Manufacturing has grown challenging because of the increased usage of harder materials, such as titanium alloys, in many industries, such as aerospace, automobiles, and marine. Conventional material removal procedures are not suitable for these tough materials due to their increased hardness and slow machinability. Wire Electrical discharge machining (WEDM) is a modern approach for material removal, particularly for harder materials, such as titanium alloys, nickel alloys, hard particle reinforced metal matrix composites, etc. The research design was performed by deeming the independent factors, such as duration of pulse and applied current. The removal rate of material, surface roughness of the machined region, dimensional deviation, and tolerance errors in form/orientation are considered performance metrics. Taguchi’s approach was engaged to assess the process variables, and the importance of the process factors was established using analysis of variance approach. The purpose of this research is to create an AI based decision making tool, which can be utilized to anticipate the various parameters that impact the WEDM material removal process. The discoveries of the present exploration allowing the manufacturers to make better-informed decisions with a developed model’s capability by demonstrating that the model’s predicted values were in close confirmation to the actual values.

Superalloys are a much-needed material for abundant engineering fields, such as nuclear-powered reactor components and aeronautics. Owing to their exceptional characteristics, such as higher thermal conductivity, they can be difficult to machine using conventional processes. Modern approaches to machining have evolved to utilize these materials. One of the techniques studied in this project is electrical discharge in a wire machine. This process can help to reduce the energy consumption during machining and negative impact on the environment. In addition, shortening the operation time of the machine can help to minimize its impact on the environment. The duration of the pulse and applied current are independent factors considered in this study. Material removal rate, surface roughness, dimensional deviation, and form/orientation tolerance errors are deemed as performance measures. The goal of this investigation is to reduce the time required to machine and improve the surface finish of components by implementing a Grey-based artificial neural network model. This method is useful in foretelling the conditions of the Wire Electro Discharge Machining (WEDM) process. This paper uses the Taguchi design and Analysis of Variance (ANOVA) framework to analyze the model’s variable inputs. The overall best coefficient of correlation (R = 0.9981) is fetched with an RMSE value of 0.0086. The material removal rate has been increased by decreasing the time taken for removal, which gives the possibility of consuming minimum energy. The finishing of the machined surface also improved. Moreover, this paper shows how to use an Artificial Neural Network (ANN) model with Grey Analysis. The results of the comparative analysis show that the values envisaged are closer with the actual values. The foretelling capacity of the evolved model is confirmed with the performance analysis of the developed model.

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.