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Finite Element Analysis and Optimization of Closed Die Forging Process for Aluminium Metal Matrix Composites


Citation

Abdulmawlla, Mohamed A. (2010) Finite Element Analysis and Optimization of Closed Die Forging Process for Aluminium Metal Matrix Composites. PhD thesis, Universiti Putra Malaysia.

Abstract

The demand for lighter and stiffer products has been increasing in the last few years especially in automobile and aerospace manufacturing. The closed die forging process of aluminium metal matrix composite (Al-MMC) material is presented as possible solution, because it produces parts with good mechanical properties and lighter weight. The computational modelling of closed die forging process, using finite element method and optimization techniques, makes the design optimization faster and more efficient, decreasing the use of conventional “trial and error” methods. In this work, a commercial finite element software, ANSYS has been used to model cold closed die forging process. The model is developed using ANSYS ParametricDesign Language (APDL) to simulate a single stage axi-symmetry closed die forging process for H-cross sectional shape. Axisymmetric forgings with rib-web represent about 50% of the total forging output. So that the H-cross sectional shape used in this study has a wide applications. Simple compression test was carried out for aluminium alloy 6061 in order to determine its flow curve. Closed die forging experiment was conducted using aluminium alloy 6061 and then the experiment was simulated in order to verify the model for large deformation. The simulation results showed similar output with the experimental one. As a result, the model can be confidently used to simulate closed die forging for particulate reinforced Al-MMC. The Al-MMC material considered is AlMgSi matrix with 15%vol SiC particles, its flow curve and fractural strain are obtained from the literature. ANSYS Optimizer is used to obtain the maximum height that the material can flow in the rib by changing the design variables (DV) and the state variables (SV). Generally, design variables are geometrical parameters such as rib height to width ratio, web height to rib height ratio, fillet radii, draft angle and billet radius. State variables (SV) are some parameters that depend on the design variables such as the equivalent strain which must be below the fracture strain of the aluminium metal composite (AMC) material, and an acceptable contact gap (within the allowable tolerance range). The objective of this work is to achieve through simulation, the optimal die shape and billet diameter in consideration of complete die filling and the influence of metal flow deformation of Aluminium Metal Matrix Composite in a single stage closed die forging process. A commercially available finite element package (ANSYS) was used to investigate the effect of different die shapes and billet diameters on the closed die forging deformation. Optimization method called “Sub-Problem Approximation Method” was used to find out the optimal design set. The maximum rib height obtained in the single stage cold closed die forging of Hcross sectional shape disc made of AlMgSi matrix with 15%vol SiC particles is 0.185 of the disc radius. Trying to get higher rib height lead to higher strain and cracks will occur in the fillet region. The rib width and the web height are 0.218 and 0.3 of the disc radius simultaneously. Optimal billet height is 0.99 of the disc radius. The billet radius is a function of the die cavity volume and the billet height. The technique used in this work can be used for newly developed materials to investigate its forgeability for more complicated shapes in closed die forging process.


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Additional Metadata

Item Type: Thesis (PhD)
Subject: Finite element method
Subject: Aluminum
Subject: Metallic composite
Call Number: ITMA 2010 4
Divisions: Institute of Advanced Technology
Depositing User: Haridan Mohd Jais
Date Deposited: 02 Apr 2013 05:53
Last Modified: 02 Apr 2013 05:53
URI: http://psasir.upm.edu.my/id/eprint/19710
Statistic Details: View Download Statistic

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