scholarly journals Tool Wear and Material Removal Predictions in Micro-EDM Drilling: Advantages of Data-Driven Approaches

2020 ◽  
Vol 10 (18) ◽  
pp. 6357
Author(s):  
Mattia Bellotti ◽  
Ming Wu ◽  
Jun Qian ◽  
Dominiek Reynaerts
Keyword(s):  
Tool Wear ◽  
Regression Models ◽  
Material Removal ◽  
Removal Rate ◽  
Processing Parameters ◽  
Data Driven ◽  
Tool Wear Prediction ◽  
Electrical Discharge Drilling ◽  
Using Data ◽  
Edm Drilling

In micro electrical discharge drilling, regression models are commonly used for predicting the material removal rate (MRR) and tool wear rate (TWR) from the applied processing parameters. However, these models can be inaccurate since the processing parameters might not always be representative of the actual machining conditions, which depend on several other factors such as the tool length or gap flushing efficiency. In order to increase the prediction accuracy, the present work investigates the capability of data-driven regression models for tool wear and material removal prediction. The errors in predicting the MRR and TWR are shown to decrease of about 65% and 85% respectively when using data collected through process monitoring as input of the regression models. Data-driven approaches for in-process tool wear prediction have also been implemented in drilling experiments, demonstrating that a more accurate control of the hole depth (50% average reduction of the depth error) can be achieved by using data-driven predictive models.

Experimental Investigation on Dry Electrical Discharge Drilling

2013 ◽  
Vol 554-557 ◽  
pp. 1845-1850
Author(s):  
Margareta Coteaţă ◽  
Irina Besliu ◽  
Hans Peter Schulze ◽  
Nicolae Pop ◽  
Laurenţiu Slătineanu
Keyword(s):  
Material Removal ◽  
Removal Rate ◽  
Active Surface ◽  
Dielectric Liquid ◽  
Dielectric Fluids ◽  
Dry Edm ◽  
Electrical Discharge Drilling ◽  
Experimental Researches ◽  
Off Time ◽  
Edm Drilling

The electrical dischargemachining (EDM) is known as the machining method in which the material removalis the result of developing electrical discharges having certain energetic andtemporal characteristics between the workpiece and the active surface of thetool electrode, while a dielectric liquid is circulated in the gap, essentiallyin order to remove debris. The last decades showed that in certain conditions,the dielectric liquid could be replaced by gaseous dielectric fluids, theprocess being named dry EDM. In the paper, some preliminary considerations areformulated about the material removal process for dry EDM drilling. Experimental researches were developed byconsidering the modifying of the values of some electrical characteristics(peak current, on time, off time and applied voltage) and determining thequantity of material removed from the test piece and the wear of the toolelectrode. The experimental results were mathematically processed and empiricalrelations were determined; these mathematical relations highlight the influenceexerted by the input parameters on the material removal rate.

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.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Process Design and Optimization of end Milling Parameters of Al 7075 Metal Matrix Composite

2021 ◽  
pp. 929-937
Author(s):  
D. S. Sai Ravi Kiran ◽  
Alavilli Sai Apparao ◽  
Vempala GowriSankar ◽  
Shaik Faheem ◽  
Sheik Abdul Mateen ◽  
...  
Keyword(s):  
Tool Wear ◽  
Material Removal Rate ◽  
Metal Matrix ◽  
Material Removal ◽  
Hybrid Composites ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Weight Percentage

This paper investigates the machinability characteristics of end milling operation to yield minimum tool wear with the maximum material removal rate using RSM. Twenty-seven experimental runs based on Box-Behnken Design of Response Surface Methodology (RSM) were performed by varying the parameters of spindle speed, feed and depth of cut in different weight percentage of reinforcements such as Silicon Carbide (SiC-5%, 10%,15%) and Alumina (Al2O3-5%) in alluminium 7075 metal matrix. Grey relational analysis was used to solve the multi-response optimization problem by changing the weightages for different responses as per the process requirements of quality or productivity. Optimal parameter settings obtained were verified through confirmatory experiments. Analysis of variance was performed to obtain the contribution of each parameter on the machinability characteristics. The result shows that spindle speed and weight percentage of SiC are the most significant factors which affect the machinability characteristics of hybrid composites. An appropriate selection of the input parameters such as spindle speed of 1000 rpm, feed of 0.02 mm/rev, depth of cut of 1 mm and 5% of SiC produce best tool wear outcome and a spindle speed of 1838 rpm, feed of 0.04 mm/rev, depth of cut of 1.81 mm and 6.81 % of SiC for material removal rate.

CHARACTERIZATION OF ZrB2-SiC COMPOSITES WITH AN ANALYTICAL STUDY ON MATERIAL REMOVAL RATE AND TOOL WEAR RATE DURING ELECTRICAL DISCHARGE MACHINING

2016 ◽  
Vol 40 (3) ◽  
pp. 331-349 ◽  
Author(s):  
S. Sivasankar ◽  
R. Jeyapaul
Keyword(s):  
Wear Rate ◽  
Tool Wear ◽  
Relative Density ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Tool Wear Rate ◽  
Tool Materials

This research work concentrates on Electrical Discharge Machining (EDM) performance evaluation of ZrB2- SiC ceramic matrix composites with different tool materials at various machining parameters. Monolithic ZrB2 possesses lower relative density (98.72%) than composites. ZrB2 with 20 Vol.% of SiC possesses 99.74% of the relative density with improved hardness values. Bend strength and Young’s modulus increase with SiC addition until it reaches 20 Vol% and then decreasing. EDM performance on tool materials of tungsten, niobium, tantalum, graphite and titanium at various levels of pulse on time and pulse off time are analyzed. Graphite produces the best Material removal rate (MRR) for all the workpieces. Tool wear rate decreases with melting point and thermal conductivity of the tool material.

scholarly journals Investigation an assisting electrode powder mixed electrical discharge machining of nonconductive ceramic

2021 ◽  
Author(s):  
Dragan Rodic ◽  
Marin Gostimirovic ◽  
Milenko Sekulic ◽  
Borislav Savkovic ◽  
Branko Strbac
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Metal Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Positive Effects ◽  
Assisting Electrode

Abstract It is well known that electrical discharge machining can be used in the processing of nonconductive materials. In order to improve the efficiency of machining modern engineering materials, existing electrical discharge machines are constantly being researched and improved or developed. The current machining of non-conductive materials is limited due to the relatively low material removal rate and high surface roughness. A possible technological improvement of electrical discharge machining can be achieved by innovations of existing processes. In this paper, a new approach for machining zirconium oxide is presented. It combines electrical discharge machining with assisting electrode and powder-mixed dielectric. The assisting electrode is used to enable electrical discharge machining of nonconductive material, while the powder-mixed dielectric is used to increase the material removal rate, reduce surface roughness, and decrease relative tool wear. The response surface method was used to generate classical mathematical models, analyzing the output performances of surface roughness, material removal rate and relative tool wear. Verification of the obtained models was performed based on a set of new experimental data. By combining these latest techniques, positive effects on machining performances are obtained. It was found that the surface roughness was reduced by 18%, the metal removal rate was increased by about 12% and the relative tool wear was reduced by up to 6% compared to electrical discharge machining with supported electrode without powder.

Investigation of Grinding Force and Surface Integrity of IC10 in Creep Feed Grinding

2020 ◽  
Author(s):  
Jun-chen Li ◽  
Wen-hu Wang ◽  
Rui-song Jiang ◽  
Xiao-fen Liu ◽  
Huang Bo ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Material Removal ◽  
Normal Force ◽  
Removal Rate ◽  
Tangential Force ◽  
Grinding Force ◽  
Wheel Speed ◽  
Creep Feed Grinding ◽  
Creep Feed

Abstract The IC10 superalloy material is one of the most important materials for aero-engine turbine blade due to its excellent performances. However, it is difficult to be machined because of its special properties such as terrible tool wear and low machined efficiency. The creep feed grinding is widely used in machining IC10 superalloy due to the advance in reducing tool wear, improving material removal rate and surface quality. The creep feed grinding is a promising machining process with the advantages of high material removal rate due to large cutting depth, long cutting arc and very slow workpiece, and its predominant features might have significant influence on the grinding force and surface quality for the workpiece. Hence, it is of great importance to study the grinding force and surface integrity in creep feed grinding IC10 superalloy. In this paper, a series of orthogonal experiments have been carried out and the effects of grinding parameters on the grinding force and the surface roughness are analyzed. The topographies and defects of the machined surface were observed and analyzed using SEM. The results of the experiments show that the tangential force is decreased with the workpiece speed increasing. However, there is no significant change in tangential force with the increasing of grinding depth and wheel speed. The normal force is decreased with the workpiece speed increasing when the workpiece speed is less than 150 mm/min, but when the workpiece speed is more than 150 mm/min the normal force is increased tardily. Moreover, the normal force is increased sharply with the increase of grinding depth and is increased slowly with the increase of wheel speed. In general, the surface roughness is increased with workpiece speed and grinding depth increasing, while the trend of increase corresponding that of workpiece speed is more evident. The value of the surface roughness is decreased with wheel speed increasing. And it is found out that the main defect is burning of the IC10 superalloy material in creep feed grinding by energy spectrum analysis of some typical topography in this study.

Fabrication and experimental investigation of magnetic field assisted powder mixed electrical discharge machining on machining of aluminum 6061 alloy

2019 ◽  
Vol 233 (12) ◽  
pp. 2283-2291 ◽  
Author(s):  
Arun Kumar Rouniyar ◽  
Pragya Shandilya
Keyword(s):  
Magnetic Field ◽  
Wear Rate ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Tool Wear Rate ◽  
Pulse On Time

Magnetic field assisted powder mixed electrical discharge machining is a hybrid machining process with suitable modification in electrical discharge machining combining the use of magnetic field and fine powder in the dielectric fluid. Aluminum 6061 alloy has found highly significance for the advanced industries like automotive, aerospace, electrical, marine, food processing and chemical due to good corrosion resistance, high strength-to-weight ratio, ease of weldability. In this present work, magnetic field assisted powder mixed electrical discharge machining setup was fabricated and experiments were performed using one factor at a time approach for aluminum 6061 alloy. The individual effect of machining parameters namely, peak current, pulse on time, pulse off time, powder concentration and magnetic field on material removal rate and tool wear rate was investigated. The effect of peak current was found to be dominant on material removal rate and tool wear rate followed by pulse on time, powder concentration and magnetic field. Increase in material removal rate and tool wear rate was observed with increase in peak current, pulse on time and a decrease in pulse off time, whereas, for material removal rate increases and tool wear rate decreases up to the certain value and follow the reverse trend with an increase in powder concentration. Material removal rate was increased and tool wear rate was decreased with increase in magnetic field.

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