Intelligent Process Modeling and Optimization of Porosity Formation in High-Pressure Die Casting-Reference-Cited by-同舟云学术

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Intelligent Process Modeling and Optimization of Porosity Formation in High-Pressure Die Casting

Published:2018-02-22 Issue:4 Volume:12 Page:814-824
ISSN:1939-5981
Container-title:International Journal of Metalcasting
language:en
Short-container-title:Inter Metalcast

Author:

Cica Djordje,Kramar Davorin

Funder

The Ministry of Science and Technology of the Republic of Srpska

Publisher

Springer Science and Business Media LLC

Subject

Materials Chemistry,Metals and Alloys,Industrial and Manufacturing Engineering,Mechanics of Materials

Link

http://link.springer.com/article/10.1007/s40962-018-0213-8/fulltext.html

Reference26 articles.

1. G.P. Syrcos, Die casting process optimization using Taguchi methods. J. Mater. Process. Technol. 135(1), 68–74 (2003)

2. V.D. Tsoukalas, S.A. Mavrommatis, N.G. Orfanoudakis, A.K. Baldoukas, A study of porosity formation in pressure die casting using the Taguchi approach. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 218(1), 77–86 (2004)

3. K.S. Anastasiou, Optimization of the aluminium die casting process based on the Taguchi method. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 216(7), 969–977 (2002)

4. U.A. Dabade, R.C. Bhedasgaonkar, Casting defect analysis using design of experiments (DOE) and computer aided casting simulation technique. Proc. CIRP 7, 616–621 (2013)

5. J.K. Kittur, M. Choudhari, M. Parappagoudar, Modeling and multi-response optimization of pressure die casting process using response surface methodology. Int. J. Adv. Manuf. Technol. 77(1–4), 211–224 (2015)

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1. Study of the Solidification Behavior and Porosity Measurements to Enhance Fatigue Life of HPDC Aluminum Swing Arm Design;Journal of Failure Analysis and Prevention;2024-08-24

2. A Study on Solidification Behavior of a High Pressure Die Cast Aluminum Swing Arm Using Commercial Modeling Software To Predict Porosity Level;Journal of The Institution of Engineers (India): Series C;2024-07-01

3. Automated Porosity Characterization for Aluminum Die Casting Materials Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning;Sensors;2024-05-05

4. Innovative Hybrid High-Pressure Die-Casting Process for Load-Bearing Body-In-White Structural Components;International Journal of Metalcasting;2024-02-17

5. Deep learning-based detection of aluminum casting defects and their types;Engineering Applications of Artificial Intelligence;2023-02

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