结构钢乘用车轮毂冲击强度评估数据

结构钢乘用车轮毂冲击强度评估数据贯通产品全生命周期，为各环节提供量化决策依据。在上游环节，数据为材料与供应商遴选提供可追溯证据，支撑企业和行业试验规范的制定与完善，并为有限元仿真模型提供真实工况校准依据。在中游环节，数据支持设计方案比选与轻量化优化迭代，通过应力比与极限应力比快速评估方案可行性，识别设计偏保守案例以挖掘减重潜力；同时沉淀标准化工况与边界条件模板，将安全裕度等关键指标参数化下发至制造环节，实现工艺规划指导与质量自动化判定。在下游环节，数据固化为招投标与采购技术条款，作为进出厂检验及在役抽检的量化判定依据，提供不合格、临界状态、预警区间等多级预警，并为第三方认证与监管合规提供数据佐证。此外，该数据集可作为机器学习训练样本，构建"几何参数—材料特性—强度判定"知识图谱，打通设计、制造、验收、运维的数字闭环，持续实现降本、缩短周期、提升可靠性的螺旋式改进。1. 数据采集 采集内容包括结构钢轮毂结构的几何参数与主要材料特性（如屈服强度 σ_s、极限强度 σ_u），并定义典型使用场景下的冲击载荷（施加压力:16500 N; 施力方向:沿-z轴方向; 施力位置:冲头0度受压面）。基于有限元方法开展冲击动力学仿真，提取最大等效应力 σ_max 与最大位移，以反映结构钢轮毂在冲击载荷作用下的结构响应。 2. 数据处理 （1）应力比：Rs = σ_max / σ_s （2）极限应力比：Ru = σ_max / σ_u （3）安全裕度：M = 1 − Rs 3. 数据应用（参考建议） 判定顺序：不合格 → 设计偏保守 → 设计合理 → 临界状态 → 预警区间。 （1）不合格：Rs ≥ 1.0 或 Ru ≥ 0.8。说明：存在失效风险；需调整结构方案或使用更高强度材料后复评。 （2）设计偏保守：Rs ≤ 0.6 且 Ru ≤ 0.3。说明：材料利用率较低；在满足安全性与刚度前提下可开展轻量化或成本优化。 （3）设计合理：满足下列任一：a)Rs ≤ 0.6 且 0.3 < Ru ≤ Rs；b)0.6 < Rs ≤ 0.8 且 Ru ≤ 0.6。说明：材料强度发挥充分，安全与经济性平衡。 （4）临界状态：0.8 < Rs ≤ 0.9 且 Ru ≤ 0.6。说明：已接近屈服；需加强工况监测与抽检，关注长期疲劳与异常集中载荷。 （5）预警区间：满足下列任一（且未命中以上区间）a)0.6 < Rs ≤ 0.8 且 0.6 < Ru ≤ Rs；b)0.8 < Rs ≤ 0.9 且 0.6 < Ru ≤ Rs；c)0.9 < Rs < 1.0 且 Ru < 0.8。说明：强度利用度偏高或极限强度储备偏低；宜优化关键部位几何与连接，或提升材料等级，并实施更严密的质量与工况监控。

This dataset for impact strength evaluation of structural steel passenger car wheels spans the entire product lifecycle, providing quantitative decision-making support for each stage.
In the upstream stage, the dataset provides traceable evidence for material and supplier selection, supports the formulation and improvement of enterprise and industry test specifications, and offers real working condition calibration bases for finite element simulation models.
In the midstream stage, the dataset supports design scheme comparison and lightweight optimization iteration, rapidly evaluates scheme feasibility through stress ratio and ultimate stress ratio, identifies overly conservative design cases to tap weight reduction potential; meanwhile, it accumulates standardized working condition and boundary condition templates, parameterizes key indicators such as safety margin and delivers them to the manufacturing stage to realize process planning guidance and automated quality determination.
In the downstream stage, the dataset is formalized into bidding and procurement technical clauses, serving as the quantitative judgment basis for factory entry/exit inspection and in-service sampling inspection, providing multi-level early warnings including non-conforming, critical state and early warning interval, and offering data evidence for third-party certification and regulatory compliance.
In addition, this dataset can be used as machine learning training samples to construct a knowledge graph of 'geometric parameters – material properties – strength judgment', forming a digital closed-loop spanning design, manufacturing, acceptance and operation and maintenance, and continuously achieving spiral improvements in cost reduction, cycle shortening and reliability improvement.

1. Data Collection
The collection content includes the geometric parameters of the structural steel wheel structure and main material properties (such as yield strength σ_s, ultimate strength σ_u), and defines the impact load under typical usage scenarios (applied pressure: 16500 N; force direction: along the -z axis; force application position: 0-degree pressure surface of the indenter). Impact dynamics simulation is carried out based on the finite element method, and the maximum equivalent stress σ_max and maximum displacement are extracted to reflect the structural response of the structural steel wheel under impact load.

2. Data Processing
(1) Stress Ratio: Rs = σ_max / σ_s
(2) Ultimate Stress Ratio: Ru = σ_max / σ_u
(3) Safety Margin: M = 1 − Rs

3. Data Application (Reference Recommendations)
Judgment Sequence: Non-conforming → Overly Conservative Design → Reasonable Design → Critical State → Early Warning Interval.
(1) Non-conforming: Rs ≥ 1.0 or Ru ≥ 0.8. Note: There is a risk of failure; the structural scheme needs to be adjusted or higher-strength materials should be used for re-evaluation.
(2) Overly Conservative Design: Rs ≤ 0.6 and Ru ≤ 0.3. Note: The material utilization rate is low; lightweight or cost optimization can be carried out under the premise of meeting safety and stiffness requirements.
(3) Reasonable Design: Satisfy any of the following:
a) Rs ≤ 0.6 and 0.3 < Ru ≤ Rs;
b) 0.6 < Rs ≤ 0.8 and Ru ≤ 0.6.
Note: The material strength is fully utilized, achieving a balance between safety and economic efficiency.
(4) Critical State: 0.8 < Rs ≤ 0.9 and Ru ≤ 0.6. Note: It is close to yielding; working condition monitoring and sampling inspection should be strengthened, and attention should be paid to long-term fatigue and abnormal concentrated loads.
(5) Early Warning Interval: Satisfy any of the following (and do not fall into the above intervals):
a) 0.6 < Rs ≤ 0.8 and 0.6 < Ru ≤ Rs;
b) 0.8 < Rs ≤ 0.9 and 0.6 < Ru ≤ Rs;
c) 0.9 < Rs < 1.0 and Ru < 0.8.
Note: The strength utilization rate is high or the ultimate strength reserve is low; it is advisable to optimize the geometry and connection of key parts, upgrade the material grade, and implement stricter quality and working condition monitoring.

搜集汇总

背景与挑战

背景概述

该数据集包含509条CSV格式记录，专注于结构钢乘用车轮毂在冲击载荷下的强度评估，关键字段包括最大等效应力、材料屈服强度、应力比和安全裕度等，用于量化分析轮毂的结构响应。数据集应用于产品全生命周期，支持从材料遴选、设计优化到制造质量控制的决策，并可作为机器学习训练样本，实现降本增效和可靠性提升。