Triboinformatic Modelling for Friction Prediction using Machine Learning Algorithms / (Record no. 614842)
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| 000 -LEADER | |
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| fixed length control field | 03339nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Manzoor, Muhammad Talha |
| 245 ## - TITLE STATEMENT | |
| Title | Triboinformatic Modelling for Friction Prediction using Machine Learning Algorithms / |
| Statement of responsibility, etc. | Muhammad Talha Manzoor |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 101p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | The tribology is the study of surface roughness, adhesion, friction, wear, and lubrication<br/>of solid surfaces in relative motion. The integration of Tribology with Machine Learning<br/>(ML) and other data-driven/informatics methodologies is commonly referred to as<br/>Triboinformatics. This dissertation employs triboinformatic approaches for the prediction<br/>of coefficient of Friction.<br/>This dissertation presents the development of machine learning models, including<br/>artificial neural network (ANN), support vector machine (SVM) and gradient boosting<br/>machine (GBM) to correlate the coefficient of friction with various tribological<br/>parameters. Machine learning investigation indicates that the instantaneous entrainment<br/>velocity has the most substantial impact on the coefficient of friction (COF).<br/>For enhancement of energy efficiency in automobiles, the reduction of frictional forces is<br/>vital. The key areas of focus for friction reduction have been lubricant chemistry, surface<br/>coatings, and surface changes. All experiments were performed on a modified<br/>reciprocating tribometer encompassing actual cam and tappet shim under actual engine<br/>running conditions. The three tappet shim samples, were engraved with texture densities<br/>of 5%, 8% and 10%. These textured shims were analyzed in comparison with the original<br/>shim and the friction data was obtained at different temperatures. The best friction<br/>reduction effectiveness of 18.33% was achieved by 8% textured shim at 90°C.<br/>After the experimentations on textured shims, experimentations on various friction<br/>modifiers were done using the same tribometer setup. The four different types of friction<br/>modifiers including Organic, Moly A, Polymeric and Moly B Friction Modifier were<br/>used along with base oil and the trends were observed.<br/>For application of ML models, it is required to acquire the suitable dataset. Two types of<br/>experimentations are performed to obtain the required dataset and the dataset of<br/>experimentations with different friction modifiers is used to train and test the used ML<br/>models. The ANN, SVM, and GBM models for coefficient of friction (COF) for camshim contact under lubricated conditions are developed. The most effective predictive<br/>xvi<br/>performance for COF has been demonstrated by the GBM model. The lubricant<br/>entrainment velocity is recognized as the primary variable for predicting coefficient of<br/>friction (COF). The values of mean squared error (MSE), mean absolute error (MAE) and<br/>coefficient of determination (R2) are obtained and compared for all of the three models.<br/>This dissertation illustrates that the Triboinformatic methodologies can be effectively<br/>applied in tribology, yielding new insights into structure-property correlations across<br/>diverse material classes. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Mechanical Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Riaz Ahmed Mufti |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/54981">http://10.250.8.41:8080/xmlui/handle/123456789/54981</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
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| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 09/24/2025 | 621 | SMME-TH-1170 | Thesis |