| 引用本文: | 董世运，徐滨士，董丽虹，王 丹.金属磁记忆检测技术用于再制造毛坯寿命预测的试验研究[J].中国表面工程,2006,(7):71~75 |
| DONG Shi-yun, XU Bin-shi, DONG Li-hong, WANG Dan.Experiment Research on Metal Magnetic Memory methods for Life Prediction of Remanufacturing Old Parts[J].China Surface Engineering,2006,(7):71~75 |
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| 摘要: |
| 为探讨金属磁记忆无损检测技术预测再制造毛坯的剩余寿命，采用金属磁记忆技术检测了拉－拉疲劳过程中45钢光滑试件和预制缺陷试件表面的磁记忆信号变化规律。结果表明：45钢光滑试件疲劳试验过程中，在试件断裂之前，随疲劳载荷作用循环次数的增加，试件表面磁信号的分布规律变化不大，试件断裂后断口处磁信号发生激变；表面预制槽型缺陷的45钢试件在疲劳试验过程中，其表面磁信号在预制缺陷扩展后发生变化，且缺陷部位磁信号峰峰值随裂纹长度增加而持续增大。研究分析认为，金属磁记忆检测技术对疲劳裂纹的扩展情况有较好的反映，有望通过建立磁记忆信号峰峰值——疲劳裂纹长度a的关系模型，实现金属磁记忆技术对再制造毛坯剩余寿命的定量方法。但是，采用金属磁记忆技术预测疲劳裂纹萌生前铁磁材料的损伤程度尚需进一步探索。 |
| 关键词: 金属磁记忆技术 再制造毛坯 寿命预测 试验研究 |
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| 基金项目:国家自然科学基金资助项目（50505052）；武器装备预研基金项目（编号略） |
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| Experiment Research on Metal Magnetic Memory methods for Life Prediction of Remanufacturing Old Parts |
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DONG Shi-yun, XU Bin-shi, DONG Li-hong, WANG Dan
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Natioanl Key Laboratory for Remanufacturing, Academy of Armord Forces Engineering, Beijing, 100072 China
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| Abstract: |
| Metal Magnetic Memory (MMM) method was empolyed to detect the magnetic sign variation for 1045 steel samples in the fatigue tests in order to study the technolgy that MMM method predicts residual life of the remanfucturing old parts. Results showed that the magnatic sign distribution on surface of the 1045 steel smooth sample varied a little with loading cycles before the sample ruptured, though the sign near the ruptured line varied abruptly. As for the 1045 steel sample with a prefabricated groove, the surface magnetic sign distribution varied with the groove expanded, i.e., the sign value near the groove increased. It was concluded that MMM method can disclose growing of the fatigue cracks, and it is possible to predict residual life of the remanufacturing old parts after upseting the relationship of magnetic sign and fatigue crack length, a. However, MMM method still needs further researches to predict the fatigue extent of the metal without fatigue cracks initiating. |
| Key words: metal magnetic memory methods remanufacturing old parts life prediction experiment research |
