Wire Electrical Discharge Machining (WEDM) is a competent method employed for machining intricate shapes in electrically conductive harder materials. Metal Matrix Composites (MMCs) possesses improved properties and considered as an alternate material for various engineering applications. An enduring issue with MMCs is that they are problematic for machining, because of abrasive nature of hard reinforcing phase. This current exploration is dealing the WEDM machinability analysis of LM6/SiC/Dunite Hybrid Metal Matrix Composites (HMMC) fabricated by two step stir casting process and to develop an artificial intelligence decision making model for WEDM the process parameters. The influence of input machining process variables namely Pulse ON, Pulse OFF, Flushing Pressure, Wire Feed and Servo Voltage against the desired output like Material Removal Rate (MRR), Surface finish, overcut, circularity error and perpendicularity error are investigated. Grey Relational Analysis (GRA) method is employed and grey relational coefficient values are given as input values for evolving the Adaptive Neuro Fuzzy Inference System (ANFIS) to predict the desired performance characteristic. A comparative study has been accomplished for validating the outcomes attained from evolved models and experimentation outcomes. The performance analysis of developed model proves that the developed model and this method are efficacious for predicting the desired performance measures.

The machinability and the process parameter optimization of turning operation for 15-5 Precipitation Hardening (PH) stainless steel have been investigated based on the Taguchi based grey approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). An L₂₇ orthogonal array was selected for planning the experiment. Cutting speed, depth of cut and feed rate were considered as input process parameters. Cutting force (F_z) and surface roughness (R_a) were considered as the performance measures. These performance measures were optimized for the improvement of machinability quality of product. A comparison is made between the multi-criteria decision making tools. Grey Relational Analysis (GRA) and TOPSIS are used to confirm and prove the similarity. To determine the influence of process parameters, Analysis of Variance (ANOVA) is employed. The end results of experimental investigation proved that the machining performance can be enhanced effectively with the assistance of the proposed approaches.

This present exploration detailed the machining performance characteristics of stir-casted aluminum metal matrix composites during CNC turning process with Tungsten Carbide inserts. An endeavor has been taken to attain the possible set of process parameter for obtaining the better machinability performance such as minimized cutting temperature, vibration, and roughness of machined surfaces. Response surface methodology has been adopted to evolve empirical relationship among the desired performance characteristics and effects of interaction among those selected variables were determined. An analysis on wear of the tool was performed with the aid of SEM and EDAX. The outcome of the exploration divulged that the poor surface quality occurred because of adhesion of composites on inserts and abrasion wear in rack face. Notching wear was observed at the time of machining of Al-SiC Metal Matrix Composites (MMC), which produces an abrasive chip. The evolved empirical relationship are validated by confirmation tests, and it is affirmed that the employed RSM approach is an prevailing tool for developing empirical relations and also fruitful approach to envisage the desired performance measures in an effective and efficient manner.

Incoloy 800H (Fe–Ni–Cr) is an iron-based superalloy that is difficult to machine because of its rapid work-hardening behaviour. Although Taguchi-based grey relational analysis has previously been applied to machining optimisation problems, studies employing an integrated grey-fuzzy framework for the dry turning of Incoloy 800H remain limited. Therefore, the present investigation aims to develop and validate a grey-fuzzy optimisation approach for determining the optimal dry turning parameters of Incoloy 800H by simultaneously minimising machining forces, surface roughness, and specific cutting pressure. Cutting speed (35, 45, and 55 m/min), feed rate (0.02, 0.04, and 0.06 mm/rev), and depth of cut (0.5, 0.75, and 1 mm) were selected as input factors, whereas feed force, thrust force, cutting force, surface roughness, and specific cutting pressure were considered output responses. Experiments were conducted using a Taguchi L27 orthogonal array (OA). The proposed methodology integrates grey relational analysis (GRA) with fuzzy logic (FL) to obtain a grey-fuzzy reasoning grade (GFRG) for multi-response optimisation. Analysis of variance (ANOVA) was employed to identify the most influential machining parameter. The results demonstrated that the grey-fuzzy approach provided a more discriminative optimisation index than conventional grey relational analysis by reducing uncertainty in multi-response decision-making. The confirmation experiments revealed an increase in GFRG from 0.550 to 0.900, corresponding to a relative improvement of 63.64% at the optimal parameter setting. The proposed methodology demonstrates that integrating grey relational analysis with fuzzy inference provides a reliable and statistically supported approach for multi-response optimisation of dry turning parameters for Incoloy 800H.