TY - JOUR

T1 - Computational methods for the detection of wear and damage to milling tools br

AU - Ninevski, Dimitar

AU - Thaler, Julia

AU - O'Leary, Paul

AU - Klünsner, Thomas

AU - Mücke, Manfred

AU - Hanna, Lukas

AU - Teppernegg, Tamara

AU - Treichler, Martin

AU - Peissl, Patrick

AU - Czettl, Christoph

PY - 2022/10

Y1 - 2022/10

N2 - The current paper presents a new computational approach to detect wear and damage to milling tools' cutting edges. The proposed approach is independent from exact information on tool-workpiece interaction conditions and only requires that they remain constant for compared milling operations. Additionally, the approach was thoroughly tested on time-series data obtained from an industrial-scale milling process, instrumented by commercially available instrumentation equipment, during which 18 identical parts were milled. The time-series data contains the bending moments in the x and y directions as well as the torque and tension acting on the milling tool. Some measures used are systematic in nature, based on shape, rotation and work needed for milling, whereas others are statistical in nature, describing the change in the distribution of the data. All of the measures proposed in the current work are relative and mutually invariant, meaning they address different information content of the data independently. A comparison of the mentioned measures with the real-world damage evolution of the milling tool's cutting edges for multiple produced parts yielded consistent results and suggests a high potential for practical tool damage detection in industrial production.

AB - The current paper presents a new computational approach to detect wear and damage to milling tools' cutting edges. The proposed approach is independent from exact information on tool-workpiece interaction conditions and only requires that they remain constant for compared milling operations. Additionally, the approach was thoroughly tested on time-series data obtained from an industrial-scale milling process, instrumented by commercially available instrumentation equipment, during which 18 identical parts were milled. The time-series data contains the bending moments in the x and y directions as well as the torque and tension acting on the milling tool. Some measures used are systematic in nature, based on shape, rotation and work needed for milling, whereas others are statistical in nature, describing the change in the distribution of the data. All of the measures proposed in the current work are relative and mutually invariant, meaning they address different information content of the data independently. A comparison of the mentioned measures with the real-world damage evolution of the milling tool's cutting edges for multiple produced parts yielded consistent results and suggests a high potential for practical tool damage detection in industrial production.

KW - Condition monitoring

KW - Milling tool damage

KW - Time-series sensor data

U2 - 10.1016/j.jmapro.2022.07.030

DO - 10.1016/j.jmapro.2022.07.030

M3 - Article

VL - 82.2022

SP - 78

EP - 87

JO - Journal of manufacturing processes

JF - Journal of manufacturing processes

SN - 1526-6125

IS - October

ER -