scholarly journals Real time uncertainty estimation in filling stage of resin transfer molding process

2020 ◽  
Vol 41 (12) ◽  
pp. 5387-5402 ◽  
Author(s):  
K. I. Tifkitsis ◽  
A. A. Skordos
Keyword(s):  
Real Time ◽  
Resin Transfer Molding ◽  
Uncertainty Estimation ◽  
Molding Process ◽  
Transfer Molding ◽  
Time Uncertainty ◽  
Filling Stage

On-Line Characterization of Bulk Permeability and Race-Tracking During the Filling Stage in Resin Transfer Molding Process

2003 ◽  
Vol 37 (17) ◽  
pp. 1525-1541 ◽  
Author(s):  
Mathieu Devillard ◽  
Kuang-Ting Hsiao ◽  
Ali Gokce ◽  
Suresh G. Advani
Keyword(s):  
Resin Transfer Molding ◽  
Molding Process ◽  
Transfer Molding ◽  
Filling Stage ◽  
On Line

scholarly journals Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP

2019 ◽  
Vol 9 (9) ◽  
pp. 1795 ◽  
Author(s):  
Kim ◽  
Kim ◽  
Hwang ◽  
Kim
Keyword(s):  
High Pressure ◽  
Real Time ◽  
Process Monitoring ◽  
High Speed ◽  
Resin Transfer Molding ◽  
Time Signal ◽  
Process Variables ◽  
Molding Process ◽  
Transfer Molding ◽  
Rtm Process

Carbon Fiber Reinforced Plastics (CFRP) is a material developed for its high strength and light weight in a broad variety of industries including aerospace, automotive, and leisure. Due to the rapid molding cycle time, high-pressure resin transfer molding (HP-RTM) processes are prone to molding defects and susceptible to various process variables such as the resin injection rate, pressure and temperature in the mold, vacuum, end-gap, pressing force, and binder. In recent years, process monitoring technology with various sensors has been applied to stabilize the HP-RTM process and control process variables. The field-programmable gate array (FPGA) based embedded monitoring system proposed in this study enabled high-speed real-time signal processing with multiple sensors, namely pressure, temperature, and linear variable differential transformer (LVDT), and proved feasibility in the field. In the HP-RTM process, the impregnation and curing of the resin were predicted from the cavity pressure and temperature variations during the injection and curing stages. In addition, the thickness of the CFRP specimen was deduced from the change in the end-gap through the detection of the LVDT signal. Therefore, the causes of molding defects were analyzed through process monitoring and the influence of molding defects on the molding quality of CFRP was investigated.

Flow Characteristics of Vacuum Assisted Resin Transfer Molding Process Depending on the Capillary Phenomenon

2013 ◽  
Vol 762 ◽  
pp. 612-620 ◽  
Author(s):  
Yun Hae Kim ◽  
Jin Woo Lee ◽  
Jun Mu Park
Keyword(s):  
Low Cost ◽  
Resin Transfer Molding ◽  
Flow Characteristics ◽  
Molding Process ◽  
Transfer Molding ◽  
Filling Stage ◽  
Resin Impregnation ◽  
Movement Mechanism ◽  
Capillary Phenomenon ◽  
Vartm Process

Reducing the cost of composite material production is significant for expanding its usage and application in many ways, such as in the fields of aerospace, aviation, ocean industry and so on. To do this, It is important to minimize the production process of the material and to decrease the amount of scraps or any unnecessary particles. The Vacuum Assisted Resin Transfer Molding (VARTM) process, which is known for having many advantages, has become recognized as one of the most low-cost manufacturing model. VARTM process can be divided into three main steps: performing, resin filling and hardening steps. The most important step among all these three steps is the Resin Filling stage, a process when resin is impregnated into the mat. Mostly, Resin Filling stage is greatly affected by the level of permeability, a characteristic of stiffener due to pneumatic resistant nature in the process. Other factors such as viscosity, technological vacuuming, or even stiffening process itself could also influence the production as well. During Resin Filling stage, Resin tends to spread out in the center first because of capillary phenomenon. In this research, the researchers examined the mechanical property and the pneumatic nature of Resin by dividing the pneumatic movement of the Resin into sections. Based on this result, the researchers found the correlations between the capillary phenomenon and Resin impregnation, and analyzed the movement mechanism in Resin filling stage.

scholarly journals A Conceptional Approach of Resin-Transfer-Molding to Rosin-Sourced Epoxy Matrix Green Composites

Aerospace ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 5
Author(s):  
Sicong Yu ◽  
Xufeng Zhang ◽  
Xiaoling Liu ◽  
Chris Rudd ◽  
Xiaosu Yi
Keyword(s):  
Composite Laminates ◽  
Resin Transfer Molding ◽  
High Viscosity ◽  
Ramie Fiber ◽  
Liquid Composite Molding ◽  
Curing Agent ◽  
Molding Process ◽  
Transfer Molding ◽  
Low Viscosity ◽  
Composite Molding

In this concept-proof study, a preform-based RTM (Resin Transfer Molding) process is presented that is characterized by first pre-loading the solid curing agent onto the preform, and then injecting the liquid nonreactive resin with an intrinsically low viscosity into the mold to infiltrate and wet the pre-loaded preform. The separation of resin and hardener helped to process inherently high viscosity resins in a convenient way. Rosin-sourced, anhydrite-cured epoxies that would normally be regarded as unsuited to liquid composite molding, were thus processed. Rheological tests revealed that by separating the anhydrite curing agent from a formulated RTM resin system, the remaining epoxy liquid had its flowtime extended. C-scan and glass transition temperature tests showed that the preform pre-loaded with anhydrite was fully infiltrated and wetted by the liquid epoxy, and the two components were diffused and dissolved with each other, and finally, well reacted and cured. Composite laminates made via this approach exhibited roughly comparable quality and mechanical properties with prepreg controls via autoclave or compression molding, respectively. These findings were verified for both carbon and ramie fiber composites.

Development of resin transfer molding process using scaling down strategy

2013 ◽  
Vol 35 (9) ◽  
pp. 1683-1689 ◽  
Author(s):  
Raghu Raja Pandiyan Kuppusamy ◽  
Swati Neogi
Keyword(s):  
Resin Transfer Molding ◽  
Molding Process ◽  
Transfer Molding ◽  
Scaling Down

scholarly journals Resin transfer molding process: a numerical and experimental investigation

2013 ◽  
Vol 7 (2) ◽  
pp. 125-136 ◽  
Author(s):  
Iran de Oliveira ◽  
Sandro Amico ◽  
Jeferson Souza ◽  
Antonio de Lima
Keyword(s):  
Experimental Investigation ◽  
Resin Transfer Molding ◽  
Molding Process ◽  
Transfer Molding

Neural Network Based Control of Preform Permeation in Resin Transfer Molding Processes With Real-Time Permeability Estimation

2000 ◽  
Author(s):  
David Nielsen ◽  
Ranga Pitchumani
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Resin Transfer Molding ◽  
Local Pressure ◽  
Transfer Molding ◽  
Model Based ◽  
Rtm Process ◽  
Virtual Sensing ◽  
Intelligent Model ◽  
Injection Pressures

Abstract Variabilities in the preform structure in situ in the mold are an acknowledged challenge to effective permeation control in the Resin Transfer Molding (RTM) process. An intelligent model-based controller is developed which utilizes real-time virtual sensing of the permeability to derive optimal decisions on controlling the injection pressures at the mold inlet ports so as to track a desired flowfront progression during resin permeation. This model-based optimal controller employs a neural network-based predictor that models the flowfront progression, and a simulated annealing-based optimizer that optimizes the injection pressures used during actual control. Preform permeability is virtually sensed in real-time, based on the flowfront velocities and local pressure gradient estimations along the flowfront. Results are presented which illustrate the ability of the controller in accurately steering the flowfront for various fill scenarios and preform geometries.

Real-time Uncertainty Estimation Based On Intermediate Layer Variational Inference

2021 ◽  
Author(s):  
Ahmed Hammam ◽  
Seyed Eghbal Ghobadi ◽  
Frank Bonarens ◽  
Christoph Stiller
Keyword(s):  
Real Time ◽  
Intermediate Layer ◽  
Uncertainty Estimation ◽  
Variational Inference ◽  
Time Uncertainty
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