Process Modelling of Induction Welding for Thermoplastic Composite Materials By Neural Networks

2019 ◽  
Author(s):  
Hao Guo ◽  
Jaspreet Pandher ◽  
Michael van Tooren ◽  
Song Wang
Keyword(s):  
Neural Networks ◽  
Composite Materials ◽  
Process Modelling ◽  
Thermoplastic Composite

Modeling The Microwave Frequency Dielectric Properties of Thermoplastic Composite Materials

1992 ◽  
Vol 269 ◽  
Author(s):  
Mitchell L. Jackson ◽  
Curtis H. Stern
Keyword(s):  
Composite Materials ◽  
Dielectric Properties ◽  
Volume Fraction ◽  
Perturbation Technique ◽  
Resonant Cavity ◽  
Microwave Frequency ◽  
Thermoplastic Composite ◽  
Fiber Volume ◽  
Thermoplastic Matrix ◽  
Rotation Procedure

ABSTRACTMixture models were studied in an effort to predict the microwave frequency permittivities of unidirectional-fiber-reinforced thermoplastic-matrix composite materials as a function of fiber volume fraction, fiber orientation relative to the electric field, and temperature. The permittivities of the constituent fiber and plastic materials were measured using a resonant cavity perturbation technique at 9.4 GHz and at 2.45 GHz. The permittivities of the composite specimens were measured using a reflection cavity technique at 9.4 GHz and at 2.45 GHz. Simple “rule-of-mixtures” models that use the fiber and plastic permittivities have been found to approximate the complex dielectric properties of the composite for varied fiber volume fractions. The permittivities of oriented composites were modeled using a tensor rotation procedure. Composite permittivities were modeled with temperature up to the glass transition temperature of the thermoplastic matrix.

scholarly journals Development of thermoplastic composite materials based on modified polypropylene

2021 ◽  
pp. 145-149
Author(s):  
D.O. Chervakov ◽  
◽  
O.S. Sverdlikovska ◽  
O.V. Chervakov ◽  
◽  
...  
Keyword(s):  
Tensile Strength ◽  
Composite Materials ◽  
Carbon Fibers ◽  
Thermophysical Properties ◽  
Reactive Extrusion ◽  
Substantial Improvement ◽  
Thermoplastic Composite ◽  
Basalt Fibers ◽  
Reinforcing Fillers ◽  
Reinforcing Fibers

To improve the physical-mechanical and thermophysical properties of polypropylene-based thermoplastic composite materials, we performed modification of a polymer matrix by reactive extrusion of polypropylene in the presence of benzoyl peroxide and polysiloxane polyols. Modified polypropylene was compounded with basalt, carbon, and para-aramide reinforcing fillers in a screw-disc extruder. It was established that the reinforcement of modified polypropylene by basalt fibers ensured a 110% increase in tensile strength. The reinforcement of modified polypropylene by carbon fibers allowed fabricating thermoplastic composite materials with tensile strength increased by 14%. The maximum reinforcing effect was observed by using para-aramide fibers as reinforcing fibers for modified polypropylene with tensile strength increased by 30% as compared with initial polypropylene. It was determined that the obtained thermoplastic composite materials based on modified polypropylene can be processed into products by the most productive methods (extrusion and injection molding). The developed materials exhibited improved thermal stability. The proposed ways of modification methods provide substantial improvement in physical-mechanical and thermophysical properties of modified polypropylene-based thermoplastic composite materials as compared with initial polypropylene. In addition, they ensure a significant increase in service properties of the products prepared from thermoplastic composite materials based on modified polypropylene.

scholarly journals Damage detection in laminar thermoplastic composite materials by means of embedded optical fibers

2006 ◽  
Vol 60 (7-8) ◽  
pp. 176-179
Author(s):  
Aleksandar Kojovic ◽  
Irena Zivkovic ◽  
Ljiljana Brajovic ◽  
Dragan Mitrakovic ◽  
Radoslav Aleksic
Keyword(s):  
Composite Materials ◽  
Optical Fiber ◽  
Real Time ◽  
Optical Fibers ◽  
Impact Damage ◽  
Experimental Testing ◽  
Low Energy ◽  
Thermoplastic Composite ◽  
Woven Fabric ◽  
The Impact

This paper investigates the possibility of applying optical fibers as sensors for investigating low energy impact damage in laminar thermoplastic composite materials, in real time. Impact toughness testing by a Charpy impact pendulum with different loads was conducted in order to determine the method for comparative measurement of the resulting damage in the material. For that purpose intensity-based optical fibers were built in to specimens of composite materials with Kevlar 129 (the DuPont registered trade-mark for poly(p-phenylene terephthalamide)) woven fabric as reinforcement and thermoplastic PVB (poly(vinyl butyral)) as the matrix. In some specimens part of the layers of Kevlar was replaced with metal mesh (50% or 33% of the layers). Experimental testing was conducted in order to observe and analyze the response of the material under multiple low-energy impacts. Light from the light-emitting diode (LED) was launched to the embedded optical fiber and was propagated to the phototransistor-based photo detector. During each impact, the signal level, which is proportional to the light intensity in the optical fiber, drops and then slowly recovers. The obtained signals were analyzed to determine the appropriate method for real time damage monitoring. The major part of the damage occurs during impact. The damage reflects as a local, temporary release of strain in the optical fiber and an increase of the signal level. The obtained results show that intensity-based optical fibers could be used for measuring the damage in laminar thermoplastic composite materials. The acquired optical fiber signals depend on the type of material, but the same set of rules (relatively different, depending on the type of material) could be specified. Using real time measurement of the signal during impact and appropriate analysis enables quantitative evaluation of the impact damage in the material. Existing methods in most cases use just the intensity of the signal before and after the impact, as the measure of damage. This method could be used to monitor the damage in real time, giving warnings before fatal damage occurs.

Enhanced Infrared Heating of Thermoplastic Composite Sheets for Thermoforming Processes

2021 ◽  
Vol 36 (1) ◽  
pp. 35-43
Author(s):  
M. Längauer ◽  
G. Zitzenbacher ◽  
C. Burgstaller ◽  
C. Hochenauer
Keyword(s):  
Steady State ◽  
Composite Materials ◽  
Mechanical Performance ◽  
Heating Time ◽  
Thermoplastic Composite ◽  
Infrared Heating ◽  
Thermoplastic Composites ◽  
Steady State Condition ◽  
Reflector System ◽  
Heating Station

Abstract Thermoforming of thermoplastic composites attracts increasing attention in the community due to the mechanical performance of these materials and their recyclability. Yet there are still difficulties concerning the uniformity of the heating and overheating of parts prior to forming. The need for higher energy efficiencies opens new opportunities for research in this field. This is why this study presents a novel experimental method to classify the efficiency of infrared heaters in combination with different thermoplastic composite materials. In order to evaluate this, different organic sheets are heated in a laboratory scale heating station until a steady state condition is reached. This station mimics the heating stage of an industrial composite thermoforming device and allows sheets to slide on top of the pre-heated radiator at a known distance. By applying thermodynamic balances, the efficiency of chosen parameters and setups is tested. The tests show that long heating times are required and the efficiency of the heating is low. Furthermore, the efficiency is strongly dependent on the distance of the heater to the sheet, the heater temperature and also the number of heating elements. Yet, using a full reflector system proves to have a huge effect and the heating time can be decreased by almost 50%.

Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

2020 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Data Fusion ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Mixed Data ◽  
Image Encoding ◽  
Using Data

Abstract Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

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