Maximum Material Removal Rate Research Articles

Processing of Al/SiC/Gr Hybrid Composite on EDM by Different Electrode Materials Using RSM-COPRAS Approach

The present research used the stir-casting method to develop an Al-based composite. The developed composite exhibited challenges while being processed on conventional machining. Thus, a non-traditional machining process was opted to process the composite. The machining variables selected for the current research were the pulse off time (Toff), pulse on time (Ton), servo voltage (SV), current (I), and tool electrode. Three tool electrodes (SS-304, copper, and brass) were used to process the developed composite (Al/SiC/Gr). The experimental plan was designed using response surface methodology (RSM). The output responses recorded for the analysis were the material removal rate (MRR) and tool wear rate (TWR). The obtained data was optimized using complex proportional assessment (COPRAS) and machine learning methods. The optimized settings predicted by the RSM–COPRAS method were Ton: 60 µs; Toff: 60 µs; SV: 7 V; I: 12 A; and tool: brass. The maximum MRR and TWR at the suggested settings were 1.11 g/s and 0.0114 g/s, respectively. A morphological investigation of the machined surface and tool surface was conducted with scanning electron microscopy. The morphological examination of the surface (machined) presented the presence of cracks, lumps, etc.

Experimental and numerical investigations on plain turning of AA 8011 alloy using grey relational analysis

The current study investigates the effect of major process parameters on the plain turning process, such as Speed (S), feed (F), and depth of cut (DOC). The work piece material chosen is Aluminium (AA8011), and the output responses considered are Cutting force (CF), Material removal rate (MRR), surface roughness (SR), and amplitudes (Amp) of vibrations. In this present work, forces and vibrations of the tool are determined using a dynamometer and vibrometer. The nature of amplitude is identified with respect to speed and depth of cut. Three levels of variation are applied to the input process parameters (S, F, DOC). Experiments are carried out on an All-geared lathe machine following the orthogonal array (L9). A multi-optimization is also performed using Grey relation analysis, with equal weightage given to cutting force, Material removal rate (MRR), surface roughness (SR), and amplitudes (Amp). The significant input process parameters are Speed (higher values: 1200), feed (moderate: 0.003), and Depth of cut (higher values: 0.6), which are the optimal levels for minimizing surface roughness and amplitude while maximizing Material removal rate.

Experimental Investigations of Nano Finishing Process on Nickel-Free Austenitic Stainless Steel by Grey Relational and Principal Component Analysis

The nano surface roughness of metallic materials is important in engineering and medical fields for specific applications. Magneto rheological abrasive flow finishing (MRAFF) process was performed on nickel-free austenitic stainless workpieces in order to obtain surface roughness at the nano level and also to forecast the performance of the MRAFF process in terms of responses such as surface roughness (SR) and material removal rate (MRR). These two responses are affected by process factors such as hydraulic pressure, current to the electromagnet, and the number of cycles performed during the machining process. The design of experiments (DOE) was used to determine the contributions of process parameters to output responses. The techniques of grey relational analysis (GRA) and principal component analysis (PCA) were used in these experimental investigations to discover the process factors that minimise the final Ra and maximise MRR. Through the DOE, a minimum SR of 63.24 nm and a maximum MRR of 2.34 mg/sec were obtained on the samples for the combination of 30 bar pressure, 6 A current, and 300 number of cycles.

Deep insights into interaction behaviour and material removal of β-SiC wafer in nanoscale polishing

Silicon carbide (β-SiC) polishing aims to maximize material removal rate and surface quality. Molecular dynamics (MD) simulations revealed β-SiC substrate removal methods with various movement mechanisms. The single and multi-asperities models show depths, abrasive sizes, polishing velocities, oscillation amplitude, and frequency. The vibration-coupled rolling motion removes the most atoms in both models, but the improvement in atom removal is most remarkable in the sliding motion. In all models, root-mean-square (RMS) was highest in an anti-clockwise rolling motion, while RMS improved most when increasing from one to three asperities. From single to multi-asperities, surface roughness improvement was lowest in rolling motion associated with vibration. Our findings on surface roughness and movement process in β-SiC materials' anti-friction and elimination capabilities provide new insights.

Application of graphene nano powder in the dielectric oil to optimize the machining variables during µ-EDM of Hastelloy C 276

The present work contributes to determine the impact of process parameters i.e., capacitance (C), gap voltage (V) and powder concentration (P) on the response parameters i.e., material removal rate (MRR) and diametral overcut (DOC) during graphene nano powder mixed μ-EDM performance of HC 276. Taguchi’s L 18 orthogonal array has been used to design the experimental dataset. It is found that the enhancement of C and V increases the values of MRR. MRR tends to reduce after 0.21 g/L concentration of graphene nano powder in the dielectric oil. The higher value of capacitance reduces the DOC, after an initial increase of DOC with gap voltage it starts to reduce and higher concentration of graphene nano powder generates lower DOC of the μ-hole. The lower concentration of graphene nano powder provides maximum MRR and minimum DOC can be achieved at a higher concentration. Taguchi's grey relational analysis (GRA) revealed that higher value of gap voltage along with higher capacitance and powder concentration resulted in the maximal MRR and minimal DOC. ANOVA analysis established that C has the maximum percentage contribution towards both MRR and DOC followed by V and V × P.

MRR and TWR Study of Powder Mix EDM and Pure EDM Based on Response Surface Methodology

Powder-mixed electrical discharge machining (PMEDM) is the process of enhancing the output of the machined surface by combining dielectric fluid with various types of powders. This process is quickly gaining acceptance in the electrical discharge machining (EDM) sector. The purpose of this study is to determine whether a dielectric fluid containing tantalum carbide (TaC) powder can improve material removal rate (MRR) also lessen tool wear rate (TWR) during the subsequent EDM machining of stainless steel material. During the machining, the material removal rates, tool wear rate, and mathematical models of two different EDM mediums were examined. For the machining procedure, kerosene dielectric fluid containing TaC powder at a concentration of 25.0 g/L was used. The machining input variables were used peak current, pulse on time, and pulse off time. We determined how these variables affected the MRR and TWR of the copper-based EDMed electrode tools. During electrical discharge machining, the MRR for stainless steel (SUS 304) was increased by MRRPMEDM by 4.3 to 5.3% and TWRpEDM was reduced by 37.9% when TaC powder additive was used. Optimized results also show that TWR and maximum MRR can be achieved at 81.98% and 13.779mm3/min respectively with 83.50% desirability whenever the pulse on-time and pulse off-time are 6.20 µs and 6.50 µs respectively. The models are reliable and can be used to forecast the machining responses within the experimental region, it can be said. The MRR and TWR model for EDM with TaC powder additive (MRRPMEDM) identifies current as the most significant factor, followed by pulse on time and pulse off-time.

Discussions on Some Key Issues of Two-Dimensional Rotational Ultrasonic Combined Electro-Machining of Composite Materials.

In order to improve the surface forming quality and machining efficiency of composite materials and reduce tool wear, a two-dimensional rotary ultrasonic combined electro-machining (2DRUEM) technology with low electrical conductivity and low current density was proposed in this study. Additionally, a gap detection unit of the machining system was designed with the integration of grinding force and gap current, and the average errors and maximum errors of the model were 5.61% and 12.08%, respectively, which were better than single detection. Furthermore, the machining parameters were optimally selected via NSGA-II, and the maximum machining surface roughness error was 5.9%, the maximum material removal rate error was 5.5%, and the maximum edge accuracy error was 8.9%, as established through experiments.

A study of thermal etching of GaN by atmospheric argon inductively coupled plasma

GaN has a wide application in electronics and optoelectronics due to its excellent electrical properties. The thermal etching process is commonly used in the growth and subsequent process of GaN. As an easy-to-operate technique, atmospheric argon inductively coupled plasma (Ar-ICP) is first applied to the thermal etching of GaN in this study. Through the analysis of surface chemical composition and near-surface plasma spectroscopy, the etching principle and mechanism of the circular etching pit formation are revealed. The maximum material removal rate of Ar-ICP thermal etching can reach 18.72 μm/min. The optical properties of the sliced GaN after the Ar-ICP thermal etching and lapping process are evaluated by photoluminescence (PL), respectively. The PL intensity of the Ar-ICP thermally etched sample increases and the peak position of near-band emission shows a red shift compared to the lapped sample.

Effects of Tool Edge Geometry and Cutting Conditions on the Performance Indicators in Dry Turning AISI 1045 Steel

This article presents an experimental investigation and statistical analysis of the effects of cutting conditions on the machining performance of AISI 1045 steel using a wiper-shaped insert. Experimental findings are used to compare the machining performance obtained using wiper inserts with those obtained using conventional round-nose inserts as recently reported in the literature. In addition, the effects of process conditions, namely cutting speed, feed rate, and depth of cut, are analyzed in order to obtain optimum conditions for both types of inserts. The goal is to achieve the optimal machining outcomes: minimum surface roughness, resultant cutting force, and cutting temperature, but maximum material removal rate. A full factorial design was followed to conduct the experimental trials, while ANOVA was utilized to estimate the effect of each factor on the process responses. A desirability function optimization tool was used to optimize the studied responses. The results reveal that the optimum material removal rate for wiper-shaped inserts is 67% more than that of conventional inserts, while maintaining a 0.7 µm surface roughness value. The superior results obtained with wiper-shaped inserts allow wiper tools to use higher feed rates, resulting in larger material removal rates while obtaining the same surface quality.

Evaluation of Power Consumption in High Efficiency Milling (HEM) of Aluminium 6061

In the machining industry, reducing energy consumption at a maximal material removal rate (MRR) has long been a priority. Using the response surface method, a predictive model has been proposed for the minimal power consumption in side-milling machining. Using response surface method (RSM), the effect of cutting parameters (feed rate, spindle speed, and radial depth of cut) on power consumption was investigated. The results revealed thatfeed rateis the most influential parameter for power consumption. The higher feed rate, the shorter cycle time thus reduce the power consumption. Based on the optimization model, minimum power consumption of 82.38 kW can be achieved at feed rate = 6,000 mm/in, radial depth of cut of 0.3 mm and spindle speed 12,000 rpm.

Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems

Honing processes are usually employed to manufacture combustion engine cylinders and hydraulic cylinders. Honing provides a crosshatch pattern that favors the oil flow. In this paper, Adaptive Neural Fuzzy Inference System (ANFIS) models were obtained for tool wear, average roughness Ra, cylindricity and material removal rate in finishing honing processes. In addition, multi-objective optimization with the desirability function method was applied, in order to determine the process parameters that allow minimizing roughness, cylindricity error and tool wear, while maximizing material removal rate. The results showed that grain size and tangential velocity should be at their minimum levels, while density, pressure and linear velocity should be at their maximum levels. If only roughness, cylindricity error and tool wear are considered, then low grain size, low pressure and low linear velocity are recommended, while density and tangential velocity vary, depending on the optimization algorithm employed. This work will help to select appropriate process parameters in finishing honing processes, when roughness, cylindricity error and tool wear are to be minimized.

Characterization and Machinability Studies of Aluminium-based Hybrid Metal Matrix Composites – A Critical Review

Metal matrix composites (MMCs) are attracting automobile and aeronautical sector because of their superior mechanical and physical characteristics which ultimately reduce the weight of components and hence the energy requirements. These composites are prepared by adding various reinforcements into the base metal by the methods like stir casting, squeeze casting, stir and squeeze casting, sand casting, in-setu method, powder metallurgy etc. When more than one particle is added into the base metal; these composites are called as Hybrid Metal Matrix Composites (HMMCs). The machinability of these hard to cut materials is a challenging task in front of manufacturing industry. Present study considers turning operation of HMMC done on either lathe or CNC machine by using different cutting tool materials. This review focuses on effect of various cutting parameters like speed, depth of cut, feed and also the parameters like reinforcement particle type, particle size and weight percentage on the machinability issues like surface roughness, MRR, cutting forces, tool wear etc. Further the various optimization methods used to suggest the cutting parameters to obtain minimum surface roughness, minimum cutting forces, minimum tool wear and maximum Material Removal Rate (MRR) are addressed.

Design of Real-Time Extremum-Seeking Controller-Based Modelling for Optimizing MRR in Low Power EDM

Electric discharge machining (EDM) is one of the non-conventional machining processes that supports machining for high-strength and wear-resistant materials. It is a challenging task to select the process parameters in real-time to maximize the material removal rate since real-time process trials are expensive and the EDM process is stochastic. For the ease of finding process parameters, a modelling of the EDM process is proposed. Due to the non-linear relationship between the material removal rate (MRR) and discharge time, a model-free adaptive extremum-seeking controller (ESC) is proposed in the feedback path of the EDM process for finding an optimal value of the discharge time at which the maximum material removal rate can be achieved. The results of the model show a performance that is closer to the actual process by choosing steel workpieces and copper electrodes. The proposed model offers a lower error rate when compared with actual experimental process data. When compared to manual searching for an optimal point, extreme seeking online searching performed better as per the experimental results. It was observed that the experimental validation also proved that the ESC can produce a large MRR by tracking the extremum control. The present study has been limited to only the MRR, but it is also possible to implement such algorithms for more than one response parameter optimization in future studies. In such cases the performance measures of the process could be further enhanced, which could be used for a real-time complex die- and mold-making process using EDM.

Optimization and analysis of machining performance for the milling process during milling of W-Al-Si-C alloy material

This study determined the optimum HSS cutting tool technique parameters for milling W-Al-Si-C rods using Taguchi methodology. This paper explains the empirical results of the selection of appropriate cutting settings that assure lower power consumption in high-end Computer Numerical Control (CNC) machines. An experiment employing the Taguchi methodology on an extruded W-Al- Si-C rod was performed on a CNC lathe with cutting speed, feed rate, and depth of cut as the process parameters. The performance characteristics (energy usage) were quantified by a data collection system. Minor energy process parameters were selected after data analysis. Experimental results are presented to demonstrate the worth of the chosen methodology. A total of 350[Formula: see text]rpm, 0.37[Formula: see text]mm/rev feed rate, and 1[Formula: see text]mm of cut depth produced the best MRR result. The maximum material removal rate (MRR) is obtained at lower levels of spindle speed and depth of cut, i.e., 1.452[Formula: see text]g/sec.

Optimization of machining parameters in EDM using Taguchi based grey relational analysis

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade.

Experimental Investigation of EN-31 Steel Using VMC

This research paper presents an experimental investigation using the Vertical Machining Centre (VMC) for machining the EN31 material. The study looks at how different input factors, including feed, speed, and cut depth, affect the output factors, like the material removal rate (MRR) and surface roughness (SR). The Taguchi method is used as the optimization technique to identify the optimal combination of input parameters that lead to maximum MRR and minimum SR. The objective of the study is to establish the relationship between input and output parameters and to offer a workable strategy for machining process optimization. The study's findings will advance our understanding of EN31 material machining with VMCs and have real-world implications for the machining sector.

Experimental Analysis on Machining Properties of Aluminum Hybrid Composite Materials

<div class="section abstract"><div class="htmlview paragraph">Hybrid Composite materials meet a major demand in current industrial market through mixture of various percentages of metal compositions at different levels. One such material, which is most widely employed, is aluminum. Aluminum hybrid metal matrix composites is substituted with conventional alloys in order to meet the rigid, high-end requirements at major industries. In this work, it is reinforced with Aluminum-Tungsten Carbide-Graphite composite and fabricated using the stir casting process. Die sinking electric discharge machining (EDM) process is preferred over conventional process for the purpose of machining complex shaped materials with accuracy. The optimization of minimizing surface roughness and maximize material removal rate is essential to increase the effective and efficient usage of hybrid composites. In this experimental work, the sensitivity of various parameters of EDM on the output responses is found using Taguchi experimental design, Multi response optimization and Analysis of variance (ANOVA) to generate the mathematical model. From the experiment, it is found that %WC = 6 %, %Gr = 3 %, I= 5A, V=50V, T<sub>OFF</sub>=50μs, is the most optimal value among others due to its meta stable bonding in improving the machining parameters to achieve optimization of minimizing surface roughness and maximizing material removal rate</div></div>

Assessing the performance of air, argon, and oxygen as dielectric mediums during dry-micro-EDM of Ti6Al4V alloy: A comparative study

ABSTRACT Due to sheer environmental risks associated with hydrocarbon-based dielectric media, their use is discouraged not only in manufacturing processes but also throughout all commercial operations to meet the Sustainable Development Goals. Therefore, continuous research efforts are being made to find an alternative to hydrocarbon-based EDM oil. The current work is an attempt in the same direction, wherein the use of alternate dielectric media during EDM of micro-grooves on Ti6Al4 V alloys has been investigated. The performance of three dielectric gases, air, argon, and oxygen, has been evaluated. To predict the parametric relationship, investigations have been performed by altering pulse current along with pulse-on/off times by utilizing the response-surface-methodology. The material removal mechanism for different gases is expounded using analysis of debris, single-discharge crater, and recast-layer. Using oxygen instead of air or argon yielded about 19% and 51% quicker material removal rate (MRR) for identical parameters. On the contrary, using argon instead of air and oxygen yielded roughly 17% and 34% less roughness and nearly 17% and 34% less width over-cut, respectively. Optimization results showed that oxygen rendered a maximum MRR of mm /min, which is almost three times that of argon. Splat-like surface features were observed on the machined surface for oxygen.

Experimental study on the influence of turning process parameters on surface texture and material removal rate of composites by using Grey relational Taguchi technique

Now a days industries are looking to manufacture components that are having a good surface texture and should have more life to be in working condition. The automobile industry and aerospace industries are looking for new materials and methods to increase the good surface texture with the optimum resource utilization to prepare the components. To achieve this it is essential to go for CNC machines, new methods, and materials. At present industries are using alloys and composite materials widely to manufacture their components. To meet the needs of industries it is essential to study the surface roughness, and material removal rate of new materials, as these output responses are vital for materials to use in industries. In this study, my work is on composites Al6061/SiC/Gr (HAMMC). To machine the samples of composite, CNC turning operation using of carbide single point cutting tool. The objective is to find the influence of machining parameters on surface roughness, material removal rate, and sustain of material under hot working conditions. By using, the Taguchi technique, some experiments are designed based on input parameters and level of parameters. Grey Relational Analysis was also used to optimize the parameters for the multi-performance characteristics of minimum surface roughness and maximum material removal rate in machining.

Comparison of material removal rate of AA2014 aluminum alloy using HSS M42 and Titanium Nitride coated drill tools

This research study focuses on the comparison of material removal rate (MRR) of two tools, HSS M42 and TiN Coated twist drill bits to find out the tool with maximum Material Removal Rate. Tool materials used in this research are HSS M42 tool and Titanium Nitride Coated tool as drill bits. Aluminium alloy AA2014 material is used as a specimen on which the drilling operation is done. Two groups with 20 specimens each are equally divided to measure the MRR of both the tools using CNC drilling operation. Spindle speed, feed and depth of cut were controlled factors. The observed results for each tool were recorded and analysed using SPSS software. Final Experimental MRR was investigated and found the final Mean MRR obtained for HSS M42 was 277.476 with a standard deviation of 37.4548. And the Mean MRR obtained for the TiN coated tool was 364.9031 with a standard deviation of 23.452. These values were obtained with a significance level of 0.035 which is less than 0.05. This research study shows that the TiN Coated drill bit has comparatively higher MRR than the HSS M42 drill bit in CNC Machining under dry drilling conditions.