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Due to their various features, aluminum alloy can be used in various applications. These include aerospace, automotive, and electrical and thermal applications. Compared to structural steels, aluminum offers superior corrosion resistance and specific strength. Aluminum alloy known as AA 2014 exhibits various mechanical properties. These include its improved strength and weight ratio. It can also be used in military and aircraft applications. Aluminum alloy is commonly used in various engineering applications, such as the manufacturing of structures and aircraft components. Due to its corrosion resistance, it can be utilized in severe environmental environments. Different methods are used in unconventional ways to generate complicated forms of electrical components. One of these is wire electro-discharge machine (WEDM). This process involves making intricate shapes out of conductive materials.

Superalloys, also known as nickel alloys, are widely employed in a wide variety of engineering applications, including the creation of parts for the chemical processing industry and appliances for the food processing industry. Their high heat conductivity and strength, among other characteristics, make them challenging to machine using traditional techniques. Instead, cutting-edge techniques are typically created for the milling of such tougher materials. In this study, we use a modern method called wire electrical discharge machining, which is typically used for working with tougher materials. In order to anticipate WEDM variables, this paper aims to create a Grey-based Artificial Neural Network (ANN) Model and Adaptive Neuro Fuzzy Inference System. The paper uses a Taguchi method to investigate the model’s varying inputs. The purpose of this model is to visualize the process’s varying performance characteristics.

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.

In view of the improvements in manufacturing sectors, which are an important component of any economy’s growth, there is a significant need for new and advanced materials, particularly alloy materials need to be analysed and investigated so that new technologies may be effectively utilized. Materials with low weight and high strength, such as aluminium alloys, are recommended for a variety of applications that require both strength and corrosion resistance, such as marine applications and high-temperature applications. Aluminium alloy Al 5052, a nonferrous material with outstanding properties is an Al-Mg alloy with high thermal conductivity and corrosion products that are non-toxic. Minimum Quantity Lubrication (MQL) is a cost-effective and environmentally friendly method of lubrication employed in a variety of machining processes. The investigation of CNC milling of AA5052 alloy with standard Tungsten Carbide (WC) tool inserts with MQL settings are detailed in this paper.

With the progress of manufacturing industries being critical for economic development, there is a significant requirement to explore and scrutinize advanced materials, particularly alloy materials, to facilitate the efficient utilization of modern technologies. Lightweight and high-strength materials, such as aluminium alloys, are extensively suggested for various applications requiring strength and corrosion resistance, including but not limited to automotive, marine, and high-temperature applications. As a result, there is a significant necessity to examine and evaluate these materials to promote their effective use in the manufacturing sectors. This research paper presents the development of an Artificial Neural Network (ANN) model for Computer Numerical Control (CNC) drilling of AA6061 aluminium alloy with a coated textured tool. The primary aim of the study is to optimize the drilling process and enhance the machinability of the material.

Laser Beam Welding (LBW) is one of the advanced methods of joining metals by fusion. The LBW process exhibits comparatively better welding performance than conventional processes and this method of welding approach is exclusively employed in higher volume applications such as automotive industries. One of the most common nickel alloys used in various engineering fields is Inconel 718. This material has high strength and corrosion resistance properties, and is commonly used in high-temperature applications, such as gas turbines and rocket engines. In this study, we aim to develop an artificial intelligence tool that can analyze the influence of various process variables on the design and performance of a metal. The experiments were planned using the design approach of Taguchi. An L27 orthogonal array was used for the experiments. The three performance measures are the top width, bottom width, and penetration.

The numerous applications and desirable attributes of Monel 400 urge many researchers to undertake multiple systematic evaluation studies for diverse manufacturing operations. Because of their exceptional mechanical qualities and great corrosion resistance, nickel-based alloys, particularly Monel 400, are increasing in popularity in a variety of applications. Because of their tendency for rapid work hardening and low thermal conductivity, these materials are particularly difficult to machine using traditional manufacturing techniques. Advanced material removal methodologies have been applied to eliminate such drawbacks and are regarded as a suitable alternative approach to traditional machining processes. Based on the Electrical Discharge Machining technique, Wire Electrical Discharge Machining was developed, which a sophisticated machining technology is used to machine hard materials with complex forms in any electrically conducting materials.

Inconel 718 is a superalloy made from nickel that has exceptional mechanical properties. It has been widely used in the manufacturing of various components such as nuclear and aerospace aircraft. Due to its exceptional corrosion resistance, this material can be utilized in various environments. Due to the increasing number of challenges that come with conventional methods of welding, the use of advanced techniques has been developed to produce better and sound quality joints. One of these is Laser Beam Welding (LBW) technique. This method utilizes a high-intensity beam to create a better and more quality weld joints with improved mechanical properties. This study aims to develop multiple regression models that can be used to analyze the performance of laser beam welding on Inconel 718 alloy joints.

In addition to traditional methods, there are also non-traditional techniques that can be used to overcome the challenges of conventional metal working. One such technique is wire electrical discharge (WEDM). This type of advanced manufacturing process involves making complex shapes using materials. Utilizing intelligent tools can help a company meet its goals. Nickel is a hard metal to machine for various applications such as nuclear, automobile and aerospace. Due its high thermal conductivity and strength, traditional methods are not ideal when it comes to producing components using this material. This paper aims to provide a comprehensive analysis of the various steps in the development of a neural network model for the manufacturing of Inconel 625 alloy which is used for specific applications such as exhaust couplings in sports motor vehicle engines. The study was conducted using a combination of computational and experimental methods.

Magnesium alloy, known for its high strength and lightweight properties, finds widespread utilization in various technical applications. Aerospace applications, such as fuselages and steering columns, are well-suited for their utilization. These materials are frequently employed in automotive components, such as steering wheels and fuel tank lids, due to their notable corrosion resistance. The performance of magnesium alloy components remains unimproved by normal manufacturing methods due to the inherent characteristics of the material. This work introduces a contemporary approach to fabricating complex geometries through the utilization of Wire-Electro Discharge Machining (WEDM). The material utilized in this study was magnesium alloy. The investigation also considered the input parameters associated with the Wire Electrical Discharge Machining (WEDM) process, specifically the pulse duration and peak current.

Modern automobile applications such as petrol, diesel, and gaseous fuel injection system use dissimilar Inconel 718 (IN718) and Stainless Steel 304 (SS 304) joints. IN 718 is a precipitation-hardened austenitic nickel-based superalloy with exceptional qualities such as high strength, resistance to corrosion, greater toughness, as well as resistance to thermal induced fatigue at elevated temperatures (between 150 and 1500oC), while SS 304 is a T 300 Series austenitic stainless steel alloy that can be used successfully in wide range of applications due to greater resistance to corrosion, good high and low temperature strength and ductility with excellent weld ability and formability. To get a better understanding of the mechanical characteristics of these heterogeneous weldments, these alloy joints were created using laser beam welding, one of the most modern joining techniques for high-strength materials.

Titanium alloys are deemed as one amongst the light weight material most preferably adopted in numerous engineering applications due to its exceptional features such as corrosive resistance and thermal strength. These alloys are predominantly used in components of IC engines such as valves and springs, connecting rods. Especially Ti-Grade 5 adopted in aircraft, automobile parts ski plates and bicycles. The preliminary goal of this present research is to optimize the machining variables for Wire Electrical Discharge Machining (WEDM) of Ti-6Al-4V (Grade 5) to accomplish improved rate of material removal and surface finish. Taguchi’s design and analysis method was chosen for devising and examining the experiments by considering input factors (pulse duration and current). An L9 OA was utilized for experimentation to analyze the various output variables, such as surface finish and material removal rate, using the response analysis of Taguchi.

Among the challenging materials used in high-temperature applications is Inconel 625. Due to its low thermal coefficient and greater strength, traditional methods tend to produce poor results when it comes to turning Inconel 625. In order to overcome these issues, a new approach has been proposed that utilizes unconventional techniques. WEDM is a variant of the electrical discharge manufacturing process that is commonly used in the production of complex components. It is mainly utilized for the hard to machine parts. A study on the process parameters of WEDM for the machining of Inconel 625 was performed by utilizing the analysis of Taguchi. The study focused on the various parameters of the process, such as peak current, pulse on time, and off time. The performance measures that were considered in this study included surface roughness and material removal rate. The results of the analysis revealed that the various process variables affected the performance indicators.

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.

The advanced lifestyle demands materials that are light and robust, and aluminum and its alloys are commonly used in various engineering components due to their exceptional properties such as light weight, enhanced strength, and being economically affordable. Due to their superior mechanical properties, such as strength and flexibility, are commonly used in various industrial applications. Metal Matrix Composites (MMCs) are very essential materials used in several applications as they are more robust and harder than any conventional material. In this study, a metal matrix composite made of aluminum and Boron Nitride (BN) is investigated to analyze its various properties. The study is performed by using Wire Electrical Discharge Machining (WEDM). The three independent parameters of the composite are its pulse on time, peak current, and pulse off time. The study aims to analyze the effects of various process variables on the desired performance of the metal matrix composite.

Over the last decade the utilization of laser sources has seen a marked increase with its reducing expenses and increasing productivity. Enabling technologies such as better process knowledge, better laser sources and systems, and on-going advances in Laser Beam Welding (LBW) processing technologies have all contributed to these accomplishments which include both macro and micro component fabrication through LBW. There are various existing applications that benefit from using challenging materials together, hence integrating dissimilar metals allows us to gain their benefits at a higher level and can be applied extensively for multiple applications. Metals with different mechanical and microstructural qualities and features such as high corrosion resistance and low specific weight are commonly chosen to fabricate dissimilar joints.

Wire Electrical Discharge Machining (WEDM) is a variant of the electrical discharge machining (EDM) process, which represents an innovative method for the removal of material from a workpiece. The aforementioned process is frequently employed for the machining of harder materials that possess intricate geometries. Titanium alloys are a class of lightweight materials that find extensive utilization in many technical applications. Titanium Grade-5 is a titanium-based alloy that exhibits enhanced mechanical strength and improved resistance to corrosion. The objective of this exploratory analysis is to establish empirical correlations between the selected input variables, namely ‘Pulse on,’ ‘Pulse off,’ and peak current, and the desired output measures, which are material removal rate and surface roughness. The experimental design employed the Taguchi method to effectively organize the combination of tests by considering input factors.

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.

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.

One of the most common types of lightweight materials used in aerospace is magnesium alloy. It has a high strength-to-weight ratio and is ideal for various applications. Due to its corrosion resistance, it is commonly used to manufacture of fuselages. Unfortunately, the conventional methods of metal cutting fail to improve the performance of magnesium alloy. One amongst the most common methods used for making intricate shapes in harder materials is through Wire-Electro-Discharge (WEDM). In this study, we have used magnesium alloy as the work material. The independent factors were selected as pulse duration and peak current. The output parameters of the process are the Surface Roughness (SR) and the Material Removal Rate (MRR). Through a single aspect optimization technique, Taguchi was able to identify the optimal combination that would improve the effectiveness of the WEDM process.