| 引用本文: | 姜爱龙,李娜娜,刘庆义,王健,宋琳,王迎光,田学雷.基于聚类共晶团算法的蠕墨铸铁形核率预测模型[J].材料科学与工艺,2022,30(4):61-68.DOI:10.11951/j.issn.1005-0299.20210323. |
| JIANG Ailong,LI Nana,LIU Qingyi,WANG Jian,SONG Lin,WANG Yingguang,TIAN Xuelei.Prediction model of nucleation rate for vermicular graphite cast iron based on cluster eutectic algorithm[J].Materials Science and Technology,2022,30(4):61-68.DOI:10.11951/j.issn.1005-0299.20210323. |
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| 基于聚类共晶团算法的蠕墨铸铁形核率预测模型 |
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姜爱龙1,李娜娜1,刘庆义1,王健1,宋琳2,王迎光2,田学雷2
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(1.内燃机可靠性国家重点实验室(潍柴动力股份有限公司),山东 潍坊 261061; 2.材料液固结构演变与加工教育部重点实验室(山东大学 材料科学与工程学院),济南 250061)
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| 摘要: |
| 蠕墨铸铁是一种性能优良的结构材料,蠕化效果对力学性能有重要影响,形核率的准确预测是精准调控蠕化过程的重要前提,进而实现高效蠕化。为了更准确地通过金相照片计算金属材料的形核率,本文采用计算机图形学中的DBSCAN聚类算法来统计试样中的共晶团数,建立了直接描述形核率的模型及相对应的生长模型,并与通过统计金相照片中的石墨个数得到形核率的方法作为对照,对比分析发现,聚类算法计算后得到的冷却曲线和蠕化率与实验结果吻合良好,证明了该算法对应的形核率预测模型的合理性。此外,采用温度场模拟计算,通过对比石墨数统计共晶团数目、聚类数统计共晶团数目的温度场变化,进一步验证了聚类数统计共晶团方法的合理性。 |
| 关键词: 铸造工艺及设备 蠕墨铸铁 共晶凝固模拟 DBSCAN聚类算法 形核模型 |
| DOI：10.11951/j.issn.1005-0299.20210323 |
| 分类号:TS913+.2 |
| 文献标识码:A |
| 基金项目:山东省自然基金重大基础研究项目(ZR2021ZD22). |
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| Prediction model of nucleation rate for vermicular graphite cast iron based on cluster eutectic algorithm |
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JIANG Ailong1, LI Nana1, LIU Qingyi1, WANG Jian1, SONG Lin2, WANG Yingguang2, TIAN Xuelei2
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(1.State Key Laboratory of Engine Reliability (Weichai Power Co., Ltd.), Weifang 261061, China; 2.Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials, Ministry of Education (School of Materials Science and Engineering, Shandong University), Jinan 250061, China)
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| Abstract: |
| Vermicular graphite cast iron is a kind of structural material with excellent properties. The vermicular effect has a significant impact on mechanical properties. The accurate prediction of nucleation rate is an important premise for the precise regulation of the vermicular process, so as to realize an efficient vermicular process. For accurately calculating the nucleation rate of metal materials through metallographic photos, the DBSCAN clustering algorithm in computer graphics was adopted to count the number of eutectic clusters in the sample, and a model that can directly describe nucleation rate and the corresponding growth model were established. The method of counting the number of graphite in metallographic photos to obtain the number of eutectic clusters was used as a contrast.Results show that the calculated cooling curve and creep rate based on clustering algorithm were in good agreement with the experimental results, which proved the rationality of the nucleation rate prediction model. Besides, the differences between the temperature fields of the numbers of eutectic clusters calculated by counting graphite and clusters were compared via temperature field simulation, which further verified the method of counting the number of clusters to obtain the number of eutectic clusters. |
| Key words: casting process and equipment vermicular graphite cast iron eutectic solidification simulation DBSCAN clustering algorithms nucleation model |
