Optimization of Surface Roughness in Electro Chemical Machining

2014 ◽  
Vol 606 ◽  
pp. 193-197 ◽  
Author(s):  
M. Sankar ◽  
A. Gnanavelbabu ◽  
R. Baskaran
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Copper Electrode ◽  
Alloy Specimen ◽  
Micro Ecm ◽  
The Future

Electro chemical machining seems to be the future of micro and nanomachining due to its advantages like high MRR, no tool wear, highly precise, reliability, better control over machining and so on. Surface roughness is an important factor in electro chemical machining. ECM can produce surface roughness of the order of 0.4μm. This paper is devoted to the study of micro ECM process to obtain a surface roughness of about 0.3μm in an alluminium alloy specimen using a copper electrode.

scholarly journals Performance evaluation of graphite and titanium oxide powder mixed dielectric for electric discharge machining of Ti–6Al–4V

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Shahriar Tanvir Alam ◽  
AKM Nurul Amin ◽  
Md. Ibrahim Hossain ◽  
Maliha Huq ◽  
Shaqauit Hossain Tamim
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Titanium Oxide ◽  
Metal Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Graphite Powder ◽  
Copper Electrode ◽  
Sem Images ◽  
Powder Concentration

AbstractTi–6Al–4V is the most commonly used titanium alloy in aerospace, marine, and biomedical applications. Due to the properties of poor machinability in conventional machining, Electrical Discharge Machining (EDM) is considered a prospective alternative for machining this strategic material. This study aims at enhancing the performance of powder mixed EDM (PMEDM) in the machining of Ti–6Al–4V with the application of two different types of powders, namely Graphite (Gr) and Titanium Oxide (TiO2) powders, with different concentrations in dielectric—kerosene. The effect of these powers and their relative quantities are studied in terms of metal removal rate (MRR), tool wear rate, Surface Roughness, and surface integrity. Machining is performed using the copper electrode and kerosene as the dielectric medium. A separate container and a submersible pump are used to limit the quantity of powder and keep the powder in suspension, respectively. Design of experiments guided by Design-Expert software is employed to minimize the number of experimental runs and develop empirical models of response parameters in terms of the variable parameters—peak current, powder type, and powder concentration. Findings indicate that TiO2 powder has a much higher effect on MRR compared to graphite powder, as the maximum MRR in the case of TiO2 powder is recorded 41.01 mm3/min against 11.98 mm3/min for graphite powder, i.e., 3.42 times higher. Similarly, the tool wear ratio for TiO2 powder is 0.0704 against 0.1219 for graphite powder at the maximum MRR, which is 1.73 times lower compared to that of graphite powder. The same ratios at the minimum MRR for TiO2 is 0.0098, and for graphite power is 0.0282, which is again 2.88 times lower compared to that of graphite powder. In terms of average surface roughness, Ra, the performance of TiO2 is far better compared to graphite powder since the maximum surface roughness attained with TiO2 powder is 3.265 μm against 9.936 μm for graphite powder at the highest MRR and the same attained at the lowest MRR are 2.228 μm and 2.411 μm for TiO2 and graphite powders respectively. The mechanism of the effects of PMEDM on surface texture has also been observed using SEM images to study the influence of powder concentration on surface morphology.

scholarly journals Effect of deep cryogenic treatment on copper electrode for non-traditional electric discharge machining (EDM)

2019 ◽  
Vol 10 (2) ◽  
pp. 413-427
Author(s):  
Gurdev Singh Grewal ◽  
Dhiraj Parkash Dhiman
Keyword(s):  
Surface Roughness ◽  
Wear Rate ◽  
Tool Wear ◽  
Electric Discharge ◽  
Copper Electrode ◽  
Machining Process ◽  
Tool Wear Rate ◽  
Electric Discharge Machining ◽  
Electrode Rotation ◽  
Input Parameters

Abstract. In the last few decades, non-traditional machining made the machining process easier than the traditional machining method. Electric discharge machining (EDM) is one of the most prominent methods of non-traditional machining processes. By the use of EDM, a complex profile and high hardness materials can be easily machined, which cannot easily be machined by the traditional machining method. EDM is widely used by the industries. This paper investigates an experiment with the cryogenically treated copper electrode and an ordinary copper electrode with various input parameters like the electrode rotation, gap voltage and discharge current for an EN24 (a high-strength and wear-resistant steel) material. An experiment was performed with electric discharge machining. Designs of an experiment are carried out using the Taguchi approach. An orthogonal L16 array prepared and used the different combination of the three input parameters (current, electrode rotation and gap voltage) to find an optimum value of the factors. The output factors are the overcut (OC), the tool wear rate (TWR) and surface roughness (Ra). The optimal level and importance levels of each of these parameters are obtained statically using an analysis-of-variance (ANOVA) table through the analysis of the S∕N ratio. The study also compares the theoretical and experimental values of the overcut, tool wear rate and surface roughness for traditional and non-traditional EDM. The following research finds optimal or dominating factors (current and rotational speed) for the TWR and Ra in both traditional and non-traditional electric discharge machining; moreover there was a reduction of approximately 9 % in overcut, 13.25 % in the tool wear rate and 15.75 % in surface roughness for the deep cryogenic and non-traditional machining process.

Effect of SiC-Cu Electrode on Material Removal Rate, Tool Wear and Surface Roughness in EDM Process

2020 ◽  
Vol 38 (9A) ◽  
pp. 1406-1413
Author(s):  
Yousif Q. Laibia ◽  
Saad K. Shather
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
304 Stainless Steel ◽  
Tool Electrode ◽  
Pulse Time ◽  
Edm Process

Electrical discharge machining (EDM) is one of the most common non-traditional processes for the manufacture of high precision parts and complex shapes. The EDM process depends on the heat energy between the work material and the tool electrode. This study focused on the material removal rate (MRR), the surface roughness, and tool wear in a 304 stainless steel EDM. The composite electrode consisted of copper (Cu) and silicon carbide (SiC). The current effects imposed on the working material, as well as the pulses that change over time during the experiment. When the current used is (8, 5, 3, 2, 1.5) A, the pulse time used is (12, 25) μs and the size of the space used is (1) mm. Optimum surface roughness under a current of 1.5 A and the pulse time of 25 μs with a maximum MRR of 8 A and the pulse duration of 25 μs.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

scholarly journals Influence of turning tool wear on the surface integrity and anti-fatigue behavior of Ti1023

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110112
Author(s):  
Li Xun ◽  
Wang Ziming ◽  
Yang Shenliang ◽  
Guo Zhiyuan ◽  
Zhou Yongxin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Plastic Deformation ◽  
Titanium Alloy ◽  
Fatigue Life ◽  
Tool Wear ◽  
Surface Integrity ◽  
Low Cycle Fatigue ◽  
Fatigue Behavior ◽  
Machined Surface ◽  
Deformation Layer

Titanium alloy Ti1023 is a typical difficult-to-cut material. Tool wear is easy to occur in machining Ti1023, which has a significant negative effect on surface integrity. Turning is one of the common methods to machine Ti1023 parts and machined surface integrity has a direct influence on the fatigue life of parts. To control surface integrity and improve anti-fatigue behavior of Ti1023 parts, it has an important significance to study the influence of tool wear on the surface integrity and fatigue life of Ti1023 in turning. Therefore, the effect of tool wear on the surface roughness, microhardness, residual stress, and plastic deformation layer of Ti1023 workpieces by turning and low-cycle fatigue tests were studied. Meanwhile, the influence mechanism of surface integrity on anti-fatigue behavior also was analyzed. The experimental results show that the change of surface roughness caused by worn tools has the most influence on anti-fatigue behavior when the tool wear VB is from 0.05 to 0.25 mm. On the other hand, the plastic deformation layer on the machined surface could properly improve the anti-fatigue behavior of specimens that were proved in the experiments. However, the higher surface roughness and significant surface defects on surface machined utilizing the worn tool with VB = 0.30 mm, which leads the anti-fatigue behavior of specimens to decrease sharply. Therefore, to ensure the anti-fatigue behavior of parts, the value of turning tool wear VB must be rigorously controlled under 0.30 mm during finishing machining of titanium alloy Ti1023.

scholarly journals Using uniform design and regression methodology of turning parameters study of nickel alloy

2021 ◽  
Author(s):  
Shao-Hsien Chen ◽  
Chih-Hung Hsu
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Nickel Alloy ◽  
Design Methodology ◽  
Cutting Tool ◽  
Uniform Design ◽  
Machining Time ◽  
Variable Parameters ◽  
Cutting Test ◽  
Set Up

AbstractThe nickel alloy has good mechanical strength and corrosion resistance at high temperature; it is extensively used in aerospace and biomedical and energy industries, as well as alloy designs of different chemical compositions to achieve different mechanical properties. However, for high mechanical strength, low thermal conductivity, and surface hardening property, the nickel alloy has worse cutting tool life and machining efficiency than general materials. Therefore, how to select the optimum machining parameters will influence the workpiece quality, cost, and machining time. This research will be using a new experimental design methodology to the cutting parameter planning for nickel-based alloy cutting test, and used the uniform design methodology to cutting test to reduce the number of experiments. Three independent variable parameters are set up, including cutting speed, feed rate, and cutting depth, and four dependent variable parameters are set up, including cutting tool wear, surface roughness, machining time, and cutting force. A nickel alloy turning parameter model is built by using regression analysis to further predict the I/O relationship among various combinations of variables. The errors between actual values and prediction values are validated. When the cutting tool wear (VB) is 2.72~6.18%, the surface roughness (Ra) is 4.10~7.72%, the machining time (T) is 3.75~8.82%, and the cutting force (N) is 1.54~7.42%; the errors of various dependent variables are approximately less than 10%, so a high precision estimation model is obtained through a few experiments of uniform design method.

Influence of cryogenic treatment of cutting tool inserts on tool wear and surface roughness during milling of Inconel 718

2020 ◽  
Author(s):  
Ivan Sunit Rout ◽  
P. Pal Pandian ◽  
Manish Mathew ◽  
Kevin Lobo Ivan ◽  
Shomyajit Misra
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Inconel 718 ◽  
Cutting Tool ◽  
Cryogenic Treatment

Digital Object Memory Integration into Indirect Surface Roughness Measurement in Turning

2013 ◽  
Vol 465-466 ◽  
pp. 764-768 ◽  
Author(s):  
Tanel Aruväli ◽  
Tauno Otto
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Digital Object ◽  
Roughness Measurement ◽  
Object Memory ◽  
Surface Roughness Measurement ◽  
Memory Integration ◽  
Evaluation Time ◽  
Roughness Evaluation ◽  
Influencing Parameters

The paper investigates in-process signal usage in turning for indirect surface roughness measurement. Based on theoretical surface roughness value and in-process signal, a model is proposed for surface roughness evaluation. Time surface roughness and in-process signal surface roughness correlation based analysis is performed to characterize tool wear component behavior among others. Influencing parameters are grouped based on their behavior in time. Moreover, Digital Object Memory based solution and algorithm is proposed to automate indirect surface roughness measurement process.

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