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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.

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.

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.

Stainless Steel 304 (SS304) is a nickel–chromium–based alloy that is regularly used in valves, refrigeration components, evaporators, and cryogenic containers due to its greater corrosion resistance, high ductility, and non-magnetic properties, as well as good weldability and formability. Multiple regression analysis was used to establish empirical relationships between process variables. Additionally, the established regression equations are employed to predict and compare experimental data. Due to the increasing demands for high-quality surface finishes and complex geometries, traditional methods are being replaced by non-conventional techniques such as wire EDM. This process, which emerged from the electrical discharge machining concept, mainly involves creating intricate components. WEDM results in a high degree of precision and excellent surface quality. Due to the complexity of WEDM, the processing parameters cannot be selected by using the trial-and-error method.

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.

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.

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.

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.

This paper examines the concurrent scheduling of machines and tools with machines in a multi-machine flexible manufacturing system (FMS) with the aim of minimizing the makespan in automobile manufacturing industry. Due to the high cost of tools in FMS, each type of tool has only one duplicate in circulation. To reduce the cost of duplicating tools on each machine, a central tool magazine (CTM) is used to store and share tools among several machines. The main challenge in this scenario is to allocate machines from alternate machines and tools to job operations in a way that minimizes the make span. To address this problem, the article proposes a mixed nonlinear integer programming formulation and a Flower Pollination Algorithm (FPA). The results show that the FPA outperforms existing algorithms and using alternate machines for operations can reduce the make span.

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.