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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.

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.

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.

Aluminium alloys are attracting importance in various engineering industries because of their exceptional characteristics such as strength, resistance to oxidation etc., AA5052 is an alloy that categorized under Al-Mg series, commonly adopted in anti-rust applications, especially for desalination applications because of its good corrosion resistance in seawater at temperatures up to 125°C, low cost, good thermal conductivity, and non-toxicity of its corrosion products. Minimum Quantity Lubrication (MQL) is one of the approaches that are economically affordable and also eco-friendly used in various machining operations. This present exploration details the investigation CNC turning of AA5052 alloy with conventional Tungsten Carbide (WC) tool inserts under MQL conditions. There are two different natural cutting fluids were engaged such as live oil and coconut oil.

The alloy investigated in this research is Precipitation Hardened Stainless Steel (PHSS) 15-5, which provides good corrosion, high strength and hardness. 15-5 Stainless Steel is extensively employed in a variety of applications, including aero plane components, high-pressure corrosive environments that include valves, fasteners, shafts, fittings and gears. In this current exploration, an analysis of the machinability of PHSS is analyzed with textured inserts and the outcomes as compared to conventional inserts. To increase the machinability conditions, two distinct types of textures were produced on the rake face of the tool inserts and employed for this machining procedure utilizing a Wire Electric Discharge Machine (WEDM).The dimensions of the textures were cut on the trial-and-error method. Three different machining parameters with three different levels were chosen. Cutting Speed, feed rate and depth of cut were chosen as the input parameters.

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.

Precipitation Hardened Stainless Steel (PHSS) is one of the martensitic steels that possess exceptional strength and corrosion resistance. Because of its characteristics, this PHSS is exclusively adopted in numerous engineering uses such as nuclear, chemical and marine industries. Welding is one of the important methods of joining that helps to make weldments with better performance characteristics. Corrosion behaviour is one of the important characteristics that contribute hugely to marine and other corrosion-related environments and also this is the most common problem for most of the manufacturing industries. The goal of this study was to analyze the PHSS weldments’ corrosive behavior and compare it with that of the two commonly used welding processes, namely MIG and TIG. The corrosive properties of the weldments were evaluated using various mediums, such as nitric acid, ferric chloride, and Oxalic acid.

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.

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.

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.

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.

SS304 is a type of stainless steel that is well-known for its high ductility and resistance to corrosion; as a result, it is typically utilized in a variety of applications, such as the exhaust systems of automobiles and the springs that are used in seatbelts. Because of its qualities, it will eventually be employed in a variety of body parts, including fuel tanks and chassis, among other things. Due to its properties, SS304 is known to be incredibly difficult to machine using conventional methods. Through a wire electrical discharge machining process, it is easier to cut complex materials with high surface finishes. In this study, a study was conducted on the WEDM process parameters of SS304 to optimize its machining process. The study was carried out using the DoE approach, which involved planning the various experiments. The parameters of the process, such as the pulse on time, peak current, and off time, were analyzed to determine their performance.

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.

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.

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.

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.

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.

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.

Considering the advancements in manufacturing industries, which are crucial for economic growth, there is a substantial demand for exploration and analysis of advanced materials, especially alloy materials, to enable efficient utilization of new technologies. Lightweight and high-strength materials, like aluminium alloys, are highly recommended for various applications that necessitate both strength and resistance to corrosion, such as automobile, marine and high-temperature applications. Therefore, there is a significant need to investigate and analyse these materials to facilitate their effective application in manufacturing sectors. This study investigates the machinability of drilling AA6061 using a micro-textured tool and proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) model for investigating the machinability of drilling AA6061 aluminium alloy with a micro-textured uncoated tool.