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Relevant bibliographies by topics / Material Property Identification

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Journal articles

Academic literature on the topic 'Material Property Identification'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 September 2023

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Journal articles on the topic "Material Property Identification"

1

Wilkie, Jack, Paul D. Docherty, and Knut Möller. "Model-based bone material property identification." at - Automatisierungstechnik 68, no. 11 (2020): 913–21. http://dx.doi.org/10.1515/auto-2020-0083.

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AbstractCorrect torqueing of bone screws is important for orthopaedic surgery. Surgeons mainly tighten screws ad hoc, risking inappropriate torqueing. An adaptive torque-limiting screwdriver may be able to measure the torque-rotation response and use parameter identification of key material properties to recommend optimal torques. This paper analyses the identifiability and sensitivity of a model of the bone screwing process. The accuracy with which values of the Young modulus (E) of the bone were identified depended on the value of E, with larger values being less accurately identified. The error in identified {\sigma _{uts}} (Tensile strength) values was less than 0.5 % over all the cases tested, with no discernible dependence on the co-identified values of E. Experimental validation is still required for the model and identification process, but this approach is feasible and promising from a theoretical perspective.

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2

Kottner, Radek, Richard Hynek, Tomáš Mandys, and Jan Bartošek. "Material property determination of the lining layers of a versatile helmet." MATEC Web of Conferences 157 (2018): 06005. http://dx.doi.org/10.1051/matecconf/201815706005.

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This paper deals with material property identification of a helmet lining consisting of an outer layer of an expanded polystyrene (EPS) and inner layer of an open-closed cell foam (OCCF). A combined numerical simulation and experimental testing was used for the material property identification. Compression and drop tests were performed. The ABAQUS finite element commercial code was used for numerical simulations in which the OOCF was modelled as a rate dependent viscoelastic material, while the EPS as a crushable foam. The reaction force time histories coming from the numerical simulation and the experiment have been used as a criterion for material parameter determination. After the identification of the material properties, numerical drop-tests were used to study the behaviour of a plate and a conical composite OOCF and EPS liners to decide which of them suits more for the helmet.

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3

TAKEKOSHI, Kunio. "Study on the Identification Method for the Non-linear Material Property." Proceedings of the Materials and Mechanics Conference 2019 (2019): OS1512. http://dx.doi.org/10.1299/jsmemm.2019.os1512.

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4

O'Callaghan, Tim. "Intellectual property in the petroleum production and exploration sector—the other hidden asset." APPEA Journal 55, no. 2 (2015): 447. http://dx.doi.org/10.1071/aj14082.

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According to IBISWorld (2013), 7.7% of Australia’s A$11 trillion assets are natural resources and 5.4% is intellectual property. Despite this intellectual property is overlooked as a valuable asset in the oil and gas industry. As the means of extraction become more complex, the methods and tools needed for the purpose can give one company an edge over another. Intellectual property rights help to protect that competitive advantage. Companies need to have a strategy for the early identification, management and protection of this asset. Customers, contractors and joint venture partners can create intellectual property ownership issues that must also be identified and properly managed. This extended abstract provides: a framework for establishing a robust intellectual property management strategy for companies in the exploration and production sector; identification of key intellectual property assets of businesses in the sector; a review of industry specific challenges, such as the requirement under WA’s Petroleum and Geothermal Energy Resources (Environment) Regulations 2012 to disclose trade secrets and commercially sensitive material about downhole substances; and, consideration of model agreements used in the sector, such as the AMPLA Model Petroleum Exploration Joint Operating Agreement.

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5

Sung, Byung Joon, Jin Woo Park, and Yong Hyup Kim. "Material Property Identification of Composite Plates Using Neural Network and Evolution Algorithm." AIAA Journal 40, no. 9 (2002): 1914–16. http://dx.doi.org/10.2514/2.1873.

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6

Sung, B. J., J. W. Park, and Y. H. Kim. "Material property identification of composite plates using neural network and evolution algorithm." AIAA Journal 40 (January 2002): 1914–16. http://dx.doi.org/10.2514/3.15278.

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7

Lißner, Julian, and Felix Fritzen. "Data-Driven Microstructure Property Relations." Mathematical and Computational Applications 24, no. 2 (2019): 57. http://dx.doi.org/10.3390/mca24020057.

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An image based prediction of the effective heat conductivity for highly heterogeneous microstructured materials is presented. The synthetic materials under consideration show different inclusion morphology, orientation, volume fraction and topology. The prediction of the effective property is made exclusively based on image data with the main emphasis being put on the 2-point spatial correlation function. This task is implemented using both unsupervised and supervised machine learning methods. First, a snapshot proper orthogonal decomposition (POD) is used to analyze big sets of random microstructures and, thereafter, to compress significant characteristics of the microstructure into a low-dimensional feature vector. In order to manage the related amount of data and computations, three different incremental snapshot POD methods are proposed. In the second step, the obtained feature vector is used to predict the effective material property by using feed forward neural networks. Numerical examples regarding the incremental basis identification and the prediction accuracy of the approach are presented. A Python code illustrating the application of the surrogate is freely available.

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8

Feng, Xiang Sai, and Kai Shu Guan. "Identification of Creep Property by Small Punch Creep Test and Neural Networks." Applied Mechanics and Materials 711 (December 2014): 227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.711.227.

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Present work describes an approach to identify creep properties of P91 with finite element simulations and neural networks. The small punch test was used to determine the material property under high temperature. Results showed that, the neural networks could be used to evaluate the creep property together with the small punch creep test and finite element simulations.

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9

Gaillard, Claire, Agnieszka Mech, Wendel Wohlleben, et al. "A technique-driven materials categorisation scheme to support regulatory identification of nanomaterials." Nanoscale Advances 1, no. 2 (2019): 781–91. http://dx.doi.org/10.1039/c8na00175h.

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10

Savvas, Dimitrios, Iason Papaioannou, and George Stefanou. "Bayesian identification and model comparison for random property fields derived from material microstructure." Computer Methods in Applied Mechanics and Engineering 365 (June 2020): 113026. http://dx.doi.org/10.1016/j.cma.2020.113026.

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