| Comparative Study of Reverse Algorithms via Artificial Neural Networks Based on Simulated Indentation Tests |
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| 作者单位： | Department of Civil Engineering，National University of Singapore |
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| 基金项目： | the Singapore Ministry of Education’s ACRF Tier 1 Funds (No. R-264-000-186-112) |
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| 摘 要： | The advances in the instrumented indentation equipments and the need to assess the properties of materials of small volume such as those constitute the micro-electro-mechanical devices, micro-electronic packages, and thin films have propelled the interest in material characterization via indentation tests. The load-displacement curves and their characteristics, namely, the curvature of the loading path, C, and the ratio of the remaining and total work done, WR / WT, can be conveniently obtained from finite element simulations for various elasto-plastic material properties. The paper reports the comparative study on two reverse neural networks algorithms involving several combinations of databases established from the results obtained from simulated indentation tests. The performance of each set of results is analyzed and the most appropriate algorithm identified and reported. The approach with the selected neural networks model has great potential in practical applications on the characterization of a small volume of materials.
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| 关 键 词： | artificial neural networks finite element simulation friction least square support vector machines material characterization indentation tests |
Comparative Study of Reverse Algorithms via Artificial Neural Networks Based on Simulated Indentation Tests |
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| Authors: | Somsak Swaddiwudhipong Edy Harsono Liu Zishun |
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| Institution: | Department of Civil Engineering, National University of Singapore |
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| Abstract: | The advances in the instrumented indentation equipments and the need to assess the properties of materials of small volume such as those constitute the micro-electro-mechanical devices, micro-electronic packages, and thin films have propelled the interest in material characterization via indentation tests. The load-displacement curves and their characteristics, namely, the curvature of the loading path, C, and the ratio of the remaining and total work done, WR / WT, can be conveniently obtained from finite element simulations for various elasto-plastic material properties. The paper reports the comparative study on two reverse neural networks algorithms involving several combinations of databases established from the results obtained from simulated indentation tests. The performance of each set of results is analyzed and the most appropriate algorithm identified and reported. The approach with the selected neural networks model has great potential in practical applications on the characterization of a small volume of materials. |
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| Keywords: | artificial neural networks finite element simulation friction least square support vector machines material characterization indentation tests |
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