基于迁移学习的砂带磨损状态预测数据集

基于迁移学习的砂带磨损状态预测数据集来源于三磨海达机床2M5540D外圆磨床加工时的多种工艺参数数据和加工时用于测量工件位移变化的电涡流位移传感器采集数据。其中，位移数据由NI-cRIO-9045控制器和NI-9215采集卡搭配使用进行测量，测量数据由Hexagon Leitz PMM-C三坐标测量仪进行测量。数据量为21.2MB。

A dataset for abrasive belt wear state prediction based on transfer learning is sourced from two types of data collected during machining on the Sanmo Haida 2M5540D cylindrical grinding machine: one is various process parameter data, and the other is data collected by an eddy current displacement sensor for detecting workpiece displacement changes during machining. The displacement data was measured using a combined setup of NI-cRIO-9045 controller and NI-9215 acquisition card, while the reference calibrated measurement data was acquired via a Hexagon Leitz PMM-C coordinate measuring machine. The total size of this dataset is 21.2 MB.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于砂带磨损状态预测的数据集，基于迁移学习方法，来源于外圆磨床加工过程中的工艺参数和传感器数据。数据集包含21.2MB的CSV和DOCX文件，共160个文件，旨在支持磨削加工领域的故障诊断和预测研究，属于机械工程学科，由重庆大学在2023年发布。