The application of PEEK in stainless steel fiber and carbon fiber reinforced composites

Fu, Hua; Liao, Bo; Qi, Fangjuan; Sun, Baochen; Liu, Aiping; Ren, Dengfeng

doi:10.1016/j.compositesb.2007.09.003

Cited by 42 publications

(18 citation statements)

References 9 publications

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“…The composites developed based on PEEK resin show better heat stability and no fading at 350°C. According to detailed discussion and SEM morphology observation in earlier publications ( Ref 18,19), the predominant wear mechanisms of composites have been ascribed to abrasion and adhesion. Adherence straps can increase friction coefficient and maintain it steadily.…”

Section: Properties and Abrasion Mechanismmentioning

confidence: 98%

Abrasion Mechanism of Stainless Steel/Carbon Fiber-Reinforced Polyether-Ether-Ketone (PEEK) Composites

Zhang

et al. 2009

J. of Materi Eng and Perform

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A new type of composite material, stainless steel fiber and carbon fiber-reinforced polyether-ether-ketone (PEEK), was investigated to study its friction and wear properties and mechanisms. The friction materials containing 11 ingredients were hot-pressed and tested using a pad-on-disc type wear tester under unlubricated sliding friction and wear conditions at a constant sliding speed. The worn surface morphology was observed by Field-emission Scanning Electron Microscope (FE-SEM) and Atomic Force Microscope (AFM). The role of transfer film was studied using X-ray photoelectron spectroscopy (XPS) to investigate the thermal decomposition of the friction material. The fade ratio of the composites was only 4.8%, the recovery ratio 107%, and the total wear ratio was as low as 0.99 3 10 27 cm 3 (N m) 21 , showing the perfect properties of heat stability and recovery, as well as high antiwear ability of the composites. Adherence abrasion and particle abrasion take place at higher temperature. A transfer film is formed, which may improve abrasive resistant performance to get stable friction coefficient and low abrasion value for composite friction materials.

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Section: Properties and Abrasion Mechanismmentioning

confidence: 98%

Abrasion Mechanism of Stainless Steel/Carbon Fiber-Reinforced Polyether-Ether-Ketone (PEEK) Composites

Zhang

et al. 2009

J. of Materi Eng and Perform

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“…However, so far single polymer material could hardly satisfy these requests. Therefore, various fibers are incorporated into a kind of polymer matrix material to enhance the friction and wear properties, which are receiving considerable attention for applications in severe space conditions [8][9][10][11]. Various high-performance modern fibers have been used as filler for the reinforced polymers, and inorganic carbon fibers (CF) and organic aramid fibers (AF) that own excellent mechanical properties and favorable dispersion in polymers are the main candidates [12].…”

Section: Introductionmentioning

confidence: 99%

Friction and wear behaviors of carbon and aramid fibers reinforced polyimide composites in simulated space environment

Zheng

Wang

et al. 2015

Tribology International

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“…Therefore, the present work is focused on the application of ANN on prediction of formulation and properties of stainless steel/carbon fiber reinforced PEEK composites developed in our early research [3] . A back propagation neural network (BPNN) was developed, i.e.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…Another pioneering works in this research field has been performed on the ANN-modeling and ANN-prediction of the wear properties of short fiber composites [1,2] . The major advantage of neural networks is their possibility to predict dependencies between many parameters, and to apply them to any given situation in a "black box" fashion.Therefore, the present work is focused on the application of ANN on prediction of formulation and properties of stainless steel/carbon fiber reinforced PEEK composites developed in our early research [3] . A back propagation neural network (BPNN) was developed, i.e.…”

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confidence: 99%

Prediction on Tribology Behavior of PEEK Composites Using Back Propagation Artificial Neural Networks

Liu

et al. 2009

2009 WASE International Conference on Information Engineering

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A multi-layer back propagation artificial neural network (BPNN) was used to predict the friction coefficient and the specific wear rate of short fiber reinforced polyetheretherketone (PEEK) composites. The best network structure for property forecast is decided as 5-[29] 1 -2. The train function is trainlm, and the transfer functions from input to hidden layers and from hidden to output layers are logsig and purelin, respectively. For the network of ingredient forecast, the best network structure is 2-[300] 1 -[150] 2 -4. The train function is trainscg, and logsig, tansig are the transfer functions from input to hidden layers and within the hidden layers, respectively. Purelin is transfer function between hidden and output layers. The results show that ANN techniques can effectively be used to predict the tribology behavior and the components of composites. (Abstract) Keywords-back propagation artificial neural network (BPNN); artificial neural networks (ANN); PEEK composite; tribology behavior; component (key words) Ⅰ. INTRODUCTIONFriction materials normally consist of more than 10 ingredients. The type and the amount of each ingredient in the friction material have been determined mostly on the basis of empirical observations. The lack of good prediction method is a barrier to a deep mechanical and chemical understanding of complex materials. Recently, artificial neural networks (ANN) are used as an interdisciplinary tool in many applications. Another pioneering works in this research field has been performed on the ANN-modeling and ANN-prediction of the wear properties of short fiber composites [1,2] . The major advantage of neural networks is their possibility to predict dependencies between many parameters, and to apply them to any given situation in a "black box" fashion.Therefore, the present work is focused on the application of ANN on prediction of formulation and properties of stainless steel/carbon fiber reinforced PEEK composites developed in our early research [3] . A back propagation neural network (BPNN) was developed, i.e. a multiple-layer feed-forward network with non-linear differentiable transfer functions. A database, containing material compositions and wear characteristics of PEEK composites, was used to train and test the neural network. Afterwards, the well-trained neural network was employed to predict the wear properties according to new input data. The quality of the prediction was also evaluated. In this paper, the modeling and forecasting functions of neural network were realized by software Matlab6.5. Ⅱ. FORECAST OF TRIBOLOGY PROPERTIES FROM MATERIAL COMPOSITIONAn ANN is conventionally constructed with input, output and hidden layers. Hidden layers can contain one or several layers for its particular application. Each layer has different numbers of neural elements. As in nature, the network function is largely determined by the connections between these elements. The process of creating an ANN for materials research can be summarized as: database collection; training of the neural ne...

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The application of PEEK in stainless steel fiber and carbon fiber reinforced composites

Cited by 42 publications

References 9 publications

Abrasion Mechanism of Stainless Steel/Carbon Fiber-Reinforced Polyether-Ether-Ketone (PEEK) Composites

Abrasion Mechanism of Stainless Steel/Carbon Fiber-Reinforced Polyether-Ether-Ketone (PEEK) Composites

Friction and wear behaviors of carbon and aramid fibers reinforced polyimide composites in simulated space environment

Prediction on Tribology Behavior of PEEK Composites Using Back Propagation Artificial Neural Networks

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