Browse Topic: Machining processes
The final step in manufacturing high-precision parts for internal combustion engines, such as cylinder heads and blocks, is the removal of machining chips from the finished parts. This step is crucial because the machining chips and cutting oil left on the surface after machining can cause quality issues in the downstream engine assembly and affect the cooling system’s performance during engine operation. This chip removal step is especially critical for parts with internal cavities, such as the water jackets in cylinder heads, due to the difficulty of removing chips lodged in the narrow passages of these internal channels. To effectively remove chips from the water jacket, machining chip washing systems typically utilize multiple high-velocity water jets directed into the water jacket, creating flows with substantial kinetic energy to dislodge and evacuate the machining chips. For machining chip washing systems equipped with dozens of water nozzles, optimizing washing efficiency
The current ASTM A653 standard for determining the bake hardening index (BHI) of sheet metals can lead to premature fracture at the transition radius of the tensile specimen in high strength steel grades. In this study, a new test procedure to characterize the BHI was developed and applied to 980 and 1180 MPa third generation advanced high strength steels (3G-AHSS). The so-called KS-1B methodology involves pre-straining over-sized tensile specimens followed by the extraction of an ASTM E8 sample, paint baking and re-testing to determine the BHI. Various pre-strain levels in the range of 2 to 10% were considered to evaluate the KS-1B procedure with select comparisons with the ASTM A653 methodology for pre-strain levels of 2 and 8%. Finally, to characterize the influence of paint baking at large strain levels, sheared edge conical hole expansion tests were conducted. The tensile mechanical properties of the 3G steels after paint baking were observed to be sensitive to the pre-strain with
Reduction of frictional losses by changing the surface roughness in the form of surface textures has been reported as an effective method in reducing friction in the boundary regime of lubrication. Laser-based micro texturing has been mostly used to create these texture patterns and it is reported that it can reduce the frictional resistance by ~20-50%. However, the use of laser-based techniques for texture preparation led to residual thermal stress and micro cracks on the surfaces. Hence, the current study emphasizes using conventional micromachining on piston material (Al alloy Al4032) to overcome this limitation. Three variations of semi-hemispherical geometries were prepared on the surface of Al alloy with dimple depths of 15, 20 and 40 μm and dimple diameters of 90, 120 and 240 μm. Prepared textured surfaces with untextured surfaces are compared in terms of wear, wettability, and friction characteristics based on Stribeck curve behaviors. Results of this investigation demonstrated
The initial powder used for the manufacturing of NdFeB permanent magnets is usually prepared through rapid cooling, either by melt spinning or strip casting. The powders produced by these two methods are suitable for different applications: while melt-spun powder is a good initial material for bonded and hot-deformed magnets, strip-cast powder is normally used for sintered magnets. To investigate the suitability of using strip-cast powder to manufacture hot-deformed magnets, NdFeB powder prepared by strip casting was hot pressed (without particle alignment) and compared with melt-spun powder prepared under the same conditions (700 °C, 45 MPa, 90 min). Although the processing parameters are the same (pressed in the same mold), the magnetic properties of the magnets made from the two powders are significantly different. Surprisingly, the magnet made from the strip-cast powder (after ball milling) shows comparable magnetic properties to those of isotropic magnets, with coercivity (HcJ) of
The chemical milling process used in the aerospace industry generates substantial metallic residue in the etching bath, referred to as chemical milling sludge (CMS). The direct disposal of CMS into the environment leads to ecological deterioration and economic losses. This study focused on the recovery of aluminum from the aerospace industry CMS, aiming to mitigate environmental harm and enhance resource efficiency. The energy-dispersive X-ray (EDX) analysis revealed that the aluminum content in extracted CMS increased significantly to 95.86%, compared to 28.98% in non-extracted sludge. The XRD analysis of the CMS extracted samples also revealed the presence of increased Al2O3. The surface morphology study suggested the irregularly shaped particles with large chunks, and fine granules were observed on CMS. The yield of Al2O3 was observed to be 35.9% (wt) prior to the calcination process followed by 12.1% (wt) after calcination. The phytotoxicity study indicated that the CMS inhibited
Abrasive water jet (AWJ) machining is the most effective technology for processing various engineering materials particularly difficult-to-cut materials such as aluminum alloys, steels, brass, ceramics, composites, and the like. The present study focuses on the experimental study on surface roughness and kerf taper is carried out during AWJ machining of Al 6061-T6 alloy with 40 mm thickness, and the influence of process parameters includes water jet pressure, standoff distance, and abrasive flow rate on the kerf taper and surface roughness is analyzed. The number of experiments is designed using Taguchi’s L9 orthogonal array. Experimental results are statistically analyzed using ANOVA. Also gray relational analysis (GRA) coupled with principal component analysis (PCA) hybrid approach was implemented to optimize the performance parameters. From the results it is found that standoff distance and hydraulic jet pressure are the most influencing parameters on surface roughness and kerf
The experimental investigation analyzed the performance of three machining conditions: dry machining, cryogenic machining, and cryogenic machining with minimum quantity lubrication (MQL) on tool wear, cutting forces, material removal rate, and microhardness. The outcome of this study presents valuable knowledge regarding optimizing conditions of turning operations for Ti6Al4V and understanding the machinability under cryogenic-based cooling strategies. Based on the experimentation, cryogenic machining with MQL is the most beneficial approach, as it reduces cutting force and flank wear with a required material removal rate. This strategy significantly enhances the machining efficiency and quality of Ti6Al4V under variable feed rates (0.05 mm/rev, 0.1 mm/rev, 0.15 mm/rev, 0.2 mm/rev, 0.25 mm/rev) where cutting velocity (120 m/min) and depth of cut (1 mm) are constant. The effects of the main cutting force, feed force, thrust force, material removal mechanism, flank wear, and
Compared to manual driving, autonomous driving is more prone to the rapid development and deterioration of pavement distress due to the concentration of driving paths. Therefore, a reasonable and efficient maintenance strategy is required. To address the challenges posed by the numerous constraints and objectives in the maintenance strategy generation process, this paper proposes a multi-objective optimization-based method for generating pavement maintenance strategies. The approach leverages advanced pavement distress detection technologies to establish an initial maintenance program, incorporating a range of constraints and maintenance objectives, such as cost-efficiency, performance longevity, and environmental impact. The method applies a genetic algorithm (GA) to iteratively refine and optimize the maintenance strategy, ensuring that the solutions align with both immediate and long-term performance goals for autonomous vehicle operations. A case study utilizing real-world road
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 objective of this research is to develop an optimization strategy for the Electrochemical Drilling process on Nimonic alloy material, taking into account various performance factors. The optimization strategy relies on the integration of the Taguchi method with Grey Relational Analysis (GRA). Nimonic is extensively utilized in aerospace, nuclear, and marine industries, specifically in situations that are prone to corrosion. The experimental trials are structured based on Taguchi's principle and encompass three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This inquiry examines performance indicators like the rate of material removal, surface roughness, as well as geometric parameters such as overcut, shape, and orientation tolerance. Based on the investigation, it is determined that the feed rate is the primary factor that directly affects the intended performance criteria. In order to enhance the accuracy of predictions, multiple regression
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 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
In automotive applications, most of the engineering components come across the material removal process in manufacturing. Face milling is one of the prominent material removal processes wherein a multi-point cutter is used to machine the flat workpiece to bring it to its required dimension. In the material removal process, the cost of the cutting tool occupies the major part of the total manufacturing cost of a product. Also, the continuous usage of the cutting tool results in tool wear. The usage of the cutting tool after the threshold value of the tool wear deteriorates the surface finish of the workpiece which leads to product rejection. Hence, optimal tool usage is inevitable. The continuous monitoring of the cutting tool condition will ensure optimal tool usage. In the present work, four real-time tool conditions are considered, namely, fresh tool (G), tool flank wear (FW), tool flaking on rake surface (FL) and tool with broken tip (B). Vibration signals are acquired while milling
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
Surface roughness is a key factor in different machining processes and plays an important role in ergonomics, assembly process, wear and fatigue life of components. Other factors like functionality, performance and durability of parts are also affected by surface roughness. Although maintaining an optimum surface roughness is a major challenge in many manufacturing industries. Surface roughness during machining depends upon machining parameters such as tool geometry, feed rate, depth of cut, rotational speed, lubrication, tool wear, etc. Tool vibrations during machining also have significant influence in surface roughness. In this work an attempt is made to predict the surface roughness of machined components made by the turning process by using machine learning of tool vibration signals. By varying different machining parameters and keeping other tooling and material properties same, a range of surface roughness values can be obtained. For each condition, corresponding tool vibration
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
Inconel 800H superalloy is a difficult-to-turn material. This study aims to achieve optimal machining results, including reduced cutting force, improved surface roughness, and minimized residual stress, by optimizing input machining parameters like cutting speed, feed rate, spraying angle, and nozzle distance on Inconel 800H. The Taguchi L27 method is utilized for experimentation, while the Harris hawks optimizer (HHO) is applied in a multi-objective optimization model. Additionally, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is used to identify the optimal input parameters. Five distinct weight schemes were employed, including the Analytic Hierarchy Process (AHP), the Entropy weight method, Criteria Importance through Inter-Criteria Correlation (CRITIC), Grey relational analysis (GRA), and Principal Component Analysis (PCA) to determine response weights. The analysis revealed that the primary factor affecting all measured weights is the feed
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
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 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
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
The aspiration of this exploration is to evolve an optimization technique for the Electrochemical Drilling process on Haste alloy material, considering various performance factors. The Taguchi approach, along with Grey Relational Analysis (GRA), forms the basis for optimization. Haste alloy has a wider range of uses in industries such as aerospace, nuclear, and marine, especially in harsh environments. The experimental trials conducted in accordance with Taguchi's approach have utilized three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. When doing this examination, we analyze not only the rate at which material is removed and the roughness of the surface, but also other characteristics that indicate performance, such as overcut, shape, and orientation tolerance. The analytical findings indicate that the feed rate is the primary factor that directly impacts the required performance standards. Regression models are constructed to make predictions
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
This specification covers requirements for the superfinishing of High Velocity Oxygen/Fuel (HVOF) applied tungsten carbide thermal spray coatings.
This specification covers a free-machining, low-alloy steel in the form of round bars 3.50 inches (88.9 mm) and under in nominal diameter produced by a die-drawing process.
This study describes the Taguchi optimization process applied to optimize drilling parameters for glass fiber reinforced composite (GFRC) material. The machining process is analyzed in relation to process parameters using analysis of variance (ANOVA). The characteristics assessed for both the drilling and the specimen include speed, feed rate, drill size, and specimen thickness. The commercial software program MINITAB14 was used to collect and analyze the measured results. Cutting force and torque during drilling are examined in relation to these parameters using an orthogonal array and a signal-to-noise ratio. The primary goal is to identify the critical elements and combinations of elements that impact the machining process to achieve minimal cutting thrust and torque, based on the evaluation of the Taguchi technique.
This study focuses on machining automobile parts such as drive shafts and axles made of low alloy steel AISI 4140. The influence of cutting inserts geometrical parameters, viz., relief angle (RIA), rake angle (RAA), and nose radius (NA) are studied by designing experiments using Taguchi’s methodology. Numerical simulation is conducted using DEFORM-2D; a suitable L9 orthogonal array (OA) is considered for this work for varying combinations of inputs, and the resultant cutting force, maximum principal stress, and tool life are determined. Adopting a signal-to-noise (S/N) ratio minimizes the outputs for better machining conditions and achieves high-quality components with precision, tolerance, and accuracy. The ideal conditions obtained from the S/N ratio are RAA of 6°, RIA of 3°, and NR of 0.6 mm. Analysis of variance presents that the NR influences the resultant cutting force, wear depth, and work piece damage 73.51%, RAA following by 23.99%, and RIA by 2.03% achieved with a R2 value of
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) 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 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 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 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 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 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
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
The larger domain of surface texture geometry and other input variables related to engine operation, i.e., elevated temperature, has remained to be studied for finding suitable surface texture for real-time engine operations. In previous efforts to find suitable surface texture geometry and technique, the tribological performance of the piston material (Al4032) with dimples of varying diameters (90 to 240 μm) was evaluated under mixed and starved lubrication conditions in a pin-on-disk configuration. The disc was textured using a ball nose end mill cutter via conventional micromachining techniques. The area density and aspect ratio (depth to diameter) of the dimples were kept constant at 10% and 1/6, respectively. SAE 20W-40 oil was used as a lubricant with three separate drop volumes. The experiments were conducted in oscillating motion at temperatures of 50, 100 and 150°C. Conventional micromachining achieved improved dimensional precision and minimized thermal damage. Textured
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
Items per page:
1 – 50 of 1709