A hybrid global–local approach for optimization of injection gate locations in liquid composite molding process simulations

Henz, Brian J.; Mohan, Ram; Shires, Dale R.

doi:10.1016/j.compositesa.2007.03.005

Cited by 25 publications

(15 citation statements)

References 33 publications

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“…A refined algorithm, hybrid GA has been proposed for design improvement. A global-local approach for the optimization has been employed [13,14]. The hybrid approach used provides a global search with the GA and was subsequently further refined with a gradient-based search.…”

Section: Numerical Resultsmentioning

confidence: 99%

Influence of hollow glass microspheres on the mechanical and physical properties and cost of particle reinforced polymer composites

Aruniit¹,

Kers²,

Majak³

et al. 2012

Proc. Estonian Acad. Sci.

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The goal of the study was to fmd a cost-effective composition of a particle reinforced composite that is light in weight but has sufficient mechanical properties. The matrix of the particulate composite is unsaturated polyester resin that is reinforced with alumina trihydrate particles. Part of the alumina trihydrate proportion was replaced with hollow glass microspheres to reduce weight and save costs. In order to ftnd out the influence of the light ftUer on the physical and mechanical properties of composites, materials with different percentages of the light filler were prepared. Test specimens were cut from moulded sheets that were fabricated with vacuum assisted extruder. Tensile strength, indentation hardness measured with a Barcol impressor, and density were determined. Based on the experimental data a multi-criteria optimization problem was formulated and solved to find the optimal design of the material. Artificial neural networks and a hybrid genetic algorithm were used. The optimal solution is given as a Pareto curve to represent the distinction between the density and selected mechanical properties of the composite material. The composite material filled with 6% hollow glass microspheres showed 3% loss in the tensile strength and 26% loss in the surface hardness cornpared to the composition without the filler. The weight decreased by 13% compared with the initial composition. The addition of hollow glass microspheres did not lower the net value of the material, it increased 7%.

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Section: Numerical Resultsmentioning

confidence: 99%

Influence of hollow glass microspheres on the mechanical and physical properties and cost of particle reinforced polymer composites

Aruniit¹,

Kers²,

Majak³

et al. 2012

Proc. Estonian Acad. Sci.

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show abstract

“…While many studies have been conducted to optimize the number and positions of inlet gates in molds [18][19][20], a robust and effective procedure is still not available. The problem is rather complex, given the number of interconnected physical variables and the various possible tool configurations.…”

Section: State Of the Artmentioning

confidence: 99%

A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part I—Theory

et al. 2020

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The present article introduces a dimensionless number devised to assist composite engineers in the fabrication of continuous fiber composites by Liquid Composite Molding (LCM), i.e., by injecting a liquid polymer resin through a fibrous reinforcement contained in a closed mold. This dimensionless number is calculated by integrating the ratio of the injection pressure to the liquid viscosity over the cavity filling time. It is hereby called the “injectability number” and provides an evaluation of the difficulty to inject a liquid into a porous material for a given part geometry, permeability distribution, and position of the inlet gate. The theoretical aspects behind this new concept are analyzed in Part I of the article, which demonstrates the invariance of the injectability number with respect to process parameters like constant and varying injection pressure or flow rate. Part I also details how process engineers can use the injectability number to address challenges in composite fabrication, such as process selection, mold design, and parameter optimization. Thanks to the injectability number, the optimal position of the inlet gate can be assessed and injection parameters scaled to speed up mold design. Part II of the article completes the demonstration of the novel concept by applying it to a series of LCM process examples of increasing complexity.

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“…Computer-aided flow analysis with engineering software such as MPI (moldflow plastic insight) is now widely used to predict the injection molding results of given mold scheme and to optimize the mold design, most work simply evaluating the moldability of mold schemes from the quantitative analysis of certain product defects or some physical parameters during injection molding (Kabanemi et al, 2002;Courbebaisse and Garcia, 2002;Alam and Kamal, 2005;Sanchez et al, 2006;Yen et al, 2006;Henz et al, 2007). However, there may be various kinds of defects in injection-molded products, the seriousness of which can hardly be described with crisp variables.…”

Section: Introductionmentioning

confidence: 99%

Optimization of injection mold based on fuzzy moldability evaluation

Cheng

Feng

Tan

et al. 2008

Journal of Materials Processing Technology

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A hybrid global–local approach for optimization of injection gate locations in liquid composite molding process simulations

Cited by 25 publications

References 33 publications

Influence of hollow glass microspheres on the mechanical and physical properties and cost of particle reinforced polymer composites

Influence of hollow glass microspheres on the mechanical and physical properties and cost of particle reinforced polymer composites

A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part I—Theory

Optimization of injection mold based on fuzzy moldability evaluation

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