Modal Analysis of Porosity Defects in High Pressure Die Casting with a Neural Network
Karuna Kumar. G1, K. Ramteja2
1Karunakumar.G, Assistant Professor, Department of Mechanical Engineering, K L University, Guntur, Andhra Pradesh, India.
2K. Ramteja, Department of Mechanical Engineering, K L University, Guntur, Andhra Pradesh, India.
Manuscript received on 12 December 2012 | Revised Manuscript received on 21 December 2012 | Manuscript Published on 30 December 2012 | PP: 38-42 | Volume-2 Issue-1, December 2012 | Retrieval Number: A0366112112 /2012©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: High Pressure Die Casting (HPDC) is a complex process that results in casting defects if configured improperly. However, finding out the optimal configuration is a non -trivial task as eliminating one of the casting defects (for example, porosity) can result in occurrence of other casting defects. The industry generally tries to eliminate the defects by trial and error which is an expensive and error -prone process. This paper aims to improve current modelling and understanding of defects formation in HPDC machines. We have conducted conventional die casting tests with a neural network model of HPDC machine and compared the obtained results with the current understanding of formation of porosity. While most of our findings correspond well to established knowledge in the field, some of our findings are in conflict with the previous studies of die casting.
Keywords: Artificial Neural Network, High Pressure Die Casting, Porosity
Scope of the Article: Artificial Neural Network