Artificial Neural Network Based Defect Prediction in Casting

Authors

  • Anoop Sankar Mechanical Engineering Department, Mar Baselios Institute of Technology and Science, Ernakulam, Kerala, India
  • P Karunakaran Mechanical Engineering Department, Erode Sengunthar Engineering College, Perundurai, Erode, Tamilnadu, India

DOI:

https://doi.org/10.37591/joma.v2i2.7270

Keywords:

Casting, Neural Networks, Defect Prediction

Abstract

The main problems which are facing by most of the casting industries is loss of productivity which is due to casting defects occurred during the production time. The main casting defects are cracks, misruns, blowholes scabs and airlocks. Most of the investigations made in this area is only discussing the defects occurred after a cast is made and no method has yet been developed to prevent the defects before casting. In this work the data which was collected from an industry is used as input to train the neural network. After training, if a new set of data is entered the network will automatically predict the defects, and then by altering the molding parameters the defects can be prevented.

References

Dobrzanski LA, Krol M. Application of the neural network for Mg-Al-Zn mechanical properties modelling. Journal of achievements in materials and manufacturing Engineering. 2009; 37(2) 549–555p.

Irena Zmak, Tomislav Filetin. Mechanical properties ofcast iron Determined by Neural Networks. Proceedings of the Third International Conference on Modeling, Simulation and Applied Optimization. Sharjah,U.A.E, 20–22 January 2009.

Meneghetti G, Pediroda V, Poloni C. Application of a Multi Objective Genetic Algorithm and a Neural Network to the optimisation of foundry processes. indentations and transmission electron microscopy, Acta Materialia. 2003; 51: 2859–2872p.

Florin Susac, Mihaela Banu, Alexandru Epureanu. Artificial Neural Network Applied to Thermomechanical Fields Monitoring during Casting. Proceedings of the 11th WSEAS Int. Conf. on Mathematical methods,Computational Techniques and intelligent. 2010.

Yinggan,Xiumei Zhang. Application of a new image segmentation method to detection of defects in castings. Journal of Advanced manufacturing Technology. 43(5-6): 431–439p.

Lu L and Dhale AK. Iron–Rich Intermettalic Phases and Their Role in Casting Defect Formation in Hypoeutectic Al-Si alloys. Journal of Metallurgical and Material transactions. 2005; 36(13) 819–835p.

Suyitno D, Eskin G, Saravan VI Effect of Alloy Composition and Casting Speed on Structure Formation and Hot Tearing during direct chill casting. Mettalurgical and Material Transactions. 2004; 35a: 3551p.

Li D, Baba N. Study of Pd-Ag dental alloys: Examination of effect of casting porosity on fatigue behaviour and micro structural analysis, Mater Sci: Mater Med. 2010 ; 21(10): 2723–2731p.

Choong Do Lee. Defect Susceptibility of Sensile Strength to Micro porosity Variation in As –Cast Magnesium Alloys with Different Grain Sizes. Met. Mater.Int. 2003; 16: 543–551p.

Hong CP, Shen HF. Prevention of Macro defects in Squeeze Casting of an Al-7 Wt Pct Si Alloy. Metallurgical and Material Transactions. 2000; 31 B: 297p.

Shen HF, Hong CP. Prevention of Macro segregation in Squeeze Casting of an Al-4.5 Wt Pct Cu Alloy. Mettulurgical and Material Transactions. 1998; 29 A: 339p.

Jens Emmerlich, Denis Music, Per Eklund, Ola Wilhelmsson et al. Thermal stability of Ti3SiC2 thin films. Acta Materialia. 2007; 55(1): 1479–1488p.

Souchet A, Fontaine J, Belin M et al. Tribological duality of Ti3SiC2. Tribology Letters. 2005; 18(3): 220p.

Ke Tang, Chang-an Wang, Lifeng Wu et al. Sintering of Ti3SiC2 with B2O3 additions, Ceramics International. 2002; 28 (1): 761–765p.

Xinpo Lu, Yanchun Zhou. Pressure-less Sintering and Properties of Ti3AlC2, Int. J. Appl. Ceram. Technology. 2010; 7 (6) 744–751p .

Downloads

Published

2023-08-04

Issue

Vol. 2 No. 2 (2015)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.