1. INVESTIGATION OF SQUEEZE CAST AA7075-B4C COMPOSITES
Fifth National Conference on Trends in Automotive Parts Systems and Applications (TAPSA – 2017), 30th And 31st March 2017 (Student Poster Presentation)
INVESTIGATION OF SQUEEZE CAST AA7075-B4C COMPOSITES
R.Rajkumar1,R.Feroz Khan1,S.Ruban paul2
1Second Year UG Student, Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore-08
2First Year PG Student, Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore-08
*1 Corresponding Author Mobile: xxxxx xxxxx,
OBJECTIVE
EXPERIMENTAL PROCEDURE
RESULTS AND DISCUSSION
To optimize the process variables and evaluate the Mechanical Behaviour of the Squeeze cast AA7075-B4C composites using FFD.
To predict the results using regression analysis (FFD) and ANN and to determine the correlation factor and Error Percentage for each method
To validate the material for a Practical application (Connecting rod) for weight reduction using ANSYS Software
The Al7075 alloy is melted in the furnace and maintained at 800ᵒC. The preheated B4C particles and K2TiF6 are added to the molten metal and proper stirring is made for about 10 min. The molten mixture is passed to the die cavity through the preheating pathway. The squeeze pressure is applied on the molten mixture with a delay time of 3 sec using hydraulic press
Statistical analysis
Correlation factor for Predicted Results
Correlation factor for Predicted Results
Validation of composite for Practical
Application (Connecting rod) using FEA
PROBLEM IDENTIFICATION
Main Effects plot for Hardness
Main Effects plot for UTS
Selection of process variables is a key factor in a squeeze casting process as the properties of the materials not only dependent on the process but also on the parameters involved in the process.
Boron carbide (B4C) being one of the prominent reinforcing particle with less density and high hardness, has a poor bonding towards the matrix alloy (AA7075) due to its wettability.
The prediction of results using conventional methods such as physical modeling and mathematical modeling doesn’t provide the exact solutions for further processing.
In automotive vehicles, weight reduction of connecting rod is one of the key factors to obtain fuel economy. It is essential to use advanced materials with high strength to weight ratio to increase the efficiency of the automotive.
Original Mass
Optimized Mass
Conventional Connecting rod (16MnCr5)
0.124 Kg
0.111 Kg
Composite connecting rod (squeeze cast AA7075-B4C)
0.042 Kg
0.038 Kg
Experimental setup
Casted specimens
Percentage contribution of
factors for Hardness
Percentage contribution of
factors for UTS
EXPERIMENTAL DESIGN
Regression equation
In this work, the experiments are designed as per full factorial design with the factors set at their respective three levels (33).
Hardness = A B C A B C2
UTS = A B C A B C2
Conclusion
Factors
Squeeze Pressure
(MPa)
Die Preheating Temperature (ᵒC)
B4C weight (%)
Level 1 70
150 4
Level 2
105
225 8
Level 3
140
300 12
Conclusion
METHODOLOGY
Microstructure study and SEM analysis reveals that B4C particles were uniformly distributed along the grain boundaries of the Al7075 matrix alloy.
The addition of K2TiF6, proper stirring of the melt &preheating of the B4C particles provides effecting bonding of B4C particles with the Al7075 matrix.
From the main effect plot, the optimal level combination for obtaining maximum hardness and tensile strength was identified as (A3B2C3).
In comparison to the regression analysis, ANN is the most preferable solution for predicting the results in terms of Correlation factor and MEP.
From the analysis result, it observed that weight reduction of 63.45% with respect to the convention connecting rod material.
ACKNOWLEDGMENT
 I would like to thank Dr. R.Saravanan, Asst.Professor /Mech of The XXXXX College of Engineering, Bangalore for his valuable support to develop this project.
Microscopy 1
Boron Carbide (B4C) reinforced AA7075 composites are fabricated through squeeze casting process 2
Experiments are carried out by varying the process variables as per Full Factorial Design 3
The casted specimens (27) are prepared and tested (Microstructure, SEM, Hardness and UTS) as per ASTM standards 4
The selected process variables are optimized using statistical software 5
Experimental results are predicted using Regression analysis (FFD) and Artificial Neural Network (ANN) 6
A comparative study is made on the predicted results with the experimental results in terms of correlation factor and Error Percentage 7
Validation of the material for practical application (Connecting rod) using ANSYS workbench software
Maximum Mechanical properties obtained by varying the process variables are as follows
Mechanical properties
Hardness
166 VHN
Ultimate tensile strength
423 MPa
Squeeze Pressure
150 MPa
Die preheating temperature
225oC
Weight fraction of B4C particles
12%
SEM
Microstructure
Comparison of predicted results (FFD & ANN)
MODELING OF ANN
Experimental vs Predicted results for Hardness & UTS