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Subba Rao, P.
- A Study on Effect of Loading on Kink Angle in Ceramics: Mixed Mode Fracture Mechanics Approach Using Finite Element Analysis
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Authors
Affiliations
1 Department of Civil Engineering, University College of Engineering, JNT University Kakinada, Kakinada, A.P, IN
2 Department of Civil Engineering, Swarnandhra College of Engineering, Narspur-534275, A.P, IN
1 Department of Civil Engineering, University College of Engineering, JNT University Kakinada, Kakinada, A.P, IN
2 Department of Civil Engineering, Swarnandhra College of Engineering, Narspur-534275, A.P, IN
Source
International Journal of Civil Engineering Research, Vol 3, No 1 (2012), Pagination: 47-58Abstract
Although while determining fracture toughness values of materials experimental methods are important, the reasons like the difficulty of forming a commencement slanted middle crack with two crack tips , which has a high hardness like ceramic materials, the difficulty of boozing the and the validity the experiment results have led scientists to improve more active methods.In this study, fracture toughness values of a ceramic Composite material boron carbide particles have been designated in a computer environment using the two dimensional finite elements method. While determining fracture toughness values Linear Elastic Fracture Mechanics (LEFM) approach has been used. The results being taken from analysis using ANSYS 12.0 programme and reliability of finite elements has been understood clearly. The primary goal of the study is to determine and comparing the kink angle for temperature and its equivalent mechanical loading.Predicting Stress intensity factors for mechanical load and thermal loading and crack initiation angle in the case of mixed mode fracture. The analysis carried out different angles of inclinations namely 15 degrees, 30 degrees, 45 degrees, 60 degrees and 75 degrees cracks were considered in this analysis. Predicting stress intensity factors for mechanical equivalent thermal loading and crack initiation angles is dependent on the value of stress in the vicinity of the crack tip. As a result, stress intensity factor is considered as the most significant parameter in this regard because it represents the stress level at the crack tip. SIF's for inclined crack are determined numerically as well as using ANSYS 12.0. After that, the values of stress intensities are incorporated the maximum energy release rate criterion (MERR) to find the kink angle.Keywords
Stress Intensity Factors, Kink Angle, Thermal Loading, Mechanical Loading, The Maximum Energy Release Rate Criterion (MERR) etc.References
- ANSYS 12.0 Release Manuals
- F. Aldinger Germany) J.F. Baumard (ENSCI, 87065 Limoges, France)”Advanced Ceramic Materials basic research view point”
- Hannah J. Yount-“ Hardness and fracture toughness of heat treated advanced ceramic materials for use as fuel coating and inert matrix materialsin advanced reactors”A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science(Nuclear Engineering)at the University of wiscocin-Madison (2006).
- Jack D. Spain “Properties of ceramic filters” (Southern Research Institute. O. Box 55305, Birmingham,Alabama 35255-5305, 1996
- Jack D. Spain (spain@sri.org),H. Stuart Starrett (starrett@sri.org),-“ Properties of Ceramic Candle Filters “ Southern Research Institute, 757 Tom Martin Drive,Birmingham, Alabama 35211-(1998)
- M. K. Aghajanian, B. N. Morgan, J. R. Singh J. Mears and R. A. Wolffe “A new family of reaction bonded ceramics for armor applications” 2001
- P. G. Karandikar, G. Evans, S. Wong, and M. K. Aghajanian –“A Review of Ceramics for armor applications”-Presented at the 32nd International Conference on Advanced Ceramics and Composites, Daytona Beach, January, 2008. Rev. 3 Ceramic Engineering and Science Proceedings V29 No.6 (in press)
- Sunil Dutta-“”Fracture toughness and reliability in high-temperature Structural ceramics and composites: Vol. 24, No. 2, Indian Academy of Sciences, April 2001.
- Stock Market Prediction with the help of Radial Base Function - RBF using Machine Learning
Abstract Views :202 |
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Authors
Affiliations
1 Department of Computer Science & Engineering, Chalapathi Institute of Engineering and Technology, Guntur-522034, IN
1 Department of Computer Science & Engineering, Chalapathi Institute of Engineering and Technology, Guntur-522034, IN
Source
International Journal of Advanced Networking and Applications, Vol 12, No 1 (2020), Pagination: 4537-4541Abstract
In the fund world stock exchanging is one of the most significant exercises. Securities exchange expectation is a demonstration of attempting to decide the future estimation of a stock other money related instrument exchanged on a monetary trade. This paper clarifies the expectation of a stock utilizing Machine Learning[6]. The specialized and central or the time arrangement examination is utilized by the a large portion of the stockbrokers while making the stock forecasts. The programming language is utilized to anticipate the securities exchange utilizing AI is Python. Right now propose a Machine Learning[10] (ML) approach that will be prepared from the accessible stocks information and increase insight and afterward utilizes the gained information for a precise forecast. Right now study utilizes an AI system called Support Vector Machine (SVM)[1] to anticipate stock costs for the enormous and little capitalizations and in the three distinct markets, utilizing costs with both every day and regularly updated frequencies.Keywords
Machine Learning, Predictions, Stock Market, Support Vector Machine.References
- Zhen Hu, Jibe Zhu, and Ken Tse "Stocks Market Prediction Using Support Vector Machine", sixth International Conference on Information Management, Innovation Management and Industrial Engineering, 2013.M.
- Wei Huang, Yoshiteru Nakamori, Shou-Yang Wang, "Guaging securities exchange development course with help vector machine", Computers and Operations Research, Volume 32, Issue 10, October 2005, Pages 2513–2522.
- N. Ancona, Classification Properties of Support Vector Machines for Regression, Technical Report, RIIESI/CNRNr.02/99.
- K. jae Kim, "Money related time arrangement determining utilizing bolster vector machines," Neurocomputing, vol. 55, 2003.
- Debashish Das and Mohammad shorifuddin information mining and neural system procedures in securities exchange forecast: a methodological survey, universal diary of man-made consciousness and applications, vol.4, no.1, January 2013
- Ashish Sharma, Dinesh Bhuriya, Upendra Singh. "Survey of Stock Market Prediction Using Machine Learning Approach", ICECA 2017.
- Loke.K.S. “Impact Of Financial Ratios And Technical Analysis On Stock Price Prediction Using Random Forests”, IEEE, 2017.
- . Xi Zhang1, Siyu Qu1, Jieyun Huang1, Binxing Fang1, Philip Yu2, “Stock Market Prediction via Multi-Source Multiple Instance Learning.” IEEE 2018.
- VivekKanade, BhausahebDevikar, SayaliPhadatare, PranaliMunde, ShubhangiSonone. “Stock Market Prediction: Using Historical Data Analysis”, IJARCSSE 2017.
- . SachinSampatPatil, Prof. Kailash Patidar, Asst. Prof. Megha Jain, “A Survey on Stock Market Prediction Using SVM”, IJCTET 2016.
- . Hakob GRIGORYAN, “A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA)”, DSJ 2016.
- RautSushrut Deepak, ShindeIshaUday, Dr. D. Malathi, “Machine Learning Approach In Stock Market 9. Prediction”, IJPAM 2017.
- Pei-Yuan Zhou , Keith C.C. Chan, Member, IEEE, and Carol XiaojuanOu, “Corporate Communication Network and Stock Price Movements: Insights From Data Mining”, IEEE 2018..
- Mr.ashok radhesriya,”Performance Analysis of Supervised Techniques for Review Spam Detection” International Journal of Advanced Networking Applications (IJANA) ISSN No. : 0975-0290