Volume 114, Issue 1 pp. 358-368

Multiobjective optimization of molded LDPE foams characteristics using genetic algorithm

S. Chedly

Corresponding Author

S. Chedly

IFTS-LEMPE, 7, Boulevard Jean Delautre, 08000 Charleville-Mézières, France

IFTS-LEMPE, 7, Boulevard Jean Delautre, 08000 Charleville-Mézières, France===Search for more papers by this author
A. Chettah

A. Chettah

Ecole Centrale de Lyon-LTDS, 36, Avenue Guy de Collongue, 69134 Ecully Cedex, France

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M. N. Ichchou

M. N. Ichchou

Ecole Centrale de Lyon-LTDS, 36, Avenue Guy de Collongue, 69134 Ecully Cedex, France

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First published: 08 June 2009
Citations: 5

Abstract

This article concerns the injection manufacturing process of molded foam sheets and their intrinsic shock and noise performances. The main goal is to optimize the physical performances of molded plastic foams at an early stage in their design and manufacturing. The effects of injection process parameters on the properties of molded LDPE foams are investigated. The input optimization parameters considered are as follows: injection temperature, mold temperature, injection speed, plasticization back pressure, and screw rotation speed during the plasticization phase. The output optimization parameters considered are as follows: density, shock absorption, and acoustic absorption. The experimental design method made use of the central composition design. This allows us to identify simplified mathematical models for input/output and to detect the most influential input in the injection process. Ultimately, models are used to carry out multiobjective optimization of injected foams characteristics in the presence of a few constraints on decision variables. This optimization is done using a very robust technique, NSGA-II. Several two-objective functions involving sometimes the maximization and other times minimization of foam characteristics have been studied to illustrate the procedures and explain and interpret the results obtained. One needs to solve several simpler optimization problems with just one or two decision variables (smaller amount of freedom), to gain insight and to provide help in formulating the more general multiobjective optimization problem. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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