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NIRAJAN PUDASAINI 2VX12MPD15PowerPoint Presentation

NIRAJAN PUDASAINI 2VX12MPD15

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NIRAJAN PUDASAINI 2VX12MPD15

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“A Study On Machining Parameters Optimization Of Spheroidal Graphite Iron On A Vertical Machining Center”

A Project on:

NIRAJAN PUDASAINI

2VX12MPD15

PROF. R R MALAGI

PROJECT GUIDE

- Design matrix
- Sequence of operation
- Manufacture of workpiece
- Machining
- Measurement of SR
- Observation table
- Results
- Conclusion
- Future works

- Abstract
- Vertical milling
- About SG iron
- Input parameters
- Response variables
- Objective
- Literature review
- Problem statement
- About DOE
- RSM
- Box- Behnken design

- This report presents an approach to predicting the surface roughness and material removal rate in milling of spheroidal graphite iron using tungsten carbide insert tool and its optimization by coupling the prediction model with response surface methodology.
- In this work, experiments are carried out as per the Box-Behnkendesign and an L13 orthogonal array is used to study the influence of various combinations of process parameters on SR and MRR.
- ANOVA test is conducted to determine the significance of each process parameter. Two sets of L13 OA are used each for tool orientation of 45 and 90 degrees.
- This work may be useful in selecting optimum values of various process parameters that would maximize the MRR and minimize the SR in machining.

- Machining Centers classified as:
- Vertical Machining centers
- Horizontal Machining centers
- Universal Machining centers

- VMC has spindle on vertical axis relative to work table
- Used for flat works that require tool access from top
- For e.g.: Mould and die cavities, large aircraft components

Figure: A Vertical Milling Machine

- Also called ductile iron
- Characterized by graphite occurred in microscopic spheroids
- Various grades, differed due to matrix (microstructure of metal around the graphite)

- Cutting speed
- It is the speed difference (relative velocity) between the cutting tool and the surface of the workpiece.
- Cutting speed = π*D*N, D is diameter of workpiece

- Feed rate
- It is the feed of the tool against the workpiece in distance per time-unit.
- FR (mm/min) = RPM * T * CL, T= No. of teeth, CL= Chip load

- Depth of cut
- It is how deep the tool is under the surface of the material being cut. This will be the height of the chip produced.
- dcut = , D and d are initial and final weight of workpiece

- Surface roughness
- It is a measure of the level of unevenness of the part's surface.
- Measurement procedure
- Surface inspection by comparison method
- Direct instrument method

- Parameters
- Ra = arithmetic mean of departures of profile from mean line
- Rq, Ry, Rz, Sm are other parameters

2. Material removal rate

- It is the volume of material removed divided by the machining time.
- MRR can be expressed as the ratio of the difference between the weight of the work piece before and after machining to the machining time.
- MRR= (Wb-Wa)/t
- Where
- Wb= Weight of work piece before machining.
- Wa= Weight of work piece after machining.
- t = Machining time

- Where

The objective of this study is to find out the optimum levels for the process parameters so that the surface roughness value will be minimum and rate of material removal will be maximum in a vertical machining center and to check the optimality by developing empirical models.

- Pratyusha J et al. made a study for finding out optimum parameters for milling process using Taguchi methods. L9 array was used, parameters studied were speed, feed and depth of cut. They found that Taguchi method provides a systematic and efficient methodology for searching optimal milling parameters.
- R. Suresh et al. made an attempt to analyse the influence of cutting speed, feed rate, DOC and machining time on machinability characteristics like SR and tool wear using RSM. The found that combination of low feed rate, low depth of cut and low machining time with high cutting speed is beneficial for minimizing the machining force and surface roughness

- Balindersingh et al. carried out experiments for optimization of input parameters in the CNC milling on EN 24 steel.
- Taguchi technique used
- SR and MRR were response variables, speed, feed and DOC were control parameters
- L27 array was used generated from MINITAB V15
- Confirmation runs was used to verify the experiment

- Other research on VMC using Taguchi technique were done by Piyush Pandey et al., Avinash A. Thakre, Reddy Sreenivalsu and so on.
- Milon D. Selvam et al., R. JaliliSaffar et al. used GA method.

- Ahmad Hamdan et al. carried out experiments for high speed machining of stainless steel using L9 array and Taguchi method. Results showed a reduction of 25.5% in cutting forces and 41.3% in SR improvement.
- NorfadzlanYusup et al. made a comparison of five year researches from 2007 to 2007 that used evolutionary techniques to optimize machining process parameters. They found that SR is mostly studied with GA.
- A Kacal and M Gulesin studied optimal cutting condition in finish turning of of ductile iron using Taguchi method. ANOVA was used to identify significant factors affecting SR. They found that feed rate is most significant.

- In machining operation, the quality of surface finish and the rate of material removal are important requirements.
- The choice of optimized cutting parameters is very important for controlling the required surface quality and obtaining the maximum MRR.
- In this study, the optimum machining parameters, for vertical milling of SGiron, are to be determined to increase MRR and reduce the SR.

- Statistical design of experiments refers to the process of planning the experiments so that appropriate data that can be analysed by statistical methods will be collected, resulting in valid and objective conclusions.

- Guidelines for designing an experiment

- It is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response.

- In Statistics, RSM explores relationship between several explanatory variables and one or more response variables.
- Idea is to use a sequence of designed experiments to obtain an optimal solution.

- Estimate first-degree polynomial by factorial experiments
- Explains which explanatory variables have an impact on response variable of interest.
- By Box-behnken method, 2nd degree polynomial model is estimated.

- This second degree polynomial can be used to optimize.

- A useful method for developing second-order response surface models
- Based on the construction of balanced incomplete block designs and requires at least three levels for each factor.
- Requires only three levels to run an experiment. It is a special 3-level design because it does not contain any points at the vertices of the experiment region.

- Number of trails and corresponding level

- Geometric representation

- Input parameters with levels

- Design matrix for 45 degree tool orientation

- Design matrix for 90 degree tool orientation

- Using a sand casting method.
- 26 sets of workpieces were manufactured
- Made up of SG Iron

- Performed at a vertical machining center, Shradha enterprises, Udyambag, Belgaum
- The machine used is TAKUMI MCV-1000 model

Takumi MCV- 1000

Clamping of workpiece

Milling cutter

Machining with coolant

- Surface roughness measurement is done using the Surtronic 3+ device available at metrology laboratory of Gogte Institute of Technolgy, Belgaum.

- Observation table for 45 degree tool orientation

- Observation table for 90 degree tool orientation

- To find out which factors among the speed, feed and DOC is significant in increasing MRR and reducing the SR and at what level
- Response surface analysis using MINITAB v16
- ANOVA to check adequacy of model
- Confidence interval = 85 %
- Only terms whose p < 0.15 is used to develop empirical model
- Analysis done using coded units, so, empirical equation generated are expressed in coded units

- Regression analysis for MRR

- Analysis of Variance for MRR

- Empirical model for MRR
MRR

=

0.090 + 0.060250* feed +0.013583* DOC+ 0.024993* speed* speed + 0.063085* speed * DOC

- Main effect plot for MRR at 45 degree insert orientation

- Interaction effect

- Optimum machining parameters

- Regression analysis for surface roughness for insert at 45 degree

- Analysis of Variance for SR

- Empirical model for SR
SR

=

5.7 – 0.36125 * feed – 0.7925 * speed +0.38750 *feed* speed

- Main effect plot for SR at 45 degree insert orientation

- Interaction effect

- Optimum machining parameters

- Regression analysis for MRR

- Analysis of Variance for MRR

- Empirical model for MRR
MRR

=

0.0865 + 0.044588 * feed + 0.03512 * DOC +0.05825 *speed * DOC

- Main effect plot for MRR at 90 degree insert orientation

- Interaction effect

- Optimum machining parameters

- Regression analysis for surface roughness

- Analysis of Variance for SR

- Empirical model for SR
SR

=

5.51 – 0.435 * feed – 0.925 *speed + 0.37750* feed* speed + 0.29750* Speed * DOC

- Main effect plot for SR at 90 degree insert orientation

- Interaction effect

- Optimum machining parameters

ANOVA result showed that for both condition of tool orientation, feed and depth of cut have significant impact on material removal rate. Interaction of speed and DOC also have significant impact on it. For surface roughness, feed and speed has main effect and interaction of feed and speed also has significant impact for 45 degree tool orientation and for 90 degree orientation along with above factors a combination of speed and DOC is also significant.

- Results obtained can be used as a model for the selection machining parameters while machining in a VMC in order to obtain optimum MRR and SR.
- Also possible to study the effect of various other parameters like number of passes, tool diameter, austempering temperature of spheroidal graphite iron and so on.
- Another possibility is the use of collected data and results in order to find the signal to noise ratio by using the Taguchi method.

THANK YOU