Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0

Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables. The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc. Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, n; feed rate, f; and axial depth of cut, aP. The radial depth of cut, ae; was equal to the mill tool radius, r, in all of the slots. These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in X of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.

质量被定义为产品符合设计规范的程度，以及其满足组件功能要求的程度。对于汽车、航空航天等行业而言，由于其严苛的质量要求，制造零件的品质至关重要。然而，当前面临的难题在于，质量评估仅能在工序外（out-of-process）开展，这会造成额外损失——因为生产者只能将不合格零件从生产线移除，并无其他替代方案。
因此，亟需引入基于人工智能（AI）的工具包或解决方案，实现工序内评估，通过各类测量变量预测零件质量。
本类数据集的核心目标是：通过振动信号与切削参数信息估算表面粗糙度，从而实现对制造组件或零件的最终质量监控。表面粗糙度是质量控制中的关键特征，其定义为实际表面的法向矢量相较于理想形态的偏差量。
由于传统的粗糙度测量属于离线后处理流程，若能够实现在线估算该数值，可为生产线带来多方面益处：包括缩减工时与成本、提升能源利用效率、减少刀具与机床的不必要磨损等。当检测到某零件的表面质量未达预期标准时，可针对后续工序采取一系列校正措施，例如：降低进给率百分比、提高主轴转速占比，或是减小单次工序的轴向切削深度等。
GAMHE 5.0试点生产线的工作站4（WS4）配备了Kern Evo高精度加工中心，其最高主轴转速可达50000转/分钟，搭载Blum激光系统，用于开展微铣削（micro-milling）与微钻孔（micro-drilling）作业。
在本实验数据集中，实验过程中共考量五类切削参数：主轴转速$n$、进给率$f$以及轴向切削深度$a_P$。径向切削深度$a_e$在所有槽铣工序中均等于铣刀半径$r$。
本次实验采用直径分别为0.3mm、0.5mm、0.8mm与1mm的铣刀，在烧结钨铜合金（sintered tungsten-copper alloy，W78Cu22）基材上开展微铣削作业。
针对每一次微铣削操作，采集的数据包括三轴振动的均方根（root mean square, RMS）值与峰值。此外，还同步采集了五类切削相关参数：样本最后一点的X轴位置、进给率、主轴转速、刀具半径以及轴向切削深度。

搜集汇总

背景与挑战

背景概述

该数据集专注于制造业中的质量控制，通过微机械铣削实验收集振动信号和切削参数数据，旨在利用人工智能技术在线预测表面粗糙度，以优化生产流程并减少成本。数据集来自GAMHE 5.0试点线，包含微铣削操作在钨铜合金上的实验数据，涉及多种铣刀直径和关键参数，适用于人工智能在制造领域的应用研究。