full title: areal surface roughness simulation model …
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AREAL SURFACE ROUGHNESS SIMULATION MODEL FOR END-MILLING PROCESS
AUTHOR:FATIHA NAZIERA BINTI YUSOF
PRESENTER:
FATIHA NAZIERA BINTI YUSOF
FACULTY OF MECHANICAL ENGINEERING | UITM
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OUTLINE
INTRODUCTION EXPECTED RESULT
LITERATURE REVIEW CONCLUSION
METHODOLOGY ACKNOWLEDGEMENT
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INTRODUCTIONG L O B A L R E S E A R C H
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Objective
To propose a new method in predicting areal surface roughness in end-milling process using simulation technique.
Scope of Research
a. Most commonb. Greatly used in
metal removal processes
Machining Process
End-Milling
WHY?
Method Simulation and Experimental
Figure 1: The illustration of end-milling process (Li, 2001)
PROBLEM EXPLANATION
There is several limitations when using the current
existing model in predicting surface roughness
1. Experimental Approach: it is easy for an experiment not to produce the expected results
2. Taguchi Method: the results obtained are only relative and do not exactly indicate what parameter has the highest effect on the performance characteristic.
3. ANN: empirical, costly, time consuming, unexplained behaviour of the network
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Previous method for surface roughness prediction:
Milling Roughness Prediction
Empirical
ANN
Taguchi
Experimental
Analytical Simulation Model
Areal method
Ball End Mill
End Mill
Profile method End Mill
END-MILL
BALL END-MILL
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LITERATURE REVIEW
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Surface Roughness Measurement
Profile Method
Definition: Measures surface in a line across
the surface
Limitation: Product functional performance of this method cannot be diagnosed
directly
Areal Method
Definition:Displayed a topographical image
of the surface and can be interpreted mathematically as a
height function, z (x,y)
Figure 3: The illustration of areal methodFigure 2: The illustration of profile method
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METHODOLOGYThe research flow of this study was divided into three stages:
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• Modelling a significant simulation model in predicting surface roughness in end-milling.
• Experimental investigation to test the surface roughness simulation.
Analysis of simulation data and experimental data
• Identifying surface roughness modelling factors
• Developing an algorithm by modifying existing algorithm based on surface roughness factors chosen
Stage 1 Stage 2 Stage 3
Selected Factors
Spindle Speed Depth of
Cut
Cutting Velocity
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STAGE 1
Identifying surface roughness modelling factors
STAGE 2
Modelling a significant model in predicting surface roughness in end-milling
Determine the appropriate factor that influence surface roughness in
end-milling
Select only the necessary factors:
Spindle speed, Depth of cut, Cutting velocity
Select possible design variables
(cutting velocity and spindle speed)
Define the ranges on the design variables.
Identify explicit constraints on the modified
mathematical model
Developing the simulation prediction model based on the
modified mathematical model by using MATLAB algorithm
programmer software
Analysis of segmented area Plotting graph
STAGE 2
Experimental investigation to test the surface roughness simulationExperiment Surface Roughness Measurement
• Method:
Areal Method
• Equipment:
Alicona Infinite
Focus Microscope
Process ParameterCutting Velocity (m/min) 17Depth of cut (mm) 0.2Spindle speed (rpm) 1000, 2000, 3000Machining process End-millingMilling cutting tool 12 mmCutter path Single directionEquipment required NC DesktopMaterials Aluminum
Results verification
Compare simulation data
with experimental
data
Discussion Conclusion
STAGE 3
Analysis of simulation data and experimental data
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EXPECTED RESULT
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Figure 4: The simulation of edge of cutting:(a) The simulation on locus of grain locus with a respective
condition, Rg(b) Description of segmentated area (Ismail et al., 2012).
EXPECTED RESULT
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Figure 6: The relationship between the edge of cutting position, R and roughness of the surface (Ismail et al., 2012).
The theoretical result obtained would indicate a comparable pattern with the graph shown in Figure 6.
Figure 5: The variance of divided areas by cutting edge path sequence (a) Forward direction(b) Reverse direction (Ismail et al., 2012).
(a) (b)
CONCLUSION
The designation of this model will help to deliver the actual surface roughness values for decision-
making in the machining process. .
1
The theoretical model obtained is able to
predetermine surface roughness
2
Able to help the metal cutting industry to produce a certain
quality surface quality.
3
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ACKNOWLEDGEMENT
Ministry of Higher Education (MOHE) for the financialsupport given under the FRGS fund with a Grant No:FRGS/1/2018/TK03/UITM/02/03
Universiti Teknologi MARA Cawangan Pulau Pinang(UiTM/CPP)
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