Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models
Compliant mechanisms have been widely employed for precision engineering. Due to a kinematic coupling between rigid kinematics and flexible kinematics of compliant mechanisms, modeling behavior of two degrees of freedom (2-DOF) compliant mechanism is a challenging task. Therefore, the goal of this p...
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IEEE
2025-01-01
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| author | Hieu Giang Le Thanh-Phong Dao Minh Phung Dang Thao Nguyen-Trang |
| author_facet | Hieu Giang Le Thanh-Phong Dao Minh Phung Dang Thao Nguyen-Trang |
| author_sort | Hieu Giang Le |
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| description | Compliant mechanisms have been widely employed for precision engineering. Due to a kinematic coupling between rigid kinematics and flexible kinematics of compliant mechanisms, modeling behavior of two degrees of freedom (2-DOF) compliant mechanism is a challenging task. Therefore, the goal of this paper is to model complicated behaviors of the 2-DOF compliant mechanism. Random Forest is one of the popular and best machine learning models for tabular data. However, most of them have aggregated the component predictions based on the average or the voting process. The objective of this paper is to explore a more reasonable rule for aggregate. In the new technique, several Random Forest regressors are utilized as the weak learners, and their predictions are considered predictors again. The aggregated rule is then effectively learned by a primary Random Forest. The new method is subsequently applied to modeling the behaviors of a two-degrees of freedom compliant mechanism, a research area to which applying machine learning methods is limited. The numerical results demonstrate that the proposed hierarchical manner can improve the performance of component Random Forest models tuned with different values of number of trees and max-depth. Particularly, the results on the test sets of the two case studies have shown that the proposed method can reduce the MAE by 0.02 to 0.04 (about 10%) compared to the best method among the component RFs. Those results verify the applicability of the proposed method to compliant mechanism data. |
| format | Article |
| id | doaj-art-436865fa75124d2cbf62472ba569fa94 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-436865fa75124d2cbf62472ba569fa942025-01-07T00:02:28ZengIEEEIEEE Access2169-35362025-01-01132628263710.1109/ACCESS.2024.351857410819341Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest ModelsHieu Giang Le0Thanh-Phong Dao1Minh Phung Dang2https://orcid.org/0000-0002-8046-2410Thao Nguyen-Trang3https://orcid.org/0000-0003-2635-5371Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, VietnamLaboratory for Applied and Industrial Mathematics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, VietnamCompliant mechanisms have been widely employed for precision engineering. Due to a kinematic coupling between rigid kinematics and flexible kinematics of compliant mechanisms, modeling behavior of two degrees of freedom (2-DOF) compliant mechanism is a challenging task. Therefore, the goal of this paper is to model complicated behaviors of the 2-DOF compliant mechanism. Random Forest is one of the popular and best machine learning models for tabular data. However, most of them have aggregated the component predictions based on the average or the voting process. The objective of this paper is to explore a more reasonable rule for aggregate. In the new technique, several Random Forest regressors are utilized as the weak learners, and their predictions are considered predictors again. The aggregated rule is then effectively learned by a primary Random Forest. The new method is subsequently applied to modeling the behaviors of a two-degrees of freedom compliant mechanism, a research area to which applying machine learning methods is limited. The numerical results demonstrate that the proposed hierarchical manner can improve the performance of component Random Forest models tuned with different values of number of trees and max-depth. Particularly, the results on the test sets of the two case studies have shown that the proposed method can reduce the MAE by 0.02 to 0.04 (about 10%) compared to the best method among the component RFs. Those results verify the applicability of the proposed method to compliant mechanism data.https://ieeexplore.ieee.org/document/10819341/Recursive hierarchyrandom forestensemble learningcompliant mechanism |
| spellingShingle | Hieu Giang Le Thanh-Phong Dao Minh Phung Dang Thao Nguyen-Trang Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models IEEE Access Recursive hierarchy random forest ensemble learning compliant mechanism |
| title | Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models |
| title_full | Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models |
| title_fullStr | Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models |
| title_full_unstemmed | Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models |
| title_short | Modeling Behaviors for a New Compliant Mechanism by Recursive Hierarchy of Random Forest Models |
| title_sort | modeling behaviors for a new compliant mechanism by recursive hierarchy of random forest models |
| topic | Recursive hierarchy random forest ensemble learning compliant mechanism |
| url | https://ieeexplore.ieee.org/document/10819341/ |
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