Indentation of Polytetrafluoroethylene (PTFE) Thin Film: Simulations by Using Continuum Damage Mechanics

This paper proposes a wear model for polymers based on so-called mechanistic processes comprising both low cycle fatigue and abrasive wear mechanisms, which are prominent in polymer–metal sliding interfaces. Repeated elastic contact causes localized fatigue, whereas abrasive part is an anticipatory outcome of plastic contacts by hard metal asperities on to soft polymer surface. Further, presuming adhesive interactions in elastic–plastic contacts, asperity contact theories with necessary modifications were analyzed to assess load and separation for their subsequent use in elementary wear correlations. Both Gaussian and Weibull distributions of asperity heights were considered to include statistics of surface microgeometry. Finally, volumetric wear was written in terms of roughness parameters, material properties, and sliding distance. Validation was conducted extensively, and reliability of the formulation was achieved to a large extent. Experimental part of this work included several pin-on-disk tests using polyether ether ketone (PEEK) pins and 316L stainless steel disks. Disks with different roughness characteristics generated by polishing, turning, and milling were tested. Experimental results agreed well with predictions for the polished surface and with some deviations for other two surfaces. Further, fatigue to abrasive wear ratio was identified as an analytical tool to predict prevailing wear mechanism for polymer-metal tribo-systems. After examining the considered cases, it was both interesting and physically intuitive to observe a complete changeover in wear mechanisms following simply an alteration of roughness characteristics.

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An Analytical Model of Mechanistic Wear of Polymers

Panda

Sarangi

Chowdhury

2017

Journal of Tribology

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Optical Film Damage Classification Based on Neural Network

Yang

Huang

et al. 2022

Int. J. Patt. Recogn. Artif. Intell.

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Traditional machine learning requires users to have a strong ability to control features and distance calculation formulas, especially in the use of support vector machine SVM and nearest neighbor KNN. Traditional machine learning uses PCA in feature extraction will actually lead to Information is lost. In order to solve the problem of low optical film damage detection rate of traditional methods, a new method is proposed in this paper based on a convolutional neural network instead of traditional machine learning to classify CCD images with different damage degrees of SiO2 film and K9 glass. First, film images are collected by online CCD, and the proposed algorithm is designed to extract the image characteristic parameters of the film microscopic images, filter denoising, and run binarization to analyze film images. Second, gray values of images are extracted and classified by unsupervised learning. Finally, the film microscopic images under the microscope are analyzed. The experimental results show that the defect positions on the images can be detected after the images are detected and processed by a convolution neural network, binarization, and connected domains. The defective parts can be intercepted from the images, and the data related is saved for damage type determination. The average classification rate is over 99%, which is better than the traditional method by 9.1%. Therefore, it has a high application value.

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Indentation of Polytetrafluoroethylene (PTFE) Thin Film: Simulations by Using Continuum Damage Mechanics

Cited by 2 publications

References 18 publications

An Analytical Model of Mechanistic Wear of Polymers

An Analytical Model of Mechanistic Wear of Polymers

Optical Film Damage Classification Based on Neural Network

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