A Novel Approach to Monitoring the Curing of Epoxy in Closed Tools by Use of Ultrasonic Spectroscopy

Pommer, Christian; Sinapius, Michael

doi:10.3390/s18010096

Cited by 12 publications

(22 citation statements)

References 6 publications

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“…Additionally, comparative studies with a second ultrasonic cure monitoring system that is being investigated within the authors’ research group will be performed. Pommer et al [ 24 ] analyze an alternative cure monitoring technique that uses a resonant based technique instead of a pulse ultrasonic measurement. A comparative study is under development that will give further insights into the ultrasonic monitoring of the pultrusion process.…”

Section: Discussionmentioning

confidence: 99%

Pulse Ultrasonic Cure Monitoring of the Pultrusion Process

Scholle

Sinapius

2018

Sensors

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This article discusses the results of a series of experiments on pulse ultrasonic cure monitoring of carbon fiber reinforced plastics applied to the pultrusion process. The aim of this study is to validate the hypothesis that pulse ultrasonic cure monitoring can be applied (a) for profiles having small cross sections such as 7 mm × 0.5 normalmnormalm and (b) within the environment of the pultrusion process. Ultrasonic transducers are adhesively bonded to the pultrusion tool as actuators and sensors. The time-of-flight and the amplitude of an ultrasonic wave are analyzed to deduce the current curing state of the epoxy matrix. The experimental results show that ultrasonic cure monitoring is indeed applicable even to very thin cross sections. However, significant challenges can be reported when the techniques are used during the pultrusion process.

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Section: Discussionmentioning

confidence: 99%

Pulse Ultrasonic Cure Monitoring of the Pultrusion Process

Scholle

Sinapius

2018

Sensors

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“…The amplitude of those resonances are directly related to the current state of cure and can be measured via an additional sensor. This method is described in greater detail in [2]. …”

Section: Introductionmentioning

confidence: 99%

“…Although the fundamentals of this technique for cure monitoring in industrial processes was established in [2,5], there are still several challenges that have to be addressed. An especially troublesome challenge is the lack of sophisticated sensors and sensor design guidelines for RUS cure monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…There are a number of recent articles that deal with ultrasound sensor design, such as [6,7], but they do not take the response function of the tool into account. While the principle itself allows for the use of crude sensors, for example two piezo ceramics applied to the tool surface as shown in Figure 1, to measure the curing of epoxies, as shown in [2], they are prone to acoustic reflections and do not fully incorporate a possible concentric eigenmode induced by the RUS.…”

Section: Introductionmentioning

confidence: 99%

“…There are in total three papers that use resonant ultrasonic spectroscopy to detect the curing of adhesives [2,5,8]. None of those articles analyzes the near-field behavior to optimize a specific sensor design.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Design Optimization for Ultrasonic Spectroscopy Cure Monitoring

Pommer

Sinapius

2018

Sensors

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In the field of cure monitoring, resonant ultrasonic cure monitoring is a unique technique to measure the progression of cure of composites in fully or partially closed tools. It allows for the use of electronic hardware that is less sophisticated than traditional pulse-based ultrasonic systems to obtain accurate results. While this technique is not new, it has been used very rarely. One reason for this is the lack of optimized sensors. Commercially available sensors are optimized for pulse-based ultrasonic testing. This paper establishes a possible optimized sensor design for resonant ultrasound cure monitoring using a multi-parameter FE model.

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Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning

Rabby,

Das,

Rahman

et al. 2023

Polymer Composites

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Fiber‐reinforced polymer (FRP) composites are widely used in the aerospace and automotive industries due to their high strength‐to‐weight ratio. However, the manufacturing process of FRP composites is intricate, requiring precise control over multiple parameters, which can be challenging. To ensure top‐notch product quality and bolster confidence in the durability of composite materials, it requires a uniform curing evaluation technique and a predictive strength model. This study presents a rapid nondestructive quality inspection technique for composites, involving three distinct studies that employ machine learning techniques in combination with the frequency‐dependent dielectric response of materials. The first study introduces a nondestructive method for swiftly inspecting the cure state (degree of cure) of composite samples. This technique combines broadband dielectric spectroscopy with supervised machine learning algorithms, particularly support vector machines. The second study employs advanced artificial neural networks like multi‐layer perceptron to predict the tensile strength, a measure of mechanical performance, of composite materials. The results demonstrate an accurate classification of the curing state with 96.7% accuracy and a prediction of tensile strength with 87.5% accuracy. The final study explores the application of machine learning in quality monitoring of prepreg (raw materials for FRP) aging at room temperature.Highlights Integration of machine learning with frequency‐dependent dielectric measurement offers efficient method for inspecting and monitoring the curing state and mechanical performance of composite materials. Machine learning and frequency‐dependent dielectric response used to accurately assess the curing state of fiber‐reinforced polymer composites with 96.7% accuracy, which saves the time and effort and also reduces the need for destructive testing. Advanced artificial neural networks, specifically multi‐layer perceptron, employed to predict mechanical performance of composite materials with 87.5% accuracy, enhancing confidence in product reliability. Novel dielectric measurement technique integrated with machine learning algorithms enables prepreg age monitoring at room temperature.

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A Novel Approach to Monitoring the Curing of Epoxy in Closed Tools by Use of Ultrasonic Spectroscopy

Cited by 12 publications

References 6 publications

Pulse Ultrasonic Cure Monitoring of the Pultrusion Process

Pulse Ultrasonic Cure Monitoring of the Pultrusion Process

Sensor Design Optimization for Ultrasonic Spectroscopy Cure Monitoring

Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning

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