Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms
This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/11/11/1018 |
| _version_ | 1797458566679887872 |
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| author | Chiu-Hung Chen |
| author_facet | Chiu-Hung Chen |
| author_sort | Chiu-Hung Chen |
| collection | DOAJ |
| description | This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain multiple solutions, while a good quality of learning cannot be obtained by a single neural network approach. Therefore, this paper proposes employing Fourier descriptors to represent trajectory curves in a systematic and normalized form, developing a multi-solution distribution evaluation by random restart local searches (MDE-RRLS) to examine a better solution-space partitioning scheme, utilizing multiple DNNs to learn subspace regions separately, and creating a multi-facet query (MFQuery) to cooperatively predict multiple solutions. The experiments demonstrate that the proposed approach can obtain better or at least competitive outcomes compared to previous work in the literature. Furthermore, to verify the effectiveness and applicability, this paper investigates the design problem of an industrial six-linkage-bar ladle mechanism used in a die-casting system, and the proposed method can obtain several superior design solutions and offer alternatives in a short period of time when faced with redesign requirements. |
| first_indexed | 2024-03-09T16:39:55Z |
| format | Article |
| id | doaj.art-36a1bd67e1cd4ee1bcea7b15e8881715 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T16:39:55Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-36a1bd67e1cd4ee1bcea7b15e88817152023-11-24T14:52:59ZengMDPI AGMachines2075-17022023-11-011111101810.3390/machines11111018Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage MechanismsChiu-Hung Chen0Department of Mechanical and Computer-Aided Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, Taichung 407, TaiwanThis paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain multiple solutions, while a good quality of learning cannot be obtained by a single neural network approach. Therefore, this paper proposes employing Fourier descriptors to represent trajectory curves in a systematic and normalized form, developing a multi-solution distribution evaluation by random restart local searches (MDE-RRLS) to examine a better solution-space partitioning scheme, utilizing multiple DNNs to learn subspace regions separately, and creating a multi-facet query (MFQuery) to cooperatively predict multiple solutions. The experiments demonstrate that the proposed approach can obtain better or at least competitive outcomes compared to previous work in the literature. Furthermore, to verify the effectiveness and applicability, this paper investigates the design problem of an industrial six-linkage-bar ladle mechanism used in a die-casting system, and the proposed method can obtain several superior design solutions and offer alternatives in a short period of time when faced with redesign requirements.https://www.mdpi.com/2075-1702/11/11/1018deep neural networkmultiple solutionsFourier descriptorlinkage synthesisindustrial application |
| spellingShingle | Chiu-Hung Chen Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms Machines deep neural network multiple solutions Fourier descriptor linkage synthesis industrial application |
| title | Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms |
| title_full | Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms |
| title_fullStr | Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms |
| title_full_unstemmed | Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms |
| title_short | Application of Multiple Deep Neural Networks to Multi-Solution Synthesis of Linkage Mechanisms |
| title_sort | application of multiple deep neural networks to multi solution synthesis of linkage mechanisms |
| topic | deep neural network multiple solutions Fourier descriptor linkage synthesis industrial application |
| url | https://www.mdpi.com/2075-1702/11/11/1018 |
| work_keys_str_mv | AT chiuhungchen applicationofmultipledeepneuralnetworkstomultisolutionsynthesisoflinkagemechanisms |