Study on the Surface Roughness of Titanium Alloy Machined by WEDM

2013 ◽  
Vol 683 ◽  
pp. 581-584 ◽  
Author(s):  
Shu Guo Zhao ◽  
Rui Li ◽  
Xiao Min Yao
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Electrical Discharge ◽  
Medical Equipment ◽  
Electrical Discharge Machining ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Relational Analysis ◽  
Electric Parameters ◽  
Grey Relational

Due to the excellent characteristics of titanium alloy, was applied to aviation, marine, automotive, metallurgy, medical equipment and other fields. However, some of the characteristics make it machine difficultly, so the range of applications would be limited. In this paper, titanium alloy is cut by wire electrical discharge machining (WEDM) and study the surface finish. The model of surface roughness is established which is based on theoretical analysis. Experimental results were analyzed and optimized by the theory of signal-to-noise ratio and grey relational analysis (GRA) method. The minimal surface roughness is achieved by the optimal results .According to GRA, get the order that is the influence of electric parameters on the surface roughness.

scholarly journals High Speed Machining for Enhancing the AZ91 Magnesium Alloy Surface Characteristics Influence and Optimisation of Machining Parameters

2022 ◽  
Vol 72 (1) ◽  
pp. 105-113
Author(s):  
Vikas Marakini ◽  
Srinivasa P Pai ◽  
Uday K Bhat ◽  
Dinesh Thakur Singh ◽  
Bhaskar P Achar
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Signal To Noise Ratio ◽  
Surface Characteristics ◽  
Taguchi Design ◽  
Machining Parameters ◽  
Signal To Noise ◽  
Relational Analysis ◽  
Noise Ratio ◽  
Grey Relational

In this study, optimum machining parameters are evaluated for enhancing the surface roughness and hardness of AZ91 alloy using Taguchi design of experiments with Grey Relational Analysis. Dry face milling is performed using cutting conditions determined using Taguchi L9 design and Grey Relational Analysis has been used for the optimization of multiple objectives. Taguchi’s signal-to-noise ratio analysis is also performed individually for both characteristics and grey relational grade to identify the most influential machining parameter affecting them. Further, Analysis of Variance is carried to see the contribution of factors on both surface roughness and hardness. Finally, the predicted trends obtained from the signal-to-noise ratio are validated using confirmation experiments. The study showed the effectiveness of Taguchi design combined with Grey Relational Analysis for the multi-objective problems such as surface characteristics studies.

Optimization of Wire Cut Electrical Discharge Machining of Inconel 800 Using Grey Relational Analysis

2012 ◽  
Vol 576 ◽  
pp. 552-555 ◽  
Author(s):  
M. Durairaj ◽  
S. Gowri ◽  
M.H. Gauthamkumar ◽  
M. Ashok Kumar ◽  
R. Aishwarya
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Kerf Width ◽  
Performance Criteria ◽  
Relational Analysis ◽  
Grey Relational ◽  
Pulse On Time ◽  
Inconel 800

Wire Electrical Discharge Machining is one of the important non-traditional machining processes, which is used for machining difficult to machine materials and intricate profiles. In this present study, machining is done using Wire-Cut EDM and experimentation is planned according to Taguchi’s design of experiments [2]. Each experiment has been performed under different cutting conditions of gap voltage, pulse ON time, and pulse OFF time and Wire feed. Inconel 800 was selected as a work material and Brass wire of 0.25mm diameter as the tool to conduct the experiments. From experimental results, the surface roughness and Kerf Width was determined for each machining performance criteria. Grey Relational Analysis [1] is used for optimization of Surface Roughness and Kerf width.

Study on the improvement of the surface integrity and efficiency of electrical-discharge-machined TC4 titanium alloy via abrasive flow machining

2020 ◽  
pp. 095440542097109
Author(s):  
Ze Yu ◽  
Dunwen Zuo ◽  
Yuli Sun ◽  
Guohua Li ◽  
Xuemei Chen ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Surface Quality ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Small Hole ◽  
Machining Process ◽  
Machining Efficiency ◽  
Abrasive Flow Machining ◽  
Tc4 Titanium Alloy

To simultaneously optimize the surface quality and machining efficiency of the electrical discharge machining (EDM) processes used to produce titanium alloy quadrilateral group small hole parts, a combined “EDM + AFM” machining technology is proposed in this paper as an efficient and high-quality machining approach. In the proposed method, TC4 titanium alloy is first machined using the EDM process with graphite electrodes and the abrasive flow machining (AFM) process is then used to finish the machined surface. The effects of various electrical parameters on EDM-derived surface quality and improvements in EDM-derived quality under the application of AFM were assessed and, using the final surface roughness as a constraint condition, the effects of various combinations of EDM and “EDM + AFM” on efficiency were studied. The results revealed that the thickness and surface roughness of the superficial recast layer of the TC4 titanium alloy increase with both current and pulse width; in particular, increasing these parameters can increase the surface roughness by two to three grades. Following AFM, the alloy has a more uniform hardness distribution and the surface stress state changes from tensile to compressive stress, indicating that the combined “EDM + AFM” machining scheme can significantly enhance the surface quality of EDM-produced titanium alloy quadrilateral small group holes. The combined scheme achieves a balancing point beyond which increasing the roughness or the number of machining holes enhances either the machining efficiency or the machining surface quality. In the case of typical titanium alloy quadrilateral group small hole parts, the combined machining process can improve the finishing efficiency and total machining efficiency by 71.2% and 25.36%, respectively.

Surfactant and graphite powder–assisted electrical discharge machining of titanium alloy

2016 ◽  
Vol 231 (4) ◽  
pp. 641-657 ◽  
Author(s):  
Murahari Kolli ◽  
Adepu Kumar
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Graphite Powder ◽  
Recast Layer ◽  
Dielectric Fluid ◽  
Recast Layer Thickness

Surfactant and graphite powder–assisted electrical discharge machining was proposed and experiments were performed on titanium alloy in this investigation. Analysis was carried out to observe changes in dielectric fluid behaviour, material removal rate, surface roughness, recast layer thickness, surface topography and energy-dispersive X-ray spectroscopy. It was found out that the addition of surfactant to dielectric fluid (electrical discharge machining oil + graphite powder) improved the material removal rate and surface roughness. It was noticed to have reduced the recast layer thickness and agglomeration of graphite and sediment particles. Biface material migrations between the electrode and the workpiece surface were identified, and migration behaviour was powerfully inhibited by the mixing of surfactant. Surfactant added into dielectric fluid played an important role in the discharge gap, which increased the conductivity, and suspended debris particles in dielectric fluid reduced the abnormal discharge conditions of the machine and improved the overall machining efficiency.

Performance analysis of process parameters on machining α–β titanium alloy in electrochemical micromachining process

2016 ◽  
Vol 232 (9) ◽  
pp. 1577-1589 ◽  
Author(s):  
T Geethapriyan ◽  
K Kalaichelvan ◽  
T Muthuramalingam ◽  
A Rajadurai
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Electrolyte Concentration ◽  
Removal Rate ◽  
Electrochemical Micromachining ◽  
Relational Analysis ◽  
Micro Tool ◽  
Grey Relational

Due to inherent properties of Ti-6Al-4V alloy, it is being used in the application of fuel injector nozzle for diesel engine, aerospace and marine industries. Since the electrochemical micromachining process involves the no heat-affected zone, no tool wear, stress- and burr-free process compared to other micromachining processes, it is widely used in the manufacturing field to fabricate complex shape and die. Hence, it is highly important to compute the optimum input parameters for enhancing the machining characteristics in such machining process. In this study, an attempt has been made to find the influence of the process parameters and optimize the parameters on machining α–β titanium alloy using Taguchi-grey relational analysis. Since applied voltage, micro-tool feed rate, electrolyte concentration and duty cycle have vital role in the process, these parameters have been chosen as the input parameters to evaluate the performance measures such as material removal rate, surface roughness and overcut in this study. From the experimental results, it has been found that micro-tool feed rate has more influence due to its importance in maintaining inter electrode gap to avoid micro-spark generation. It has also been found that lower electrolyte concentration with lower duty cycle produces lower surface roughness with better circularity on machining α–β titanium alloy. The optimum combination has been found using Taguchi-grey relational analysis and verified from confirmation test. It has also been inferred that the multi-response characteristics such as material removal rate, surface roughness and overcut can be effectively improved through the grey relational analysis.

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