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The exploration of the prediction of wired electro-discharge machining parameters is becoming progressively more crucial in order to choose the ideal parameters for making products with impeccable designs. The objective of this work is to develop an augmented adaptive neuro-fuzzy inference system (ANFIS) model by encrypting the snake optimizer for estimating the surface roughness, material removal rate, and residual stress of Monel 400 alloy in wire electric discharge machining (WEDM). The performance of the indicated augmented model called ANFIS-SO was evaluated using 64 trials of WEDM experimental data. As a means of emphasizing the performance of the presented model, estimation results were compared to those from existing models, which included integration of ANFIS and beetle antennae search capabilities, reptile search algorithms, and COOT algorithms. The ANFIS-SO model showed excellent performance based on the statistical benchmarks tested for the developed models. The model achieved RMSE of 0.0793, 0.2781, and 16.3286 with performance errors of 0.3203, 1.9687, and 1112.529 along with an accuracy of 99%, 98%, and 95% for surface roughness, MRR, and residual stress, respectively, which are extremely more accurate than the ANFIS, ANFIS-BAS, ANFIS-RSA, and ANFIS-COOT. As a result of these findings, it is evident that the developed ANFIS-SO model might be used with reliability when designing future model forecasting approaches for optimizing machining parameters. Therefore, it is discovered that using ANFIS-SO is an innovative method for determining WEDM machining parameters which are very promising. [ABSTRACT FROM AUTHOR]

The present work aims to develop nickel-based superalloys by studying the impact of their alloying elements in providing the desired physical and mechanical characteristics. At present, materials are being developed using experiment-driven trial and improvement. The long time duration in their development rules out the planning of new materials and thus leading engineers to select the commercially available materials with a compromise towards the properties. The computationally developed materials have the potential to meet the needs of the materials engineers, and now the materials are being brought into the centre of the planning method. The selection of suitable elemental composition seems to be a multi-criterion decision-making problem with conflicting and diverse objectives as different elements execute different properties at varied conditions. Nickel is an adaptable element that can alloy with most metals. The present study deals with the procedure that can be applied to identify suitable elemental composition for a Ni based super alloy, allowing them to adjust for required properties in order to enhance its use in a particular application. The methodology deals with the selection of 'n' number of alternatives to give desired properties and applying multi-criteria decision-making algorithms to rank and select one among the alternatives developed. The Spearman rank correlation coefficient in the range of 0.74-1.00 shown a strong correlation between the methods adopted. From the analysis Cr (wt. 12%), Co (wt. 16.5%), Mo (wt. 2.5%), W (wt. 1.5%), Nb (wt. 1.5%), Al (wt. 2%), Ti (wt. 3%), Fe (wt. 1.5%), C (wt. 0.2%), and B (wt.0.03%) can be developed to get an yield strength of 504.94 MPa, Ultimate tensile strength of 678.62 MPa, % elongation 18.53 and Creep resistance at 1000 hours work life of 263 MPa to get better ductile properties. Also Cr (wt. 18%), Co (wt. 17%), Mo (wt. 4%), W (wt. 2%), Al (wt. 2%), Ti (wt. 1.5%), Fe (wt. 2%), C (wt. 0.1%) and B (wt.0.02%) can be developed to get an yield strength of 451.25 MPa, Ultimate tensile strength of 669.57 MPa, % elongation 25.18 and Creep resistance at 1000 hours work life of 309 MPa to get better strength properties. [ABSTRACT FROM AUTHOR]