Browse Topic: Nonconventional machining processes
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study focuses on Cupronickel and suggests creating predictive models to anticipate performance metrics in ECM through regression analysis. The experiments are formulated based on Taguchi's principles, and a multiple regression model is utilized to deduce the mathematical equations. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. The proposed prediction technique for Cupronickel is more adaptable, efficient, and accurate in comparison to current models, providing enhanced monitoring capabilities. The updated models have
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely forecast important performance metrics. Experimental trials were conducted using a WEDM system to mill Invar 36 under several machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study primarily examines the use of Haste alloy in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the statistical significance of process parameters that impact performance indicators. The proposed regression models for Haste alloy are more versatile
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design changes, decision-making in the manufacturing industry has become increasingly intricate. The preliminary intention of this work is to concentrate on Cupronickel and suggest the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the purpose of predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the target of maximizing material removal rate, minimizing surface roughness, and simultaneously achieving precise geometric tolerances. The ANFIS model suggested for Cupronickel provides more flexibility, efficiency, and accuracy compared to conventional approaches, allowing for enhanced monitoring and control in ECM operations
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
Wire Electrical Discharge Machining (WEDM) is a widely used manufacturing method that is employed to shape complex geometries in conductive materials such as cupronickel, which is highly regarded for its resistance to corrosion and ability to conduct heat. The aspiration of this investigation is to improve the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for cupronickel material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study analyzes the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness, material removal rate. Experimental trials are performed to collect data on these parameters and their corresponding machining characteristics. The TOPSIS optimization method is utilized to determine the most favourable parameter settings by evaluating each parameter combination against the ideal and
Wire Electrical Discharge Machining (WEDM) is a contemporary method that is extensively employed for intricate machining operations, especially in materials with high hardness and intricate shapes. Invar 36, a nickel-iron alloy known for its minimal change in size with temperature and consistent dimensions, poses distinct difficulties in the process of machining because of its specific properties. This study explores the process of optimizing WEDM parameters for Invar 36 material by adopting the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The study involved conducting experimental trials to analyze the influence of significant machining variables, such as pulse-on time, pulse-off time, applied current, on performance indicators such as material removal rate (MRR), surface roughness (Ra). The Taguchi based TOPSIS method was utilized to analyze the problem of multi-criteria decision-making and determine the most favorable parameter configurations
Wire Electrical Discharge Machining (WEDM) is an important method engaged to make intricate shapes in conductive materials like Cupronickel, which is well-known for its ability to resist corrosion and conduct heat. The intention of this exploration is to enhance the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for Cupronickel material by utilizing a Taguchi-based Grey Relational Analysis (GRA). The study examines the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on key machining outcomes such as surface roughness (Ra), material removal rate (MRR). A comprehensive dataset is generated for analysis through a systematic series of experiments designed using the Taguchi method. Grey relational grades are assessed to measure the connections between the input parameters and machining responses, making it easier to determine the best parameter settings. The Taguchi-based GRA approach provides a systematic approach for
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Wire Electrical Discharge Machining (WEDM) is an advanced method of machining that provides distinct benefits in machining materials with high hardness and intricate geometries. Invar 36, a nickel-iron alloy with a lower coefficient of thermal expansion, is widely used in the aerospace, automotive, and electronic industries because of its excellent dimensional stability across a broad range of temperatures. The main objectives are to optimize the machining parameters and create regression models that can accurately predict the key performance indicators. Experimental trials were performed utilizing a WEDM setup to machine Invar 36 under various machining conditions, such as pulse-on time, pulse-off time, current setting percentage (%). The machining performance was evaluated by measuring the material removal rate (MRR), surface roughness (Ra). The design of experiment method (DOE) was utilized to systematically investigate the parameter space and determine the most effective machining
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining method that is well-known for its capacity to create complex forms in conductive materials with exceptional precision. Cupronickel, a hard material consisting of copper, nickel, and additional components, is widely employed in marine, automotive, and electrical engineering fields because of its exceptional ability to resist corrosion and conduct heat. The intention of this study is to optimize the parameters of Wire Electrical Discharge Machining (WEDM) for Cupronickel material and create regression models to accurately forecast the performance of the machining process. An exploration was carried out to analyze the influence of important parameters in wire electrical discharge machining (WEDM), namely pulse-on time, pulse-off time, and applied current on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of design of experiments (DOE) enabled a systematic
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining approach that is well-known for its capability to create intricate forms in materials with high levels of hardness and intricate geometries. Invar 36, a nickel-iron alloy, is extensively utilized in industries that demand exceptional dimensional stability across a wide temperature range. The objective of this exploration is for optimizing the WEDM parameters of Invar 36 material. Additionally, a predictive model called Adaptive Neuro-Fuzzy Inference System (ANFIS) will be developed to forecast the machining performance. The study involved conducting experimental trials to analyze the influence of crucial factors in WEDM. These parameters included pulse-on time (Ton), pulse-off time (Toff), and current. The objective was to examine their influence on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of Design of Experiments (DOE) enabled a systematic
Have you ever gazed at the vastness of the stars and wondered what else your CNC machine can create? Greg Green had the opportunity to find out when he joined the staff at the Canada-France-Hawaii Telescope (CFHT) in Waimea, Hawaii.
The use of planetary gearboxes in heavy-duty industries is dominant due to their compact size, large transmission ratio and torque delivery capability with different configurations. Due to their harsh operating conditions, localised gear tooth faults such as cracking and chipping are more common in such gearboxes. Furthermore, localised gear tooth failure initiates distributed gear faults such as pitting and wear on the gear tooth. Therefore, it is necessary to monitor such localised gear faults continuously and detect them at an early stage to prevent sudden and catastrophic failure. In this study, gear tooth localised defects on various gear elements of the planetary gearbox are seeded using Electrical Discharge Machine (EDM). Then the vibration signals from the gearbox are captured. Afterwards, a decision tree algorithm selects the most prominent statistical features from many extracted features. Further, to automate the fault detection process, the k-nearest neighbours (k-NN
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. The machinability performance of Monel
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. The various
California-based 3DEO unveiled in February its new metal 3D printing platform and patented technology, Saffron. The proprietary platform has been in development for the past five years. “Until now, we have revealed very little about our patented technology, and for good reason - we felt we had a tiger by the tail and wanted to gain as much advantage as possible,” said Matt Sand, 3DEO's co-founder and president. Using a hybrid additive manufacturing (AM) process that leverages binder jetting and CNC machining, the next-generation printer achieves superior results in terms of surface finish, material properties and dimensional accuracy, Sand said. The build area is 81 sq. in. (523 sq. cm), covered by eight spindles operating at 60,000 rpm with micron-level positional accuracy. Depending on part geometry or print speed required, the printer can automatically vary layer thickness anywhere from 50 to 500 microns.
The impact of Laser Beam Machining (LBM) process parameters on Surface Roughness (SR) and kerf width during machining is investigated in this work. Stainless Steel is a material that is resistant to corrosion. LBM is a nontraditional machining method in which material is removed by melting and vaporizing metal when a laser beam collides with the metal surface. There are numerous process variables that influence the quality of the LBM-cut machined surface. However, the most essential factors are laser power, cutting speed, assist gas pressure, nozzle distance, focus length, pulse frequency, and pulse width. SR, Material Removal Rate (MRR), and kerf width and heat affected zone are significant performance indicators in LBM. The influence of LBM process parameters on SR and kerf width while machining stainless steel material is investigated in this study. Experiments are carried out using the L27 orthogonal array by varying laser power, cutting speed, and assisting gas pressure for
During aircraft wing assembly, machined fiberglass shims are often used between mating parts to compensate for inherent geometric variability due to manufacturing. At present, fiberglass shims for large aerospace structures, such as shims attached to wing ribs, are manufactured either manually or by precision machining, both of which pose a challenge due to tight tolerance requirements and wide geometric variations in the aircraft structures. Relative to articulated arm industrial robots, gantry-style computer numerical control (CNC) machines are costly, consume large footprints, and are inflexible in the application. Therefore, industrial robots are viewed as potential candidates to replace these gantry systems to facilitate metrology, shim machining, and permanent joining of aircraft structure, with all these processes taking place in the assembly process step. However, the accuracy of articulated arm robots is limited by errors in kinematic calibration, gear backlash, joint
Aluminum Metal Matrix Composite (AMMC) materials have loftier individualities and are known as an alternative material for a range of aerospace and automotive engineering applications. Reinforcement inclusion makes the components tougher, resulting in low performance of machining by traditional conservative machining practices. The present study presents a detailed review of the machinability of AMMC (Pure Aluminum + Graphene nanoplatelets) using Wire Electric Discharge Machining (WEDM). For WEDM of AMMC, a multi-objective optimization method is proposed to evaluate possible machining parameters in order to achieve better machining efficiency. Taguchi’s approach to the design of experiments is used to organize the experiments. For performing experiments, an L27 orthogonal array was selected. Five input process variables were considered for this study. The Grey Relational Analysis (GRA) is used to achieve the best features of multi-performance machining. The experimental results show
AA 2014 is a copper based aluminium alloy which is having exceptional mechanical characteristics such as better strength, ductility and lesser fatigue. AA 2014 is most generally employed in various engineering applications such as fabrication of structural components, defence applications and manufacturing of aerospace components. Also, this material possess better resistant to corrosion which makes this material best suitable for numerous engineering applications. Unconventional methods of machining have been evolved for producing intricate shapes in electrically conductive components. Wire Electrical Discharge Machining (WEDM) is one among the unconventional machining method which is used for making intricate shape on any electrically conductive work material. In this work, an experimentation has been carried out on WEDM of AA 2014 alloy, employing Taguchi’s technique. The experimental runs have been conducted by taking into account, the process variables such as pulse-on-time, pulse
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