FULL TITLE: AREAL SURFACE ROUGHNESS SIMULATION MODEL …

AREAL SURFACE ROUGHNESS SIMULATION MODEL FOR END-MILLING PROCESS

AUTHOR:FATIHA NAZIERA BINTI YUSOF

PRESENTER:

FATIHA NAZIERA BINTI YUSOF

FACULTY OF MECHANICAL ENGINEERING | UITM

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

OUTLINE

INTRODUCTION EXPECTED RESULT

LITERATURE REVIEW CONCLUSION

METHODOLOGY ACKNOWLEDGEMENT

G L O B A L R E S E A R C H C O N F E R E N C EG R a C e 2 0 2 0

INTRODUCTIONG L O B A L R E S E A R C H

C O N F E R E N C EG R a C e 2 0 2 0

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

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

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

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

LITERATURE REVIEW

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

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

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

METHODOLOGYThe research flow of this study was divided into three stages:

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

• 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

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

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

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

EXPECTED RESULT

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

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

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0

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

G L O B A L R E S E A R C H C O N F E R E N C EG R a C e 2 0 2 0

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)

G L O B A L R E S E A R C H C O N F E R E N C E

G R a C e 2 0 2 0