Machine Learning in Tribology
Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an in...
שמור ב:
| פורמט: | Online |
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| שפה: | אנגלית |
| יצא לאור: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| נושאים: | |
| גישה מקוונת: | ONIX_20220621_9783036539812_77 |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
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| _version_ | 1869520557733576704 |
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| collection | Directory of Open Access Books |
| description | Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology. |
| format | Online |
| id | doab-20.500.12854ir-84499 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-844992024-04-09T23:16:05Z Machine Learning in Tribology Tremmel, Stephan Marian, Max artificial intelligence machine learning artificial neural networks tribology condition monitoring semi-supervised learning random forest classifier self-lubricating journal bearings reduced order modelling dynamic friction rubber seal applications tensor decomposition laser surface texturing texturing during moulding digital twin PINN reynolds equation triboinformatics databases data mining meta-modeling monitoring analysis prediction optimization fault data generation Convolutional Neural Network (CNN) Generative Adversarial Network (GAN) bearing fault diagnosis unbalanced datasets tribo-testing tribo-informatics natural language processing tribAIn BERT amorphous carbon coatings UHWMPE total knee replacement Gaussian processes rolling bearing dynamics cage instability regression neural networks random forest gradient boosting evolutionary algorithms rolling bearings remaining useful life feature engineering structure-borne sound n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology. 2022-06-21T08:38:48Z 2022-06-21T08:38:48Z 2022 book ONIX_20220621_9783036539812_77 9783036539812 9783036539829 https://directory.doabooks.org/handle/20.500.12854/84499 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/5482 https://mdpi.com/books/pdfview/book/5482 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3982-9 10.3390/books978-3-0365-3982-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036539812 9783036539829 208 Basel open access |
| spellingShingle | artificial intelligence machine learning artificial neural networks tribology condition monitoring semi-supervised learning random forest classifier self-lubricating journal bearings reduced order modelling dynamic friction rubber seal applications tensor decomposition laser surface texturing texturing during moulding digital twin PINN reynolds equation triboinformatics databases data mining meta-modeling monitoring analysis prediction optimization fault data generation Convolutional Neural Network (CNN) Generative Adversarial Network (GAN) bearing fault diagnosis unbalanced datasets tribo-testing tribo-informatics natural language processing tribAIn BERT amorphous carbon coatings UHWMPE total knee replacement Gaussian processes rolling bearing dynamics cage instability regression neural networks random forest gradient boosting evolutionary algorithms rolling bearings remaining useful life feature engineering structure-borne sound n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Machine Learning in Tribology |
| title | Machine Learning in Tribology |
| title_full | Machine Learning in Tribology |
| title_fullStr | Machine Learning in Tribology |
| title_full_unstemmed | Machine Learning in Tribology |
| title_short | Machine Learning in Tribology |
| title_sort | machine learning in tribology |
| topic | artificial intelligence machine learning artificial neural networks tribology condition monitoring semi-supervised learning random forest classifier self-lubricating journal bearings reduced order modelling dynamic friction rubber seal applications tensor decomposition laser surface texturing texturing during moulding digital twin PINN reynolds equation triboinformatics databases data mining meta-modeling monitoring analysis prediction optimization fault data generation Convolutional Neural Network (CNN) Generative Adversarial Network (GAN) bearing fault diagnosis unbalanced datasets tribo-testing tribo-informatics natural language processing tribAIn BERT amorphous carbon coatings UHWMPE total knee replacement Gaussian processes rolling bearing dynamics cage instability regression neural networks random forest gradient boosting evolutionary algorithms rolling bearings remaining useful life feature engineering structure-borne sound n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | artificial intelligence machine learning artificial neural networks tribology condition monitoring semi-supervised learning random forest classifier self-lubricating journal bearings reduced order modelling dynamic friction rubber seal applications tensor decomposition laser surface texturing texturing during moulding digital twin PINN reynolds equation triboinformatics databases data mining meta-modeling monitoring analysis prediction optimization fault data generation Convolutional Neural Network (CNN) Generative Adversarial Network (GAN) bearing fault diagnosis unbalanced datasets tribo-testing tribo-informatics natural language processing tribAIn BERT amorphous carbon coatings UHWMPE total knee replacement Gaussian processes rolling bearing dynamics cage instability regression neural networks random forest gradient boosting evolutionary algorithms rolling bearings remaining useful life feature engineering structure-borne sound n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20220621_9783036539812_77 |