Publications

2015

1. AISTATS

   Statistical Analysis of Uncertainties in Deterministic Computational Modeling – Application to Composite Process Resin Infusion Flow Model

   Kelkar, V. A., Mohan, R. V., Shiferaw, H., and Kelkar, A. D.

   Communications in Statistics - Simulation and Computation 2015

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   Deterministic physics-based flow modeling provides an effective way to simulate and understand the resin flow infusion process in liquid composite molding processes and its variants. These are effective to provide optimal injection time and locations prior to gelation for given process parameters of resin viscosity and preform permeability. However, there could be significant variations in these two parameters during actual manufacturing. This paper presents simulation-based statistical analysis of uncertainties of these process parameters involved in the resin flow infusion. Two key process parameters, viscosity and permeability, and their statistical variations are examined individually and subsequently in combination for their impact on the associated injection time. Values from statistical probability distribution of the process parameters were employed to find the solution space for this engineering application through deterministic physics-based process flow modeling simulations. A bivariate confidence envelope was developed using the appropriate Cumulative Density Function for a 95% probability of successfully completing resin infusion prior to physical resin gelation time. A logistic regression model for the influence of resin viscosity and permeability on the binary response of successful resin infusion is presented and conforms well to the sensitivity analysis inferences.

2012

1. ACSSC

   Probabilistic Analysis of Property Uncertainties Using Resin Infusion Flow Modeling and Simulations – Resin Viscosity And Preform Permeability

   Shiferaw, H.

   Dec 2012

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   For LCM processes modeling and optimization substantial amount of research and work has been done. Several researches have been done based one-dimensional mold filling models, thin-shell molds, and three-dimensional models that simulate the mold filling, and heat transfer and curing stages. The objective of this work is to study the two key parameters: preform permeability and resin viscosity in liquid composite molding. This thesis research involves several tasks: develop different kind of an integrated LCM simulation using simulation software such as ANSYS, develop statistical characterization for viscosity and permeability data value by using statistical software SPSS, and develop design and solution of LCM process with simulation and process parameters. Also research involved in development of the integrated design environment with ANSYS, C++, SPSS, and Tec-plot. The viscosity and permeability data were characterized using statistical methods and the stochastic properties distribution type and parameters. Based on the assumption that viscosity and permeability data values distribute statistically (normal distribution), optimal tooling design was simulated. In this thesis research, robust tooling design was conducted focusing on the two important parameters; viscosity and permeability, so that the process was insensitive to the viscosity and permeability variation for producing consistent part in a minimal time and quality.