Optimization under uncertainty of a composite fabrication process using a deterministic one-stage approach

Acquah, Charles; Datskov, Ivan; Mawardi, A.; Zhang, Feng; Achenie, Luke E.K.; Pitchumani, R.; Santos, Eunice E.

doi:10.1016/j.compchemeng.2005.12.015

Cited by 24 publications

(13 citation statements)

References 27 publications

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“…Parameters of cure kinetics models and cure temperature were assumed to be random with coefficient of variation in the range of 1.5% -5%, and it was found that uncertainty indu ces significant variations in cure completion times. Furthermore, incorporation of uncertainty affects the results of cure optimisation [18,19]. The effect of the variability of relevant material properties on the dimensions of produced components has also been found to be significant [20].…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulation of woven composites forming

Skordos

Sutcliffe

2008

Composites Science and Technology

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Section: Introductionmentioning

confidence: 99%

Stochastic simulation of woven composites forming

Skordos

Sutcliffe

2008

Composites Science and Technology

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“…Using these techniques often requires concurrent use of thermomechanical and consolidation models describing the laminate fabrication, which are often solved by numerical methods (Bogetti and Gillespie, 1991;Loos and Springer, 1983;Mawardi and Pitchumani, 2003). The uncertainty associated with the cure parameters, on the other hand, has been incorporated into the optimization process using stochastic, deterministic, and parametric programming (Acquah et al, 2006). In order to alleviate or reduce the high level of computational resources needed for these numerical models, Rai and Pichumani (1997b;c) proposed the use of artificial neural networks for cure process modeling.…”

Section: Reduction Of Cure Induced Defects In Lcmmentioning

confidence: 99%

Process Induced Defects in Liquid Molding Processes of Composites

Hamidi

Altan

2017

International Polymer Processing

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Liquid Composite Molding (LCM) processes are cost efficient manufacturing alternatives to traditional autoclave technology for producing near-net shape structural composite parts. However, process induced defects often limit wider usage of LCM in structural applications. Thorough knowledge of these defects, as well as their formation mechanisms and prevention techniques, is essential in developing improved LCM processes. In this article, process induced defects in liquid molding processes of composites, categorized into preform, flow induced and cure induced defects, are reviewed. Preform defects are further presented as fiber misalignment and fiber undulation (waviness and wrinkling). The respective causes, detrimental effects, and possible prevention methods of these defects are presented. Thereafter, flow induced defects are classified as voids and dry spots. Dry spot formation mechanisms in LCM processes and available prevention techniques are summarized. In addition, void formation mechanisms, adverse effects on composite properties, and removal techniques are presented. Cure induced defects include microcracks, void growth and geometrical distortions (warpage and spring-in). Each of these defects are discussed along with their underlying causes as well as their control and reduction schemes.

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“…The surface heat transfer and tool temperature variability cause significant variability in cure time reaching a coefficient of variation of approximately 20% [6]. Tool temperature variability has the greatest influence on process outcome [7] whilst, higher levels of uncertainty increase the optimal cure time [8].…”

Section: Introductionmentioning

confidence: 99%