钢铁生产过程平直度与力学性能软测量数据集

钢铁生产过程平直度与力学性能软测量数据集包含平直度预测数据集和力学性能预测数据集两部分。平直度预测数据集涉及带钢热连轧工艺流程中精轧工艺环节的数据，主要用于预测板坯的平直度。数据集的时间范围覆盖了生产过程中的多个周期，时间精度达到秒级，确保对过程中的动态变化进行精准监控。空间范围包括热连轧过程中的各个关键环节，空间精度涵盖了从原料输入到成品输出的每一阶段，数据采集覆盖了工艺设备的不同位置，确保了全面性和代表性。计算方式采用数据驱动的方法，结合传感器数据与实时反馈信息，通过回归模型和机器学习算法实现平直度预测。力学性能预测数据集则包括带钢热连轧工艺流程中的一级数据和二级数据两部分，一级数据包括原材料及其初步处理过程的数据，二级数据涉及热连轧过程中的力学性能参数数据，涵盖了温度、应力、应变等多个维度。数据的时间精度为分钟级，空间精度则体现在不同工艺环节的实时监测数据采集。数据处理过程中，通过高效的数据清洗与质量控制措施，确保了数据的准确性和完整性。数据集的潜在利用价值在于为钢铁生产中的平直度控制与力学性能优化提供了有力的数据支持，可广泛应用于生产过程优化、质量控制及智能制造等领域，进一步推动钢铁行业的数字化转型与智能化发展。

Flatness and mechanical properties soft sensing dataset for steel production processes consists of two parts: flatness prediction dataset and mechanical properties prediction dataset.
The flatness prediction dataset involves data collected from the finishing rolling stage of the hot strip continuous rolling production workflow, and is primarily used to predict the flatness of slabs. The time span of this dataset covers multiple production cycles, with a second-level time precision, enabling accurate monitoring of dynamic changes throughout the production process. Its spatial scope covers all key links in the hot rolling process, with spatial precision covering every stage from raw material input to finished product delivery. Data is collected at various positions of the process equipment, ensuring the comprehensiveness and representativeness of the dataset. The calculation adopts a data-driven approach, combining sensor data and real-time feedback information, and achieves flatness prediction via regression models and machine learning algorithms.
The mechanical properties prediction dataset includes two subsets: Level 1 data and Level 2 data from the hot strip continuous rolling production process. Level 1 data covers raw materials and their preliminary treatment processes, while Level 2 data involves mechanical property parameter data collected during the hot rolling process, covering multiple dimensions such as temperature, stress, and strain. The time precision of this dataset is minute-level, and its spatial precision is reflected in real-time monitoring data collection across different process stages. During the data processing phase, efficient data cleaning and quality control measures are adopted to guarantee the accuracy and integrity of the data.
The potential application value of this dataset lies in providing robust data support for flatness control and mechanical property optimization in steel production. It can be widely applied in fields such as production process optimization, quality control and intelligent manufacturing, further advancing the digital transformation and intelligent development of the steel industry.

搜集汇总

背景与挑战

背景概述

该数据集聚焦钢铁生产过程中的平直度与力学性能预测，由平直度预测数据集和力学性能预测数据集组成，涵盖带钢热连轧工艺的秒级和分钟级动态监测数据。它采用数据驱动方法，结合传感器数据与机器学习算法，旨在支持生产过程优化、质量控制和智能制造，推动钢铁行业数字化转型。