Optimisation of laser welding parameters for welding of P92 material using Taguchi based grey relational analysis

Creep strength enhanced ferritic(CSEF) steels are used in advanced power plant systems for high temperature applications. P92(Cr–W–Mo–V)steel, classified under CSEF steels, is a candidate material for piping, tubing, etc., in ultra-super critical and advanced ultra-super critical boiler applications. In the present work, laser welding process has been optimised for P92 material by using Taguchi based grey relational analysis(GRA).Bead on plate(BOP) trials were carried out using a 3.5 k W diffusion cooled slab CO_2 laser by varying laser power, welding speed and focal position. The optimum parameters have been derived by considering the responses such as depth of penetration, weld width and heat affected zone(HAZ) width. Analysis of variance(ANOVA) has been used to analyse the effect of different parameters on the responses. Based on ANOVA, laser power of 3 k W, welding speed of 1 m/min and focal plane at-4 mm have evolved as optimised set of parameters. The responses of the optimised parameters obtained using the GRA have been verified experimentally and found to closely correlate with the predicted value.? 2016 China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved.

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.