Multi-Parameter Modeling of Surface Texture in EDMachining Using the Design of Experiments Methodology

2006 ◽  
Vol 526 ◽  
pp. 157-162 ◽  
Author(s):  
G. Petropoulos ◽  
N. Vaxevanidis ◽  
A. Iakovou ◽  
Kostas David
Keyword(s):  
Design Of Experiments ◽  
Surface Texture ◽  
Pulse Current ◽  
Surface Characteristics ◽  
Close Correlation ◽  
Functional Surface ◽  
Machining Conditions ◽  
Texture Model ◽  
Aisi D2 ◽  
Pulse On Time

This study concerns the formulation of a multi-parameter surface texture model in EDMachining of AISI D2 tool steel. The model is developed in terms of pulse current and pulse-on time which are the dominant machining conditions, via factorial design of experiments. By applying analysis of variance and statistical multi-regression analysis to the experimental data close correlation is proved between certain surface finish parameters and the machining conditions, with pulse current exerting the strongest influence. By applying this model the appropriate conditions for successful finish can be selected, as well as functional surface characteristics can be quantified.

Response Surface Analysis of EDMED Surfaces of AISI D2 Steel

2011 ◽  
Vol 264-265 ◽  
pp. 1960-1965
Author(s):  
Mohan Kumar Pradhan ◽  
Chandan Kumar Biswas
Keyword(s):  
Material Removal ◽  
Pulse Current ◽  
Removal Rate ◽  
Machining Parameters ◽  
Response Functions ◽  
Process Variables ◽  
Aisi D2 ◽  
Pulse On Time ◽  
D2 Steel ◽  
Machining Characteristics

In this study, the effects of the machining parameters in electrical-discharge machining (EDM) on the machining characteristics of AISI D2 steel using copper electrodes were investigated. The response functions considered material removal rate (MRR) and Surface Roughness (Ra),while machining variables are pulse current, pulse on time, pause time and gap voltage. A Response surface methodology was used to reduce the total number of experiments. Empirical models correlating process variables and their interactions with the said response functions have been established. The significant parameters that critically influenced the machining characteristics were examined, and the optimal combination levels of machining parameters for material removal rate, and surface roughness were determined. The models developed reveal that pulse current is the most significant machining parameter on the response functions followed by voltage and pulse off time for MRR. However for, for Ra also pulse current is most significant followed by pulse on time and discharge voltage the respectively. The model sufficiency is very satisfactory as the coefficientR2of is determination (R2) is found to these be greater than 98 %. These models can be used for selecting the values of process variables to get the desired

scholarly journals Modelling and optimization of Chromium Powder Mixed EDM Parameter Effect Over the Surface Characteristics by Response Surface Methodology Approach

2018 ◽  
Vol 3 (2) ◽  
pp. 78-86 ◽  
Author(s):  
Nor Ain Binti Jamil Hosni ◽  
Mohd Amri Bin Lajis ◽  
Muhammad Ridzuan Bin Idris
Keyword(s):  
Response Surface ◽  
Discharge Current ◽  
Surface Characteristics ◽  
Recast Layer ◽  
Multiple Regression Equation ◽  
Chromium Powder ◽  
Powder Concentration ◽  
Aisi D2 ◽  
Pulse On Time ◽  
Powder Mixed Edm

In this paper, an optimization of chromium powder mixed parameters effect, i.e. discharge current, pulse on time and Cr powder concentration of AISI D2 steels in Powder Mixed EDM (PMEDM) has been made. RSM has been employed to plan and analyzed the experiment. Central composite design (CCD) was chosen as the RSM design that is useful for investigating the quadratic effects. The version 8.0 of the Design Expert software was used to develop the experimental plan for RSM. A mathematical model in the form of the multiple regression equation for second order response surface with the best fittings was developed. The results identify that discharge current and pulse on time the most important parameters effect to minimize recast layer. With the topmost desirability solution, the suggested optimum parameter of discharge current is 20.12 A, pulse-on time 50.14 µs and 3.96 g/L powder concentration to minimize recast layer.

Optimization and surface modification in electrical discharge machining of AA 6061/SiCp composite using Cu–W electrode

2015 ◽  
Vol 231 (3) ◽  
pp. 332-348 ◽  
Author(s):  
Balbir Singh ◽  
Jatinder Kumar ◽  
Sudhir Kumar
Keyword(s):  
Process Parameters ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Surface Characteristics ◽  
Electrode Wear ◽  
Material Transfer ◽  
Machined Surface ◽  
Recast Layer Thickness ◽  
Pulse On Time ◽  
Pulse Off Time

This paper presents the experimental investigation on the electro-discharge machining of aluminum alloy 6061 reinforced with SiC particles using sintered Cu–W electrode. Experiments have been designed as per central composite rotatable design, using response surface methodology. Machining characteristics such as material removal rate (MRR), electrode wear ratio (EWR), and surface roughness (SR) have been investigated under the influence of four electrical process parameters; namely peak current, pulse on time, pulse off time, and gap voltage. The process parameters have been optimized to obtain optimal combination of MRR, EWR, and SR. Further, the influence of sintered Cu–W electrode on surface characteristics has been analyzed with scanning electron microscopy, energy dispersive spectroscopy, and Vicker microhardness tests. The results revealed that all the process parameters significantly affect MRR, EWR, and SR. The machined surface properties are modified as a result of material transfer from the electrode. The recast layer thickness is increased at higher setting of electrical parameters. The hardness across the machined surface is also increased by the use of sintered Cu–W electrode.

Solution to Inverse Problem of Manufacturing by Surface Modification With Controllable Surface Integrity Correlated to Performance: A Case Study of Thermally Sprayed Coatings for Wear Performance

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
X. P. Zhu ◽  
P. C. Du ◽  
Y. Meng ◽  
M. K. Lei ◽  
D. M. Guo
Keyword(s):  
Inverse Problem ◽  
Surface Modification ◽  
Surface Integrity ◽  
High Performance ◽  
Surface Characteristics ◽  
Spray Angle ◽  
Functional Surface ◽  
Machining Processes ◽  
Wear Performance ◽  
Surface Features

Inverse problem of manufacturing is studied under a framework of high performance manufacturing of components with functional surface layer, where controllable generation of surface integrity is emphasized due to its pivotal role determining final performance. Surface modification techniques capable of controlling surface integrity are utilized to verify such a framework of manufacturing, by which the surface integrity desired for a high performance can be more effectively achieved as reducing the material and geometry constraints of manufacturing otherwise unobtainable during conventional machining processes. Here, thermal spraying of WC–Ni coatings is employed to coat stainless steel components for water-lubricated wear applications, on which a strategy for direct problem from process to performance is implemented with surface integrity adjustable through spray angle and inert N2 shielding. Subsequently, multiple surface integrity parameters can be evaluated to identify the major ones responsible for wear performance by elucidating the wear mechanism, involving surface features (coating porosity and WC phase retention) and surface characteristics (microhardness, elastic modulus, and toughness). The surface features predominantly determine tribological behaviors of coatings in combination with the surface characteristics that are intrinsically associated with the surface features. Consequently, the spray process with improved N2 shielding is designed according to the desired surface integrity parameters for higher wear resistance. It is demonstrated that the correlations from processes to performance could be fully understood and established via controllable surface integrity, facilitating solution to inverse problem of manufacturing, i.e., realization of a material and geometry integrated manufacturing.

Enhanced Hybrid Model to Predict the Surface Roughness of Honed cylinder Bore

2021 ◽  
pp. 1-25
Author(s):  
Burhan Afzal ◽  
Xueping Zhang ◽  
Anil Srivastava
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Surface Texture ◽  
Prediction Accuracy ◽  
Analytical Models ◽  
Superior Performance ◽  
Functional Surface ◽  
Model Framework ◽  
Multiple Variables ◽  
Cylinder Bore

Abstract Cylinder bore honing is a finishing process that generates a crosshatch pattern with alternate valleys and plateaus responsible for enhancing lubrication and preventing gas and oil leakage in the engine cylinder bore. The required functional surface in the cylinder bore is generated by a sequential honing process and is characterized by Rk roughness parameters (Rk, Rvk, Rpk, Mr1, Mr2). Predicting the desired surface roughness relies primarily on two techniques: (i) analytical models (AM) and (ii) machine learning (ML) models. Both of these techniques offer certain advantages and limitations. AM's are interpretable as they indicate distinct mapping relation between input variables and honed surface texture. However, AM's are usually based on simplified assumptions to ensure the traceability of multiple variables. Consequently, their prediction accuracy is adversely impacted when these assumptions are not satisfied. However, ML models accurately predict the surface texture but their prediction mechanism is challenging to interpret. Furthermore, the ML models' performance relies heavily on the representativeness of data employed in developing them. Thus, either prediction accuracy or model interpretability suffers when AM and ML models are implemented independently. This study proposes a hybrid model framework to incorporate the benefits of AM and ML simultaneously. In the hybrid model, an Artificial neural network (ANN) compensates the AM by correcting its error. This retains the physical understanding built into the model while simultaneously enhancing the prediction accuracy. The proposed approach resulted in a hybrid model that significantly improved the prediction accuracy of the AM and additionally provided superior performance compared to independent ANN.

Experimental Study on the Effect of Surface Integrity on Fatigue Performance of Aero-Engine Blades

2021 ◽  
Author(s):  
Yun Huang ◽  
Shaochuan Li ◽  
Guijian Xiao ◽  
Benqiang Chen ◽  
Yi He ◽  
...  
Keyword(s):  
Residual Stress ◽  
Surface Roughness ◽  
Fatigue Life ◽  
Fatigue Strength ◽  
Fatigue Test ◽  
Surface Texture ◽  
Surface Characteristics ◽  
Service Performance ◽  
Aero Engine ◽  
Vibration Fatigue

Abstract As the core component of aero-engine, the service performance of aero-engine blade has an important influence on the engine’s reliability and safety performance. Existing studies have shown that machined surface characteristics affect the fatigue strength of components. However, current studies are all based on regular fatigue samples. The structure of blades different from fatigue samples, and the influence mechanism of structural differences on the service performance of blades is still unclear. In addition, the conventional fatigue test conditions are not representative for the blades’ actual service conditions, so it is difficult to realize the processing process for the service performance optimization. In this study, the aero-engine blades processed by abrasive belt grinding and the vibration fatigue test bench were used to explore the influence of surface roughness, surface texture, and surface residual stress on the fatigue performance of aero-engine blades under actual working conditions. The aero-engine blades were ground with different process parameters to obtain different single-factor surface characteristics. By comparing the vibration fatigue life of blades with different surface features, the influence degree of each surface feature on the fatigue life was explored. Results showed that surface roughness has the greatest influence on fatigue strength, followed by residual stress, and surface texture has the least influence on fatigue strength.

scholarly journals Experimental Study on the Influence of Machining Conditions on the Quality of Electrical Discharge Machined Surfaces of aluminum alloy Al5052

Machines ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 12 ◽  
Author(s):  
Angelos P. Markopoulos ◽  
Emmanouil-Lazaros Papazoglou ◽  
Panagiotis Karmiris-Obratański
Keyword(s):  
Aluminum Alloy ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
White Layer ◽  
Machining Parameters ◽  
Machining Processes ◽  
Machining Conditions ◽  
Pulse On Time ◽  
Machined Surfaces

Although electrical discharge machining (EDM) is one of the first established non-conventional machining processes, it still finds many applications in the modern industry, due to its capability of machining any electrical conductive material in complex geometries with high dimensional accuracy. The current study presents an experimental investigation of ED machining aluminum alloy Al5052. A full-scale experimental work was carried out, with the pulse current and pulse-on time being the varying machining parameters. The polishing and etching of the perpendicular plane of the machined surfaces was followed by observations and measurements in optical microscope. The material removal rate (MRR), the surface roughness (SR), the average white layer thickness (AWLT), and the heat affected zone (HAZ) micro-hardness were calculated. Through znalysis of variance (ANOVA), conclusions were drawn about the influence of machining conditions on the EDM performances. Finally, semi empirical correlations of MRR and AWLT with the machining parameters were calculated and proposed.

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