-1992 diagram and the first authors laboratory data. The optimised committee model predicts the ferrite number (FN) in stainless steel welds with greater accuracy than the constitution diagrams and the other FN prediction methods. Using this BNN model, the influence of variations of the individual elements on the FN in austenitic stainless steel welds is also determined, and it is found that the change in FN is a non-linear function of the variation in the concentration of the elements. Elements such as Cr, Ni, N, Mo, Si, Ti, and V are found to influence the FN more significantly than the other elements present in stainless steel welds. Manganese is found to have a weaker influence on the FN. A noteworthy observation is that Ti influences the FN more significantly than does Nb, whereas the WRC-1992 diagram considers only the Nb content in calculating the Cr equivalent.

Evaluation of repair welding procedures for 2.25Cr–1Mo and 9Cr–1Mo steel welds

Abstract Chromium–molybdenum steels are extensively used in the steam generator circuits of power plants. These components may require welding of the cracks that can develop during fabrication, storage, and transportation stages, or during the service life of the plant. This investigation compares repair welding methods for Cr–Mo steels, using 2.25Cr–1Mo and 9Cr–1Mo materials. To simulate aging during service, welds were heat treated at 873 K for 5000 h. Simulated repair welding of the aged welds was carried out at the weld/base metal interface, i.e. at the location at which cracks are usually reported to occur during service. Two repair welding methods (half bead and butter bead temper bead methods) conforming to the ASME Boiler and Pressure Vessel Code were used. Tensile properties, hardness profiles, and X-ray diffraction based residual stress distributions were determined for both the Cr–Mo steel welds to evaluate the simulated repair welds. Analysis of the test results showed that both the repair welding methods can be used for 2.25Cr–1Mo steel welds, although the butter bead temper bead method is much more suitable for both the 2.25Cr–1Mo and 9Cr–1Mo steel welds.

Direct observation of amophization in load rate dependent nanoindentation studies of crystalline Si

Indentation at very low load rate showed region of constant volume with releasing load in crystalline (c-)Si, indicating a direct observation of liquidlike amorphous phase which is incompressible under pressure. Signature of amorphization is also confirmed from load dependent indentation study where increased amount of amorphized phase is made responsible for the increasing elastic recovery of the sample with increasing load. Ex situ Raman study confirmed the presence of amorphous phase at the center of indentation. The molecular dynamic simulation has been employed to demonstrate that the effect of indentation velocities has a direct influence on c-Si during nanoindentation processes.

Effect of dilution on GTAW Colmonoy 6 (AWS NiCr–C) hardface deposit made on 316LN stainless steel

Abstract Nickel based Colmonoy 6 (conforming to AWS NiCr–C) hardfacing alloy finds application in hardfacing of various components made of austenitic stainless steel (SS) used in fast reactors. Owing to considerable difference in melting points of the SS and Colmonoy 6 alloys, significant dilution from substrate occurs during hardfacing using gas tungsten arc welding process. Dilution has a significant effect on microstructure, hardness and wear resistance of the deposit. To overcome the adverse effects of dilution on the hardness and, hence, the wear resistance of the deposit, often, the minimum thickness specified for the deposit on hardfaced components is high, which in turn increases the susceptibility of the deposit to cracking during deposition. In the present investigation, microstructure of different layers of multilayer Colmonoy 6 deposits on 316LN SS is characterised by optical and scanning electron microscopy, and the correlation between hardness and microstructure of the individual layers with dilution from the base metal has been established. The dilution from the base material is the highest in the first layer, and it progressively decreases in the subsequent layers. With progressive decrease in dilution, the precipitate fraction increases from about 16 to 20% from the first to the fifth deposit layers. This is accompanied by hardness increase from about 480 to 800 HV. The precipitates in the deposit consist of both borides and carbides, with the boride content varying more with dilution than the carbide content. The boride fraction increased from 5 to 8% with a decrease in dilution; however, layer to layer variation in carbide fraction was only marginal at about 11–12%. High dilution from the base material suppresses the formation of borides in the deposit and is responsible for low hardness of the deposit diluted with the austenitic SS compared to those of the undiluted deposit.

Real-Time Monitoring of Weld Pool during GTAW using Infra-Red Thermography and analysis of Infra-Red thermal images

Real-time monitoring of the weld pool using infra-red (IR) thermography during gas tungsten arc (GTA) welding is gaining importance due to the requirements for on-line monitoring and control of the welding process. To facilitate real-time monitoring of the weld pool, a computer-controlled GTA welding machine with sensing of the weld pool using IR camera has been developed. The IR camera, mounted on the torch assembly, monitors the molten pool and the surface temperature distribution surrounding the weld pool during GTA welding. Temperature profiles were measured on the plates using thermocouples in combination with IR thermography to determine the emissivity of the plate surface. GTA welding was carried out on 3 mm-thick 316LN stainless steel (SS) plates under different welding conditions. IR thermal images were acquired on-line and analysed. A linear relationship was obtained between the thermal bead width, determined by line-scan analysis technique, and the actual bead width, measured by cross-sectional optical microscopy. The computed macroscopic temperature gradient and the actual of weld bead depth of penetration showed an inverse relationship. Full-frame analysis was carried out to estimate the surface temperature distribution for square-butt weld joints. For 1316LN SS weld joints, IR thermal signatures were acquired for various weld defects, such as lack of fusion, lack of penetration and tungsten inclusions, for use as reference signatures for on-line monitoring during GTA welding.