碳纤维轮毂刚度识别预测数据

通过综合分析碳钎维轮毂样式的轮廓点采样数据，生成相应的三维CAD模型，并利用模态分析技术计算出轮毂的固有频率f和质量m，进而应用简谐振子公式计算出轮毂的刚k。利用大量碳钎维轮毂刚度识别预测数据训练深度学习模型，实现通过输入轮毂样式图片直接预测刚度值，为轮毂设计、制造及性能优化提供科学依据。

通过刚度预测数据，快速评估不同碳钎维轮毂设计的力学性能，缩短设计周期，提高设计效率。确保生产的轮毂具备理想的刚度，增强产品在市场中的竞争力。汽车企业可以获得精准的碳钎维轮毂刚度识别预测数据，支持悬挂系统和动力传动系统的优化设计，提升整车的平衡性和操控性。汽车维修与检测中心，通过刚度值评估轮毂的使用状态，及时发现和预防潜在的安全隐患。
1. 数据采集
采集海量不同碳钎维轮毂样式的轮廓点采样数据，每个样本由一组二维坐标点构成，精确描述轮毂的几何形状。通过生成碳钎维轮毂的三维CAD模型并进行模态分析，获取每个轮毂对应的固有频率f和质量m，为刚度k的计算和碳钎维轮毂刚度识别模型训练提供精准的标签数据。
2. 数据处理
（1）数据预处理
对采集到的碳钎维轮毂点数据进行标准化处理，包括平移、缩放和旋转，以消除不同样本之间的尺度和位置差异，确保数据的一致性和可比性。
（2）特征提取
基于二维碳钎维轮毂轮廓点采样数据，生成对应的三维CAD模型。可以采用拉伸、旋转或其他建模技术，将二维点集转化为三维几何形状。对生成的三维CAD模型进行模态分析，计算出每个碳钎维轮毂的固有频率f和质量m。这些参数作为刚度k计算及碳钎维轮毂刚度识别预测数据的标签数据。

3. 数据应用
利用训练好的碳钎维轮毂刚度识别预测模型，通过输入碳钎维轮毂轮廓点数据，预测质量m和固有频率f，然后应用简谐振子公式计算刚度k：k=m×(2πf)^2。其中：m：预测的质量，单位为kg。f：预测的固有频率，单位为Hz。k：刚度，单位为kN/m。

By comprehensively analyzing the contour point sampling data of carbon fiber wheel hub styles, the corresponding 3D CAD models are generated. The modal analysis technology is utilized to calculate the natural frequency f and mass m of the hub, and then the stiffness k of the hub is computed via the simple harmonic oscillator formula. A deep learning model is trained using a large volume of carbon fiber wheel hub stiffness recognition and prediction data, enabling direct stiffness value prediction by inputting hub style images, providing a scientific basis for wheel hub design, manufacturing and performance optimization.

Using the stiffness prediction data, the mechanical properties of different carbon fiber wheel hub designs can be rapidly evaluated, shortening the design cycle and improving design efficiency. This ensures that the produced hubs have ideal stiffness, enhancing the product's market competitiveness. Automotive enterprises can acquire accurate carbon fiber wheel hub stiffness recognition and prediction data to support the optimal design of suspension systems and power transmission systems, improving the balance and handling performance of the entire vehicle. Automotive maintenance and testing centers can evaluate the service status of hubs through stiffness values, timely detecting and preventing potential safety hazards.

1. Data Collection
Collect a massive amount of contour point sampling data for various carbon fiber wheel hub styles. Each sample is composed of a set of 2D coordinate points, accurately describing the geometric shape of the hub. By generating 3D CAD models of carbon fiber wheel hubs and conducting modal analysis, the natural frequency f and mass m corresponding to each hub are obtained, providing precise label data for stiffness k calculation and carbon fiber wheel hub stiffness recognition model training.

2. Data Processing
(1) Data Preprocessing
Standardize the collected carbon fiber wheel hub point data, including translation, scaling and rotation, to eliminate scale and position discrepancies between different samples, ensuring data consistency and comparability.

(2) Feature Extraction
Generate the corresponding 3D CAD models based on the 2D carbon fiber wheel hub contour point sampling data. Techniques such as extrusion, rotation or other modeling methods can be adopted to convert the 2D point sets into 3D geometric shapes. Perform modal analysis on the generated 3D CAD models to calculate the natural frequency f and mass m of each carbon fiber wheel hub. These parameters serve as label data for stiffness k calculation and carbon fiber wheel hub stiffness recognition prediction data.

3. Data Application
Using the trained carbon fiber wheel hub stiffness recognition and prediction model, input the carbon fiber wheel hub contour point data to predict the mass m and natural frequency f, then calculate the stiffness k via the simple harmonic oscillator formula: k = m × (2πf)². Where: m: predicted mass, unit: kg. f: predicted natural frequency, unit: Hz. k: stiffness, unit: kN/m.

搜集汇总

数据集介绍

特点

该数据集包含701条碳纤维轮毂的刚度识别预测数据，每条数据包括轮毂轮廓坐标点、质量、固有频率和刚度等关键信息。数据集应用于轮毂设计和性能优化，通过深度学习模型预测刚度值，为汽车制造和维修提供科学依据。