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Nickel-based superalloys are most commonly engaged in a numerous engineering use, including the making of food processing equipment, aerospace components, and chemical processing equipment. These materials are often regarded as difficult-to-machine materials in conventional machining approach due to their higher strength and thermal conductivity. Various methods for more effective machining of hard materials such as nickel-based superalloys have been developed. Wire electrical discharge machining is one of them. In this paper, an effect has been taken to develop an adaptive neuro-fuzzy inference system for predicting WEDM performance in the future. To analyse the model’s variable input, the paper employs the Taguchi’s design and analysis techniques. The evolved ANFIS model aims to simulate the process’s various characteristics and predicted values. A comparison of the two was then made, and it was discovered that the predicted values are much closer to the actual outcomes.

This paper focuses on a low-cost simulation of a control device that automates the operation of an existing suspension test rig. The rig has a few limitations: it must be manually controlled, the load applied cannot be specified, and the deflection must be manually measured. A suspension setup can't be checked for different road profiles, either. The proposed control system in this paper effectively automates the process of suspension spring load testing at a cost that is comparable to that of a fully automated test rig on the market, while also expanding the scope of its capabilities. SIMSCAPE was used to map simulation models of both the actual test rig and the updated test rig control system. On both rigs, the results of evaluating suspension components were simulated, and the resulting graphs were compared.

Due to their inherent properties and superior performance over titanium-based materials, nickel-based superalloys are widely utilized in the manufacturing industry. Monel 400 is among them. This nickel-copper alloy possesses exceptional corrosion resistance and mechanical properties. Monel 400 is primarily utilized in the chemical industry, heat exchangers, and turbine component manufacturing. Due to the properties of Monel 400, it is deemed as hard to machine materials with the aid of conventional methods. For investigating the performance of this process, a three-level analysis was carried out. Pulse on duration and applied current at three levels are the independent parameters used for designing the experiments. In this present article, a single-response analysis technique is used which is known as Taguchi to investigate the impact of the various process parameters on the output variables.

Monel 400, a type of nickel alloy which is adopted in numerous engineering fields, such as high-temperature devices. Owing to its better strength and thermal diffusion, it can be difficult to machine with conventional methods. In order to avoid the disadvantages of conventional methods, various advanced material removal techniques have been developed. One of these is Wire Electro Discharge Machining (WEDM). This process is an evolution of the electrical discharge method. In the process of WEDM, difficult materials with intricate forms are usually machined. In this study, the performance of this method on Monel 400 has been analyzed. The three independent variables that are considered when it comes to analyzing the performance of this process are the pulse on, the applied current, and the pulse off. The experiments were performed using the design approach of Taguchi, which involves using an L27 orthogonal array.

Nickel-based superalloys are frequently adopted in various engineering applications, such as the production of food processing equipment, aerospace parts, and chemical processing equipment. Because of higher strength and thermal conductivity, they are often regarded as difficult-to-machine materials in certain processes. Various methods were evolved for machining the hard materials such as Nickel-based superalloys more effective. One of these is wire electrical discharge machining. In this paper, we will discuss the development of an artificial neural network model and an adaptive neuro-fuzzy inference system that can be used to predict the future performance of Wire Electrical Discharge Machining (WEDM). The paper uses the Taguchi and Analysis of Variance (ANOVA) design techniques to analyze the model’s variable input. It aims to simulate the various characteristics of the process and its predicted values.

A wide range of engineering domains, such as aeronautical, automobiles, and marine, rely on the use of Metal Matrix Composites (MMC). Due to the excellent properties, such as hardness and strength, Aluminum base MMC are generally adopted in various uses. Due to the increasing number of reinforcement materials being added to the MMC, its properties are expected to improve. In this exploratory analysis, an effort was given to develop a new aluminium-based MMC. The analysis of the machinability of the composite was also performed. The process of creating a new MMC using a stir casting technique was carried out. It resulted in a better and more reinforced composite than its base materials. The reinforcement materials were fabricated using different weight combinations and process parameters, such as the temperature and duration required to stir. Due to the improved properties of the composite, the traditional machining method is not feasible for machining of these materials.