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http://hdl.handle.net/10603/333487
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DC Field | Value | Language |
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dc.coverage.spatial | Studies on tribological properties of uhmwpe hybrid composites using response surface methodology and artificial neural network | |
dc.date.accessioned | 2021-07-28T06:08:32Z | - |
dc.date.available | 2021-07-28T06:08:32Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/333487 | - |
dc.description.abstract | Nowadays, polymers and polymer matrix composites reinforced with fiber or particles are used in various industrial applications where wear performance in both dry and wet condition is a key parameter for the material. Polymers and their composites have excellent strength to weight ratio, resistance to corrosion, non-toxicity, easy to fabricate, design flexibility, self-lubricating properties, low coefficient of friction and high wear resistance. For the past few decades, from thermoplastic family, Ultra-High Molecular Weight Polyethylene (UHMWPE) has been used as an orthopaedic implant material in Total Joint Replacement (TJR) because of its excellent properties such as highest wear resistance and impact strength, self-lubricating property, low coefficient of friction, and nontoxic nature. However, because of its low strength it cannot be used as load bearing material for long life. In addition, the formation of wear debris leads to failure in long-term implant applications. newlineWear is a progressive loss of material from the solid surface due to relative motion between solid surface and counter surface. Wear can occur through adhesion, abrasion, third body, fatigue and corrosion. Abrasive wear is caused due to hard particles penetration because of applied load and travel along a solid surface. Abrasive wear is the most important among all forms of wear because it contributes almost 64% of the total cost of wear. Therefore, to reduce or control the abrasive wear rate, a full understanding is required about the system variables in order to design the components. Recently, particle reinforcement has been extensively used to increase the strength, hardness, and wear resistance of UHMWPE. newline newline | |
dc.format.extent | xxiv,188p. | |
dc.language | English | |
dc.relation | p.173-187 | |
dc.rights | university | |
dc.title | Studies on tribological properties of uhmwpe hybrid composites using response surface methodology and artificial neural network | |
dc.title.alternative | ||
dc.creator.researcher | Selvam, S | |
dc.subject.keyword | Tribological properties | |
dc.subject.keyword | Polymers | |
dc.subject.keyword | Artificial neural network | |
dc.description.note | ||
dc.contributor.guide | Marimuthu, K | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Mechanical Engineering | |
dc.date.registered | ||
dc.date.completed | 2021 | |
dc.date.awarded | 2021 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Mechanical Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 19.04 kB | Adobe PDF | View/Open |
02_certificates.pdf | 76.86 kB | Adobe PDF | View/Open | |
03_vivaproceedings.pdf | 303.72 kB | Adobe PDF | View/Open | |
04_bonafidecertificate.pdf | 214.05 kB | Adobe PDF | View/Open | |
05_abstracts.pdf | 188.6 kB | Adobe PDF | View/Open | |
06_acknowledgements.pdf | 289.38 kB | Adobe PDF | View/Open | |
07_contents.pdf | 187.67 kB | Adobe PDF | View/Open | |
08_listoftables.pdf | 265.58 kB | Adobe PDF | View/Open | |
09_listoffigures.pdf | 180.67 kB | Adobe PDF | View/Open | |
10_listofabbreviations.pdf | 193.86 kB | Adobe PDF | View/Open | |
11_chapter1.pdf | 587.25 kB | Adobe PDF | View/Open | |
12_chapter2.pdf | 217.8 kB | Adobe PDF | View/Open | |
13_chapter3.pdf | 814.73 kB | Adobe PDF | View/Open | |
14_chapter4.pdf | 524.31 kB | Adobe PDF | View/Open | |
15_chapter5.pdf | 1.26 MB | Adobe PDF | View/Open | |
16_chapter6.pdf | 1.18 MB | Adobe PDF | View/Open | |
17_conclusion.pdf | 29.04 kB | Adobe PDF | View/Open | |
18_references.pdf | 183.52 kB | Adobe PDF | View/Open | |
19_listofpublications.pdf | 103.57 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 58.83 kB | Adobe PDF | View/Open |
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