Uncertainty in the manufacturing of fibrous thermosetting composites: A review

Mesogitis, Tassos; Skordos, Alexandros A.; Long, A.C.

doi:10.1016/j.compositesa.2013.11.004

Cited by 172 publications

(113 citation statements)

References 80 publications

(174 reference statements)

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“…These geometrical distortions often result in costly rejection of the part (Brauner et al, 2017;Mesogitis et al, 2014). Part distortion is inevitably induced by preform asymmetry, prevented in practice by the consistent use of symmetrical preform architectures.…”

Section: Distortions: Warpage and Spring-inmentioning

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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Section: Distortions: Warpage and Spring-inmentioning

confidence: 99%

Process Induced Defects in Liquid Molding Processes of Composites

Hamidi

Altan

2017

International Polymer Processing

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“…Mesogitis et al [4] presented a review about the multiple sources of uncertainty associated with material properties and boundary conditions. In this work, the authors presented numerical and experimental results concerning the statistical characterization and influence of uncertain inputs on the main steps of the manufacturing process of composites, including defects induced by the process itself.…”

Section: Introductionmentioning

confidence: 99%

Assessing Static and Dynamic Response Variability due to Parametric Uncertainty on Fibre-Reinforced Composites

2018

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Abstract:Composite structures are known for their ability to be tailored according to specific operating requisites. Therefore, when modelling these types of structures or components, it is important to account for their response variability, which is mainly due to significant parametric uncertainty compared to traditional materials. The possibility of manufacturing a material according to certain needs provides greater flexibility in design but it also introduces additional sources of uncertainty. Regardless of the origin of the material and/or geometrical variabilities, they will influence the structural responses. Therefore, it is important to anticipate and quantify these uncertainties as much as possible. With the present work, we intend to assess the influence of uncertain material and geometrical parameters on the responses of composite structures. Behind this characterization, linear static and free vibration analyses are performed considering that several material properties, the thickness of each layer and the fibre orientation angles are deemed to be uncertain. In this study, multivariable linear regression models are used to model the maximum transverse deflection and fundamental frequency for a given set of plates, aiming at characterizing the contribution of each modelling parameter to the explanation of the response variability. A set of simulations and numerical results are presented and discussed.

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“…The process of cure involves several sources of variability including environmental/boundary conditions uncertainty and material properties variations [16]. Stochastic simulation has shown that amongst these, tool temperature has the greatest impact on cure time variability [17].…”

Section: Introductionmentioning

confidence: 99%