发表机构
Dibrugarh University Institute of Engineering and Technology（Dibrugarh大学工程技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文首次将共形预测应用于钢材疲劳强度，发现边缘覆盖率不足，提出交叉拟合归一化共形方法实现均匀覆盖率，强调需同时评估条件与边缘覆盖率。
AI 中文摘要
通过实验预测钢材构件的疲劳失效成本高昂，因为需要针对多种成分和加工条件进行测试，这推动了数据驱动预测模型的研究。使用NIMS MatNavi钢材疲劳数据集的研究常报告较高的点预测精度，但依赖聚合误差指标，使得人们对单个预测的可靠性以及精度是否在疲劳强度谱上保持一致存疑。本文首次将共形预测应用于钢材疲劳强度，在50个独立数据划分中比较了5种区间构建方法，并区分了边缘覆盖率与预测属性特定子区域内的覆盖率。梯度提升点模型的R²为0.976±0.009，平均绝对误差为18.3±2.3 MPa。拆分共形预测提供了有效的边缘覆盖率（0.918），但在设计裕度最关键的最高强度四分位数中，覆盖率降至0.755，高斯过程基线也观察到了这一模式。交叉拟合归一化共形方法通过基于局部预测难度的交叉拟合估计值缩放区间，而非使用单一全局宽度，在不显著增加区间宽度的情况下，恢复了所有四分位数的近似均匀覆盖率（0.869-0.938）。诊断分析将最高强度四分位数的残留差距追溯至升高的残留方差（为合并的Q1-Q3水平的2.7倍），而非系统偏差，该不足与精确条件覆盖率的已证实无分布极限相关。基于机器学习的疲劳强度预测的边缘覆盖率主张可能会在工程决策风险最高的地方掩盖系统不可靠性；因此，应常规评估条件覆盖率与边缘覆盖率。
英文摘要
Predicting fatigue failure in steel components experimentally is costly, requiring testing across multiple compositions and processing conditions, spurring research on data-driven prediction models. Studies using the NIMS MatNavi steel fatigue dataset often report high point-prediction accuracy but rely on aggregate error metrics, leaving uncertainty about the reliability of individual predictions and whether accuracy is consistent across the fatigue-strength spectrum. This paper is the first to apply conformal prediction to steel fatigue strength, comparing seven interval-construction methods across 50 independent data splits and distinguishing marginal coverage from coverage within specific sub-regions of the predicted property. A gradient-boosting point model achieves an R^2 of 0.976 +/- 0.009 and a mean absolute error of 18.3 +/- 2.3 MPa. Split-conformal prediction provides valid marginal coverage (0.918) but drops to 0.758 in the highest-strength quartile, where design margins are most critical, a pattern also observed with a Gaussian process baseline. Two locally-adaptive methods correct this: a cross-fitted normalised conformal method holds 0.872-0.940 across quartiles at no cost in average width, and Mondrian group-conditional conformal prediction holds the tightest band of any method (0.917-0.939) at a 12% width premium, part of which traces to the more conservative finite-sample quantile level implied by per-group calibration at this sample size. Conformalized quantile regression, by contrast, restores marginal validity but inflates intervals in every quartile without closing the conditional gap. Marginal coverage claims for ML-based fatigue-strength predictions can conceal systematic unreliability precisely where engineering decisions are most risky; therefore, conditional coverage should be routinely assessed alongside marginal coverage.
Comments11 pages, 5 figures, 3 tables. v2: adds Mondrian group-conditional conformal prediction and conformalized quantile regression (seven methods total); quartile cut points are now derived from training-set out-of-fold predictions
