An Experimental Investigation of Resin Flow Sensing During Molding Processes

Kikuchi, Akihisa; Higuerey, Evelitsa; Coulter, John P.

doi:10.1115/1.2804376

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…This is supported by the fact that the neural-network-based intelligent control unit can make decisions in less than a second. Furthermore, the whole control unit was developed in total conformity with a joint sensing project being developed by Kikuchi et al (1994) such that, once the sensing scheme is successfully developed, the complete sensing plus control system would be ready to be implemented in the PC-based fully automated control of molding processes. Although the demonstrations were made on molding processes, the authors would like to note that the improved process development scheme that involves an intelligent control scheme combining a neural network with a search method can be successfully applied to any manufacturing process for which a process model and an in-situ sensing scheme exists.…”

Section: Discussionmentioning

confidence: 99%

“…Note that in some cases the predicted flow fronts and/or the actual flow fronts seemed to go outside the mold. The reason for this is that this method will later be used in real-time molding cases in conjunction with an in-situ sensing scheme being developed by Kikuchi et al (1994). Keeping this sensing scheme in mind, each flow front was represented by the xcoordinates of 18 points where the flow front intersects with 18 horizontal lines that are equally spaced in the ydirection across the widest section of the mold.…”

Section: Comparison Between Taguchi's Methods and The Factorial Designmentioning

confidence: 99%

“…The only reason that the capability to meet the third need was not demonstrated is that currently no reliable technique exists to monitor the flow progression in a closed mold cavity. However, in a related effort, Higuerey and coworkers are developing an in-situ sensing scheme for molding processes, which shows great potential of success (Kikuchi et aL, 1994), and the methodology outlined here is developed such that it can immediately incorporate that sensing scheme as soon as reliable sensing results are obtained.…”

Section: Meeting the First Two Of The Three Manufacturing Science Needsmentioning

confidence: 99%

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A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

Coulter

1996

J Intell Manuf

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The current investigation focused on neural-network-based control of manufacturing processes utilizing an optimization scheme. In an earlier study, Demirci and Coulter introduced the utilization of neural networks for the intelligent control of molding processes. In that study, a forward model neural network, employed with a search strategy based on the factorial design of experiments method, was shown to successfully control the flow progression during injection molding processes. Recently, Demirci et al. showed that the search mechanism based on the factorial design of experiments method can be intolerable in time during on-line control of manufacturing processes, and suggested an inverse model neural network. This inverse model neural network was shown to be beneficial as it totally eliminated time-consuming parameter searches, but it required a harder mapping than the forward model neural network and thus its performance was inferior. In the present study, the authors investigated two different optimization methods that were utilized in making the search method of the forward control scheme more efficient. The first method was Taguchi's method of parameter design, and the second method was a nonlinear optimization method known as Nelder and Mead's downhill simplex method. These two methods were separately utilized in creating an efficient search method to be used with the forward model neural network. The performance of the resulting two control methods was compared with each other as well as with that of the forward control scheme utilizing a search strategy based on the factorial design of experiments method. Although the applications in this study were on molding processes, the method can be applied to any manufacturing process for which a process model and an in-situ sensing scheme exists. 4-

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Section: Discussionmentioning

confidence: 99%

Section: Comparison Between Taguchi's Methods and The Factorial Designmentioning

confidence: 99%

Section: Meeting the First Two Of The Three Manufacturing Science Needsmentioning

confidence: 99%

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A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

Coulter

1996

J Intell Manuf

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“…Kikuchi et al investigated the sensing of the resin flow during molding processes using a modified SW system [8]. This system suffered from a low signal-to-noise (S/N) ratio and there was no attempt made to measure viscosity and degree of conversion based on the SW data.…”

Section: Previous Work On Ionic Conductivity Modelingmentioning

confidence: 99%

“…Ionic Conductivity (a) and Viscosity (T)) VS Glass Transition Temperature (TG) o& Ti = f(T G ) log(a T /aT 8 Figure 7. Schematic of continuum model.…”

Section: Jimentioning

confidence: 99%

Vinyl-Ester (VE) Cure Characterization Via Direct Current Sensors

Fink¹

2001

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.High-performance thermosetting composites typically consist of a high-modulus fibrous material embedded in a thermosetting polymer matrix. The behavior of the resulting composite depends on the properties of the reinforcement, the interphase, and the matrix. Vinyl-ester (VE) resins are relatively low-cost matrix resins used in liquid molding of large structures such as contiguous vehicle huUs. In liquid-molding processes, quality-control sensors can ensure resin impregnation of the preform and cure of the resin after infiltration. For these purposes, SMARTweave (SW), based on direct current (DC) sensing, is used as an in-situ flow-front detection and curemonitoring system. SW measures change in the ionic conductivity with cure, which can then be related to the material properties like viscosity and degree of cure. This enables in-situ measurement of degree of cure and viscosity development during cure from SW measurements. This report builds on previous modeling work that assumed a direct dependence of DC resistance on the resin viscosity, limiting the use of such models beyond gelation. This report presents a continuum model based on free volume theory to describe ionic conductivity through gelation to vitrification.

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Development of a Resin Transfer Molding Test‐bed with Process Control Capability

Raghavan

Паланки

2002

Dev. Chem. Eng. Mineral Process.

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~ ~~~ ~~~Fiber reinforced plastic parts have traditionally been manufactured by resin transfer molding. This method is based on a fixed recipe and no attempt is made to control process conditions during the manufacturing process. In this papel; an experimental test-bed is developed which has the capability of implementing a wide variety of temperature and pressure profiles for the manufacture of plastic parts via resin tranfer molding. The performance of the test-bed is evaluated by implementing several desired pressure and temperature profiles in production offiber reinforced polyester parts.

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An Experimental Investigation of Resin Flow Sensing During Molding Processes

Cited by 13 publications

References 7 publications

A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

Vinyl-Ester (VE) Cure Characterization Via Direct Current Sensors

Development of a Resin Transfer Molding Test‐bed with Process Control Capability

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