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Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm

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Abstract

This paper deals with minimization of sink depths in injection-molded thermoplastic components by integrating finite element (FE) flow analysis with central composite design (CCD) of experiments and genetic algorithm (GA). Sink-mark depth depends on various process and design variables. Out of all, four most influential variables viz. melt temperature, mold temperature, pack pressure, and rib-to-wall ratio were used for optimization. A set of FE analyses were conducted at various combinations of variables based on the CCD array. A second-order-response surface regression model (RSRM) was developed based on the CCD. The second-order model was effectively coupled with GA for optimization of variables to minimize the sink depth. Results are encouraging and the proposed methodology could be used effectively in minimizing sink-mark depths.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Anna University, Chennai, India

    D. Mathivanan & N. S. Parthasarathy

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  1. D. Mathivanan
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  2. N. S. Parthasarathy
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Correspondence to D. Mathivanan.

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Mathivanan, D., Parthasarathy, N.S. Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm. Int J Adv Manuf Technol 45, 867–874 (2009). https://doi.org/10.1007/s00170-009-2021-z

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  • DOI: https://doi.org/10.1007/s00170-009-2021-z

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