The multi sensor-based machining signal fusion to compare the relative efficacy of machine learning based tool wear models 核心 · 已核验

doi:10.7910/dvn/7iajwu

This dataset contains a force dynamometer, accelerometer sensor, acoustic emission sensor, and tool wear values for different milling conditions. For each condition, 12 experiments were conducted. Tool 1 (T1) to Tool 4 (T4) were used to develop the machine learning models and is validated with Tool 5 (T5) to Tool 8 (T8) respectively. This dataset contains raw data taken from each sensor output for each experimental cut. From this dataset, the relative efficacy of machine learning-based tool wear models was developed. Also, two sensor combination was used to compare the sensor effectiveness in tool wear prediction. The dataset shared here is part of the research work published in Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.

落地页

https://doi.org/10.7910/DVN/7IAJWU

许可证

CC-BY-NC-4.0 (判读置信:inferred)

国内可访问性

国内直连:可达 (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

发布年份

2022

设备类型

machine_tool

分发点

故障工况

传感器