i-manager Publications

Application of Artificial Neural Networks For the Prediction of Shrinkage and Warpage of Plastic Injection Molded Parts

B. Sidda Reddy*, K. Thirupathi Reddy**, Dr. K. Vijaya Kumar Reddy***

*Assistant Professor, Department of Mechanical Engineering, R.G.M College of Engg.&Tech.Nandyal, Andhra Pradesh.

**Professor & Head, Department of Mechanical Engineering, R.G.M College of Engg.&Tech.Nandyal, Andhra Pradesh

*** Controller of Examinations, Jawaharlal Nehru Technological University, Hyderabad.

Periodicity:August - October'2008
DOI : https://doi.org/10.26634/jfet.4.1.570

Abstract

This paper deals with the development of accurate shrinkage and warpage prediction model for plastic injection molded part using artificial neural networks. For training, testing of the shrinkage and warpage model, a number of MoldFlow (FE) analyses have been carried out using Box-Behnken Response Surface (BBRS) design technique by considering the process parameters such as mold temperature, melt temperature, packing pressure, packing time, cooling time and injection pressure. The shrinkage and warpage values were found by analyses which were done by MoldFlow plastic insight (MPI) 5.0 software. The artificial neural network model was developed using multilayer perceptron back propagation algorithm using train data and tested using test data. To judge the ability and efficiency of the model to predict the shrinkage and warpage values, percentage deviation and average percentage deviation has been used. The finite element results show that the adaption of back propagation algorithm in artificial neural networks achieved a very satisfactory prediction accuracy of 91.920498%, 90.857614% for warpage and shrinkage respectively.

Keywords

Plastic Injection Molding, Shrinkage, Warpage, Artificial Neural Network.

How to Cite this Article?

B. Sidda Reddy, K. Thirupathi Reddy and Dr. K. Vijaya Kumar Reddy (2008). Application of Artificial Neural Networks For the Prediction of Shrinkage and Warpage of Plastic Injection Molded Parts. i-manager’s Journal on Future Engineering and Technology, 4(1), 21-27. https://doi.org/10.26634/jfet.4.1.570

References

[1]. A. Athia and C. Ji, (1997)How Initial Conditions Affect Generalization Performance in Large Networks. IEEE Trans. on Neural Networks, no 8,pp 448-451.

[2]. D.E. Dimal, P.M.Lister, and N.J. Leighton(1997)Neural Network Solutions to the Tool Condition Monitoring Problem in Metal Cutting- A Critical Review of Methods . International Journal of Machine Tool Manufacturing, vol 37, pp 1219-1241.

[3]. D. Dornfeld(1990)A Neural Network Sensor Fusion for Tool Condition Monitoring. CIRP Annals, Vol 39, pp 101-105.

[4] A. Humar and M. Piska.(1999)Cutting Forces Analysis When Drilling Fibre Reinforced. Proceedings of the International SAMPE Technical Conference, Vol 31, pp 276-285

[5]. M. Leshno, V.Y. Lin, A. Pinkus and S. Schocken. (1993)Multilayer Feed forward Networks With a Non polynomial Activation Function Can Approximate Any Function, Neural Networksvol 6, no 6, pp 861-867.

[6]. T.W. Liao, and L.J. Chen.(1998)Manufacturing Process Modeling and Optimization Based on Multi-Layer Perceptron Network. Journal of Manufacturing Science and Engineering, vol 120, pp 109-119.

[7]. C. Murphy, M. Gilchrist, and G. Byrne.(2002) The Performance of Coated Tungsten Carbide Drills When Machining Carbon Fibre-Reinforced Epoxy Composite Materials. Proceedings of the IMECHE Part B Journal of Engineering Manufacture 216, no. 2, pp 143-150.

[8]. K. Palanikumar, L. Karunamoorthy and R. Kartikeyan.(2004) Optimal Machining Parameters for Achieving Minimal Tool wear in Turning or GFRP Composites. International Journal of Manufacturing Science and Production, vol6, Part 3, pp 119-128.

[9]. W. Pederson and M. Ramulu. (2005)Proposed Tool Wear Model for Machining Particle Reinforced Metal Matrix Composites.SocietyofManufacturingEngineers, Technical PaperTP05PUB39, pp 1-10.

[10]. V. Petridis and V.G. Kaburlasos.(2001)Clustering and Classification in Structured Data Domains Using Fuzzy LatticeNeurocomputing (FLN),IEEE Transactions on Knowledge and Data Engineering, vol 13 no 2, pp 245-260.

[11]. J. Ramkumar, S.K. Malhotra and R. Krishnamurthy.(2004) Effect of Workpiece Vibration on Drilling of GFRP Laminates, Journal of Materials Processing Technology, vol 152, no 3 pp 329-332.

[12]. H. Schneiderman and T. Kanade(2004) Object Detection Using the Statistics of Parts, International Journal of Computer Vision, vol 56 no 3, pp 151-177.

[13].A.N.ShuaibandF.A.Al-Sulaiman.(2004)Machinability ofKevlar® 49 Composite Laminates While Using Standard TiN Coated HSS Drills. Machining Science and Technology,vol 8, No.3 pp 449-467.

[14]. Y.L. Yao and X. Fang.(1993)Assessment of Chip Forming Patterns with Tool Wear Progression in Machining Via Neural Networks”, International Journal of Machine Tool Manufacture, vol 33, pp 89-102.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.