Reconstruction method of fatigue reliability S-N curves for railway wheel cast steel of grade B
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摘要: 为实现任意可靠性水平疲劳强度设计、寿命预测和可靠性评定, 提出了B级车轮铸钢疲劳可靠性S-N曲线的重构方法, 应用Monte-Carlo模拟技术在可接受误差范围内重构了B级铸钢的疲劳极限和成组法S-N数据, 依照常规法测定了B级铸钢中、短寿命范围的可靠性S-N曲线, 应用概率疲劳极限外推法获得了包含中、短和长寿命范围的可靠性曲线。在此基础上, 考虑工程应用实际情况, 推导出了任意概率水平下的里程可靠性曲线。重构获得的疲劳极限及S-N数据最大模拟误差分别只有0.15%和0.07%, 较好再现了原始数据, 对曲线的外推使其合理性达到生产需要的104km以上, 说明曲线重构方法可获得所需疲劳可靠性S-N曲线。
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关键词:
- 车辆工程 /
- 车轮 /
- 铸钢 /
- 疲劳可靠性S-N曲线 /
- 重构方法 /
- Monte-Carlo模拟
Abstract: In order to realize the reliability strength design, fatigue life prediction and reliability assessment at an arbitrary probabilistic level for railway wheel cast steel of grade B, a reconstruction method of fatigue reliability S-N curves was proposed, the fatigue limits and the S-N data were reconstructed by a technique of Monte-Carlo simulation with acceptable error range, the basic reliability curves in the middle and short life ranges were estimated by conventional method, the reliability S-N curves in the middle, short and long life ranges were established by probabilistic fatigue limit extrapolating method. Taking the practical situation of engineering application into account, the mileage reliability curves at arbitrary probabilistic level were deduced. The maximum simulation errors for the reconstructed data of fatigue limits and the S-N data are only 0.15% and 0.07% respectively, the extrapolated curves can cover the range of more than 104 km, it meets production demand, which shows that the reconstructed method is feasible. -
表 1 疲劳极限数据
Table 1. Fatigue limit data
MPa σ-1 σm σs ex 180.58, 176.63, 187.62, 189.75, 181.42, 187.41, 186.26, 191.62, 187.54 185.43 4.847 0.27 
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表 2 原S-N曲线统计参数
Table 2. Statistical parameters of original S-N curves
σa/MPa Nm/cycle NR/cycle 270.83 196 100 95 500 241.82 276 900 137 800 212.89 645 300 243 000
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表 3 统计参数的回归及误差
Table 3. Regression of statistical parameters and errors
σa/MPa Nm NR 预测值/cycle 误差/% 预测值/cycle 误差/% 270.83 181 751 -7.32 93 077 -2.54 241.82 319 645 15.44 144 654 4.97 212.89 603 139 -6.53 237 511 -2.26
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表 4 S-N数据的重构
Table 4. Reconstruction of S-N data
σa/MPa N/cycle ng 270.83 250 665, 197 546, 165 187, 182 731, 117 189, 201 750, 185 209, 213 283, 159 645, 201 695, 171 664 11 241.82 467 826, 352 815, 285 429, 321 687, 190 047, 361 729, 326 864, 386 355, 274 119, 361 611, 298 737 11 212.89 943 732, 677 358, 527 986, 607 672, 327 343, 697 517, 619 181, 753 660, 503 484, 697 251, 557 038 11
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表 5 统计参数的重构及误差
Table 5. Reconstruction of statistical parameters and errors
σa/MPa Nm NR 预测值/cycle 误差/% 预测值/cycle 误差/% 270.83 183 016 0.06 93 341 0.02 241.82 322 284 0.06 145 139 0.03 212.89 608 997 0.07 238 448 0.03
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表 6 S-N曲线的重构参数
Table 6. Reconstruction parameters of S-N curves
表 7 S-L曲线参数
Table 7. Parameters of S-L curves
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