Browse Topic: Nonconventional machining processes
Electrical discharge machining (EDM) technology is one of the unconventional machining processes with an ability to machine intricate geometrics with micro finishing. Powder-mixed EDM (PMEDM) extends the EDM process by adding conductive powder to the dielectric fluid to improve performance. This set of experiments summarizes the effect of brass and copper electrode on HcHcr D2 tool steel in chromium powder-mixed dielectric fluid. Powder concentration (PC), peak current (I), and pulse on-time (Ton) are considered as variable process parameters. General full factorial design of experiment (DOE) and ANOVA has been used to plan and analyze the experiments where powder concentration is observed as the most significant process parameter. The results also reveal that a brass electrode offers a high material removal rate (MRR). Whereas, the copper electrode has reported noteworthy improvement in surface roughness (Ra). Moreover, teaching–learning-based optimization (TLBO) algorithm has been
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
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. With the rising demand for superior products and the necessity for quick design modifications, decision-making in the industrial sector becomes increasingly complex. This study specifically examines Titanium Grade 7 and suggests creating prediction models through regression analysis to estimate performance measurements in ECM. The experiments are formulated based on Taguchi's ideas, utilizing a multiple regression approach to deduce mathematical equations. The Taguchi method is utilized for single-objective optimization in order to determine the ideal combination of process parameters that will maximize the material removal rate. ANOVA is a statistical method used to determine the relevance of process factors that affect performance measures. The suggested prediction technique for Titanium Grade 7 exhibits
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
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
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 adjustments, decision-making in the manufacturing industry has grown increasingly intricate. This study specifically examines Titanium Grade 7 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modelling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 7 provides more flexibility, efficiency, and accuracy in
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
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 19 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 19 provides more flexibility, efficiency, and accuracy in comparison to
Electrochemical machining (ECM) is a remarkably effective technique for producing detailed designs in materials that can conduct electricity, regardless of their level of hardness. As the desire for high-quality products and the necessity for rapid design changes grow, decision-making in the industrial sector becomes increasingly intricate. This work focuses on Titanium Grade 19 and proposes the development of prediction models using regression analysis to estimate performance measurements in ECM. The experiments are designed using Taguchi's methodology, employing a multiple regression approach to produce mathematical equations. The Taguchi technique is utilized for the purpose of single-objective optimization in order to determine the optimal combination of process parameters that will optimize the rate at which material is removed. ANOVA is a statistical method used to assess the relevance of process factors that impact performance indicators. The suggested prediction technique for
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
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. Due to the growing need for superior products and the requirement for quick design adjustments, decision-making in production has become more complex. This study focuses on Titanium Grade 7 and suggests creating predictive models utilizing a Taguchi-grey technique to achieve multi-objective optimization in ECM. The trials are structured based on Taguchi's principles, utilizing Taguchi-grey relational analysis (GRA) to simultaneously maximize several performance indicators. This entails optimizing the pace at which material is removed, decreasing the roughness of the surface, and attaining precise geometric tolerances. ANOVA is used to assess the relevance of process variables that affect these measures. The suggested predictive technique for Titanium Grade 7 outperforms current models in terms of flexibility
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 electrically conductive materials, irrespective of their hardness. Due to the growing need for superior products and quick design adjustments, decision-making in production has become increasingly complex. This study focuses on Titanium Grade 19 and proposes creating predictive models using a Taguchi-grey technique to achieve multi-objective optimization in ECM. The experiments are structured based on Taguchi's principles, utilizing Taguchi-grey relational analysis (GRA) to simultaneously optimize several performance indicators, including the material removal rate, surface roughness, and geometric tolerances. ANOVA is employed to assess the significance of process variables affecting these measures. The proposed predictive technique for Titanium Grade 19 outperforms current models in terms of flexibility, efficiency, and accuracy, providing enhanced capabilities for monitoring and control
These days, aluminum and other material composites are indispensable for a wide range of engineering applications, including automotive-related ones. The machinability investigations of hybrid metal matrix composites (HMMC) made of Al 6061 are reported in this paper. Graphene nanoparticles (GNp) and boron carbide were used to reinforce Al6061 alloy for the experiment. Stir casting was used to create the hybrid composite under the right circumstances. Since HMMC is not easy to machine using conventional machining procedures, the advanced method of electrical discharge machining (EDM) was used. EDM machinability studies were carried out on stir-casted Al-B4C-GNP composite materials to examine the effects of wire EDM machining variables, including current, pulse on, and pulse off, on surface roughness and material removal rate. Taguchi based Desirability function Analysis was used to optimize the EDM process parameters for maximization of the material removal rate (MRR) and minimization
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
The process of electrochemical machining, often known as ECM, is capable of effectively shaping complicated structures in materials that conduct electricity, independent of the materials' level of hardness hence especially used for automobile and aerospace applications. As a result of the demand for high-quality products and the desire for rapid design changes, the manner in which decisions are made in the manufacturing industry has become increasingly contentious. With the assistance of regression analysis, this study proposes the development of predictive models for the purpose of forecasting the performance measures in electrochemical machining of Nimonic alloy. The trials are designed in accordance with Taguchi's principles, and a multiple regression model is utilized in order to derive the mathematical equations. Taguchi's method can be applied as a methodology for single objective optimization in order to attain the most optimal combination of process parameters for the purpose
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. With the growing demand for superior products and the increasing necessity for quick design modifications, decision-making in the manufacturing industry becomes increasingly complex. The primary objective of this work is to concentrate on Cupronickel and suggest the creation of predictive models through the utilization of a Taguchi-grey technique for the purpose of multi-objective optimization in ECM. The trials follow Taguchi’s principles and utilize a Taguchi-grey relational analysis (GRA) technique to maximize numerous performance indicators concurrently. This involves optimizing the pace at which material is removed while decreasing the roughness of the surface and obtaining precise geometric tolerances. ANOVA is a statistical method used to determine the importance of process factors that influence these
The Material Removal Rate (MRR) is a vital aspect of Electro-Chemical Machining (ECM), an engineering manufacturing method that depends on electrochemical reactions. The MRR is dependent on factors such as current, voltage, electrolyte concentration, and machining time. To investigate the effect of MRR on Inconel 718 super-alloy, experiments were conducted using stainless steel tool under different independent machining conditions. Machine Learning (ML) approaches could be utilized to predict machining outcomes based on specific input parameters. In this research, ML techniques were applied to ECM by developing models using multiple linear regression, Random Forest, K-Nearest Neighbors (KNN), and Xtreme gradient boosting algorithms. These models aimed to establish the association among the collaborative impacts of the electrolytic solution, volts, amps, and feed rate on MRR. Additionally, the study seeks to recognize the best ML technique for forecasting the MRR of Inconel 718 alloy
In the highly demanding domain of advanced technologies, Wire Electro Discharge Machining (EDM) has distinguished itself as one of the most promising methods for the efficient machining of sophisticated composite materials. As a critical advanced machining process, EDM caters to the stringent requirements for intricate geometries and effective material removal. This study focuses on Al6063 Alloy Composites reinforced with Silicon Carbide and Fly Ash, materials celebrated for their high strength, exceptional oxidation-corrosion resistance, and high-temperature performance. These composites are widely applied across aerospace, marine, automotive industries, nuclear power, and oilfield sectors. The current research involves a rigorous experimental analysis and parametric optimization of the aluminum matrix composite utilizing EDM. The primary objective is to fine-tune the process parameters, including pulse-off time, current, and taper angle. The experiments were designed and conducted
The advantages of magnesium alloy composites over traditional engineering materials include their high strength and lightweight for automotive applications. The proposed work is to compose the AZ61 alloy composite configured with 0–12% silicon nitride (Si3N4) via semisolid-state stir processing assisted with a (sulfur hexafluoride—SF6) inert environment. The prepared AZ61 alloy and AZ61/4% Si3N4, AZ61/8% Si3N4, and AZ61/12% Si3N4 are machined by electrical discharge machining (EDM) under varied source parameters such as pulse On/Off (Ton/Toff ) time (100–115/30–45 μs), and composition of composite. The impact of EDM source parameters on metal removal rate (MRR) and surface roughness (Ra) is measured. For finding the optimum source for higher MRR and good surface quality of EDM surface, the ANOVA optimization tool with L16 design is executed and analyzed via a general linear model approach. With the influence of ANOVA, the Ton/Toff and composite composition found 95.42%/1.27% and 0.36
Wire Electrical Discharge Machining (WEDM) has attracted considerable attention in contemporary manufacturing because of its capacity to accurately form conductive materials. This study aims to optimize the parameters of Wire Electrical Discharge Machining (WEDM) for SAE 1010 material, which is a commonly used low-carbon steel. The Taguchi-based Grey Relational Approach (GRA) is employed for this purpose. The goal is to optimize machining efficiency and quality while minimizing production costs. The research methodology combines the Taguchi method for experimental design with the GRA for multi-response optimization. The Taguchi L27 orthogonal array is utilized to carry out experiments, taking into account three controllable factors: pulse-on time, pulse-off time, and discharge current. In addition, the performance characteristics to be optimized include surface roughness (Ra) and material removal rate (MRR). The experimental results are analyzed using the GRA (Grey Relational Analysis
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 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
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 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 adaptable machining process that is extensively employed across various engineering industries to achieve precise machining of conductive materials. SAE 1010, a steel with low carbon content, is widely used in automotive, aerospace, and machinery components because it can be welded easily and shaped effectively. The aspiration of this study is to optimize the parameters of WEDM for SAE 1010 material by employing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. An empirical study was carried out to examine the impact of crucial machining variables, such as pulse-on time, pulse-off time, applied current on performance metrics of machining, such as material removal rate (MRR), surface roughness (Ra) and Overcut. The utilization of the design of experiments (DOE) methodology enabled the methodical investigation of the parameter space. Taguchi based TOPSIS provides a comprehensive approach to parameter
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 now a crucial technique for shaping complex shapes in conductive materials such as SAE 1010 steel. This study aims to enhance machining efficiency and accuracy by developing regression analysis to model and optimize WEDM parameters for SAE 1010 material. The study aims to examine the impact of various parameters in WEDM, such as pulse-on time, pulse-off time, and discharge current, on key machining responses, including surface roughness (Ra), material removal rate (MRR). Experimental investigations are being carried out to achieve this objective. A set of Wire Electrical Discharge Machining (WEDM) experiments are conducted using a factorial design, resulting in a dataset that can be used for regression modeling. Subsequently, regression models are constructed to forecast machining responses using input parameters. The models are improved through statistical analysis, to evaluate the importance of each parameter. The regression equations
Related to traditional engineering materials, magnesium alloy-based composites have the potential for automobile applications and exhibit superior specific mechanical behavior. This study aims to synthesize the magnesium alloy (AZ61) composite configured with 0 wt%, 4 wt%, 8 wt%, and 12 wt% of silicon nitride micron particles, developed through a two-step stir-casting process under an argon environment. The synthesized cast AZ61 alloy matrix and its alloy embedded with 4 wt%, 8 wt%, and 12 wt% of Si3N4 are subjected to an abrasive water jet drilling/machining (AJWM) process under varied input sources such as the diameter of the drill (D), transverse speed rate (v), and composition of AZ61 composite sample. Influences of AJWM input sources on metal removal rate (MRR) and surface roughness (Ra) are calculated for identifying the optimum input source factors to attain the best output responses like maximum MRR and minimum Ra via analysis of variant (ANOVA) Taguchi route with L16 design
This specification provides processing and acceptance requirements for electrical discharge machining (EDM) when applied to the manufacturing of parts.
The EN24 and EN42 materials were machined by the electric discharge machine (EDM). The study aimed to optimize the input variables for the multiple outputs, such as metal removal rate (MRR), tool wear rate (TWR), and surface roughness. The machining of the metal is essential to analyze the surface quality and the production rate. The MRR is a prediction of the production rate and surface roughness resembling the quality of the surface. The input variables were current (A), pulse on time (ton), and pulse duty factor (T). The three levels of current were 3A, 6A, and 9A. The ton time was selected as 30 μs, 50 μs, and 70 μs. The pulse duty factors were selected as 4, 5, and 6. The Taguchi optimization techniques are used to optimize process parameters. The L9 orthogonal array was selected for the process. ANOVA analysis was employed to check the rank of the input parameters relative to the output. The maximum MRR were at 9A, 70 μs, and 4 duty factor for the EN24. The best MRR were at 9A
This specification covers the engineering requirements for laser beam machining, such as cutting and drilling.
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.
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. The ANN model utilizes spindle speed, feed
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. Wire Electro
Electrical steel, also known as silicon steel, is a ferromagnetic material that is often used in electric vehicles (EVs) for stator and rotor applications. Since the design and manufacturing of rotors require the use of laminated thin electrical steel sheets, the fatigue characterization of these single sheets is of interest. In this study, a 0.27mm thick non-oriented electrical steel sheet was tested under cyclic loading in the load-controlled mode with the load ratio R = 0.1 at room temperature. The specimens were prepared using the Computer Numerical Control (CNC) machining method. The Smith-Watson-Topper mean stress correction was used to find the equivalent fully reversed stress-life (S-N) curve. The Basquin equation was used to describe the fatigue strength of the electrical steel and the fatigue parameters were extracted. Furthermore, a design curve with a reliability of 90% and a confidence level of 90% was generated using Owen’s Tolerance Limit method. The fracture mechanisms
Strict environmental regulations are driving the automotive industry toward electric vehicles as they offer zero emissions. A key component in electric vehicles is the electric motor, where the stator and rotor are manufactured from stacks of thin electrical steel sheets. The electrical steel sheets can be cut in different ways, and the cutting methods may significantly affect the fatigue strength of the component. It is important to understand the effect of the cutting processes on the fatigue properties of electrical steel to ensure there is no premature failure of the electric motor resulting from an improper cutting process. This investigation compared the effect of three different edge preparation methods (stamping, CNC machining, and waterjet cutting) on the fatigue performance of 0.27mm thick electrical steel sheets. To investigate the effect of the edge finish on fatigue behavior, surface roughness was measured for these different samples. It was determined that the CNC
A comprehensive literature review of the optimization techniques used for the process parameter optimization of Abrasive Jet Machining (AJM), Ultrasonic Machining (USM), Laser Beam Machining (LBM), Electrochemical Machining (ECM), and Plasma Arc Machining (PAM) are presented in this review article. This review article is an extension of the review work carried out by previous researchers for the process parameter optimization of non-traditional machining processes using various advanced optimization algorithms. The review period considered for the same is from 2012 to 2022. The prime motive of this review article is to find out the sanguine effects of various optimization techniques used for the optimization of various considered objectives of selected non-traditional machining processes in addition to deemed materials and foremost process parameters. It is found that most of the researchers have more inclination towards the minimization of Surface Roughness (SR) compared to the
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 research aims to optimize the surface roughness, material removal rate (MRR), tool wear, and spark gap for input machining parameters such as Pulse on-off time and wire feed rate. The experiment results of WEDM of Duplex stainless steel are optimized by ANOVA and Response surface methodology (RSM) approach. Taguchi’s orthogonal array L9 (3*3) was used to design the test condition for the experiment. After the model validation, ANOVA was used to identify the most significant input factor on the output. Response surface methodology was used to find the ideal cutting conditions which produce the best-desired output in terms of less tool wear, lower surface roughness, lower spark gap, and higher material removal rate. The optimal MRR, Spark Gap, surface roughness, and tool wear parameters for Duplex Stainless Steel are obtained at Pulse on 110.23, Pulse off time of 56.0, and a wire feed rate of 1.0. The proposed RSM model is significant and suited for all machining conditions due to
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
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. Through
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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