Alejandro J. Rodriguez

A methodology to determine permeability distribution of a preform in resin transfer molding process

This article presents a scheme that directly calculates permeability distribution of a preform during the RTM process. The measured filling front locations as well as the corresponding inlet conditions are used in the proposed scheme to calculate the permeability distribution that also takes into account race-tracking phenomenon. The proposed scheme employs a numerical optimization algorithm to minimize a cost function that leads to the permeability field of the preform. Time-step-independent RTM filling algorithm is utilized as a computational kernel to generate the cost function for the subsequent minimization. Numerical development of the proposed scheme is discussed in this article. In addition, the proposed scheme is applied to several test problems involving a variety of spatial permeability distribution of the preform including a situation that involves race-tracking phenomena.

Optimization of Spine Sensor Location in Resin Ttransfer Molding

Resin Transfer Molding (RTM) is a manufacturing process to produce polymer composite parts. RTM is comprised of four stages: 1) cutting and placing of the fiber mats (preform) inside a mold, 2) resin injection, 3) curing of the part, and 4) demolding of the hardened part. Resin injection is the most critical stage in RTM and it can be affected by unpredictable parameters such as preform permeability variations. These variations can produce unrepeatable filling patterns where the Last Point to Fill (LPF) may not coincide with the exit vent location. Failure to completely wet the fibers inside the mold can cause dry spots which are major defects that usually require the part to be scrapped. In order to overcome the uncertainties in the filling stage, adaptive control can be used to monitor and regulate the flow front such that the LPF coincides with the vent location. Recently, the development of sensors has allowed continuous sensing of the flow front in a straight line. Such sensors can be placed between the injection gates and the vent. The location of these sensors can affect adaptive control and the resulting filling pattern and, therefore, the final quality of the part. The work presented in this paper uses a search algorithm to find the optimal location for the sensors. The results of this optimization study can be used to enhance future control algorithms and, therefore, can lead to a more successful RTM process.Copyright