JUST_CAS_milling

Real-time monitoring of milling parameters is essential to improve machining efficiency and quality, especially for the workpieces with complex geometry. Its main task is to build the relationship between the parameters and the monitoring data. As the relationship is challenging to be established solely through mechanism-driven or data-driven methods, the physics informed method, based on prior physical laws between physical signals and milling parameters, becomes the optimal method. However, this method is limited due to the lack of a high-quality dataset. Therefore, a multi-sensor monitoring dataset for the milling process with various milling parameters and milling materials is built. The variables include cutting depth, cutting width, feed rate, spindle speed and workpiece materials (aluminium alloy 7030 and CK45 steel). The multi-sensor includes force, vibration, noise, and current. A dataset comprising 115 samples is built, including 100 samples collected using the 'all factors' method, and 15 slot milling samples using two different workpiece materials. The 15 slot milling samples are used to calibrate mechanical milling force coefficients, which is beneficial for developing a physics-informed machine learning algorithm.

实时监测铣削参数对于提升加工效率与加工质量至关重要，针对几何形状复杂的工件而言尤为如此。该实时监测任务的核心目标在于构建铣削参数与监测数据之间的关联关系。由于仅通过机理驱动方法或数据驱动方法难以建立上述关联，基于物理信号与铣削参数间先验物理规律的物理信息驱动方法（physics informed method）遂成为最优解决方案。然而，该方法受限于高质量数据集的匮乏。为此，本研究构建了一款覆盖多类铣削参数与铣削材料的铣削过程多传感器监测数据集。数据集涉及的变量包括切削深度、切削宽度、进给速度、主轴转速以及工件材料（7030铝合金（aluminium alloy 7030）与CK45钢）。该数据集搭载的多传感器涵盖测力、振动、噪声与电流四类采集信号。本次构建的数据集共包含115组样本：其中100组采用"全因子方法（all factors method）"采集，剩余15组为采用两种不同工件材料的槽铣削样本。该15组槽铣削样本可用于校准铣削机械力系数，有助于后续物理信息驱动机器学习算法的开发。

搜集汇总

背景与挑战

背景概述

JUST_CAS_milling是一个铣削过程多传感器监测数据集，包含115个样本，涵盖多种铣削参数和两种工件材料(铝合金7030和CK45钢)，采集了力、振动、噪声和电流等多源传感器数据，旨在支持物理信息机器学习算法的开发。