Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

The present study is to optimize the process parameters for friction welding of duplex stainless steel(DSS UNS S32205).Experiments were conducted according to central composite design.Process variables,as inputs of the neural network,included friction pressure,upsetting pressure,speed and burn-off length.Tensile strength and microhardness were selected as the outputs of the neural networks.The weld metals had higher hardness and tensile strength than the base material due to grain refinement which caused failures away from the joint interface during tensile testing.Due to shorter heating time,no secondary phase intermetallic precipitation was observed in the weld joint.A multi-layer perceptron neural network was established for modeling purpose.Five various training algorithms,belonging to three classes,namely gradient descent,genetic algorithm and LevenbergeM arquardt,were used to train artificial neural network.The optimization was carried out by using particle swarm optimization method.Confirmation test was carried out by setting the optimized parameters.In conformation test,maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv,respectively.The metallurgical investigations revealed that base metal,partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50.

Comparative study on transverse shrinkage, mechanical and metallurgical properties of AA2219 aluminium weld joints prepared by gas tungsten arc and gas metal arc welding processes

Aluminium alloy AA2219 is a high strength alloy belonging to 2000 series. It has been widely used for aerospace applications, especially for construction of cryogenic fuel tank. However, arc welding of AA2219 material is very critical. The major problems that arise in arc welding of AA2219 are the adverse development of residual stresses and the re-distribution as well as dissolution of copper rich phase in the weld joint.These effects increase with increase in heat input. Thus, special attention was taken to especially thick section welding of AA2219-T87 aluminium alloy. Hence, the present work describes the 25 mm-thick AA2219-T87 aluminium alloy plate butt welded by GTAW and GMAW processes using multi-pass welding procedure in double V groove design. The transverse shrinkage, conventional mechanical and metallurgical properties of both the locations on weld joints were studied. It is observed that the fair copper rich cellular(CRC) network is on Side-A of both the weldments. Further, it is noticed that, the severity of weld thermal cycle near to the fusion line of HAZ is reduced due to low heat input in GTAW process which results in non dissolution of copper rich phase. Based on the mechanical and metallurgical properties it is inferred that GTAW process is used to improve the aforementioned characteristics of weld joints in comparison to GMAW process.

Transient out-of-plane distortion of multi-pass fillet welded tube to pipe T-joints

The out-of-plane distortion induced in a multi-pass circumferential fillet welding of tube to pipe under different weld sequences and directions was studied using Finite Element Method(FEM) based Sysweld software and verified experimentally. The FEM analyses consisted of thermal and mechanical analyses.Thermal analysis was validated with experimental transient temperature measurements. In the mechanical analysis, three different weld sequences and directions were considered to understand the mechanism of out-of-plane distortion in the tube to pipe T-joints. It was learnt that the welding direction plays a major role in minimizing the out-of-plane distortion. Further, during circumferential fillet welding of the tube to pipe component, the out-of-plane distortion generated in the x direction was primarily influenced by heat input due to the start and stop points, whereas the distortion in the z direction was influenced by time lag and welding direction. The FEM predicted distortion was compared with experimental measurements and the mechanism of out-of-plane distortion was confirmed.

Multi-objective Optimization of Continuous Drive Friction Welding Process Parameters Using Response Surface Methodology with Intelligent Optimization Algorithm

The optimum friction welding （FW） parameters of duplex stainless steel （DSS） UNS $32205 joint was determined. The experiment was carried out as the central composite array of 30 experiments. The selected input parameters were friction pressure （F）, upset pressure （U）, speed （S） and burn-off length （B）, and responses were hardness and ultimate tensile strength. To achieve the quality of the welded joint, the ultimate tensile strength and hardness were maximized, and response surface methodology （RSM） was applied to create separate regression equations of tensile strength and hardness. Intelligent optimization technique such as genetic algorithm was used to predict the Pareto optimal solutions. Depending upon the application, preferred suitable welding parameters were selected. It was inferred that the changing hardness and tensile strength of the friction welded joint influenced the upset pressure, friction Pressure and speed of rotation.

A Comparative Study of Artificial Neural Network and Response Surface Methodology for Optimization of Friction Welding of Incoloy 800 H

This article deals with the optimization of process parameters for friction welding of Incoloy 800 H rod and compares the results obtained by response surface methodology（RSM） and artificial neural network（ANN）.The experiments were carried out on the basis of a five-level,four-variable central composite design.The output parameters were the tensile strength and burn-off length（BOL）.They were considered as a function of four independent input variables,namely heating pressure（HP）,heating time,upsetting pressure（UP）,and upsetting time.The RSM results showed that the quadratic polynomial model depicted the interconnection between individual element and response.For optimizing the process parameters,ANN analysis was used,and the optimal configuration of the ANN model was found to be 4–9–2.For modeling aspect,a requisite trained multilayer perceptron neural network was rooted,and a quick propagation training algorithm was used to train ANN.The purpose of optimization was to decide the maximum tensile strength and minimum burn-off length of the welded joint which was done by varying the friction welding process variables.The order of importance of input parameters for friction welding of Incoloy 800 H was HP〉 UP〉 N〉BOL.After predicting the model using RSM and ANN,a comparison was made for predicting the effectiveness of two methodologies.By analyzing the results,it was observed that as compared to RSM,ANN model was more specific.