Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Purpose: The aim of the work was the optimization of injection molded product warpage by using an integrated environment. Design/methodology/approach: The approach implemented took advantages of the Finite Element (FE) Analysis to simulate component fabrication and investigate the main causes of defects. A FE model was initially designed and then reinforced by integrating Artificial Neural Network to predict main filling and packing results and Particle Swarm Approach to optimize injection molding process parameters automatically. Findings: This research has confirmed that the evaluation of the FE simulation results through the Artificial Neural Network system was an efficient method for the assessment of the influence of process parameter variation on part manufacturability, suggesting possible adjustments to improve part quality. Research limitations/implications: Future researches will be addressed to the extension of analysis to large thin components and different classes of materials with the aim to improve the proposed approach. Originality/value: The originality of the work was related to the possibility of analyzing component fabrication at the design stage and use results in the manufacturing stage. In this way, design, fabrication and process control were strictly links
Wydawca
Rocznik
Tom
Strony
146--152
Opis fizyczny
Bibliogr. 16 poz., rys., tab., wykr.
Twórcy
autor
- Department of Mechanical and Management Engineering,Politecnico di Bari, Viale Japigia 182, 70126, Bari, Italy
Bibliografia
- [1] B.H. Min, A study on quality monitoring of injection-molded parts, J. of Materials Processing Technology, 136 (2003), 1–6
- [2] T. Erzurumlu, B.Ozcelik, Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method, Materials & Design, (2005) - Article in press
- [3] M.-C.Huang, C.-C.Tai, The effective factors in the warpage problem of an injection-molded part with a thin shell feature, J. of Materials Processing Technology, 110 (2001), 1-9
- [4] P. Postawa, J. Koszkul, Change in injection moulded parts shrinkage and weight as a function of processing conditions, J. of Materials Processing Technology, 162-163 (2005), 109-115
- [5] S.J. Liao et al., Optimal process conditions of shrinkage and warpage of thin-wall parts, Polymer Engineering and Sceince, 44 (2004), 917-928
- [6] C. Lotti, M.M. Ueki, R.E.S. Bretas, Prediction of the shrinkage of injection molded iPP Plaques using artificial neural networks, J. of Injection Molding Technology, 6 (2002), 157-176
- [7] L.M. Galantucci, R. Spina, Evaluation of filling conditions of injection moulding by integrating numerical simulations and experimental tests, J. of Materials Processing Technology, 141 (2003), 266-275
- [8] B. Ozcelik, T. Erzurumlu, Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm, Int. Com. in Heat and Mass Transfer, 32 (2005), 1085–1094
- [9] H.C.W. Lau et alii, Neural networks for the dimensional control of molded parts based on a reverse process model, J. of Materials Processing Technology, 117 (2001), 89-96
- [10] S.L. Mok, C.K. Kwong, Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding, J. of Intelligent Manufacturing, 13 (2002), 165-176
- [11] B.H.M. Sadeghi, A BP-neural network predictor model for plastic injection molding process, J. of Materials Processing Technology, 103 (2000), 411-416
- [12] K.E. Parsopoulos, M.N. Vrahatis, Recent approaches to global optimization problems through Particle Swarm Optimization, Natural Computing, 1 (2002), 235-306
- [13] P-Y. Yin, Particle swarm optimization for point pattern matching, J. of Visual Communication and Image Representation, (2005) Article in Press
- [14] I.C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, 85 (2003), 317-325
- [15] G.D. Magoulas, Global search strategies for simulation optimisation, Proc. of the 2002 Winter Simulation Conf., (2002), 1978-1985
- [16] S. Haykin, 1998, Neural network - A comprehensive foundation, Prentice Hall, USA
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7dfaa95a-c2af-4210-a270-6e3085c046d3