TY  - CONF
A1  - Clemens, Florian
A1  - Jiang, Yuechi
A1  - Wittstamm, Max
A1  - Hu, Cuihong
A1  - Wang, Weiming
A1  - Stich, Volker
T1  - Tool Wear Prediction Upgrade Kit For Legacy CNC Milling Machines In The Shop Floor
T2  - Proceedings of the Conference on Production Systems and Logistics (CPSL)
N2  - The operation of CNC milling is expensive because of the cost-intensive use of cutting tools. The wear and tear of CNC tools influence the tool lifetime. Today’s machines are not capable of accurately estimating the tool abrasion during the machining process. Therefore, manufacturers rely on reactive maintenance, a tool
change after breakage, or a preventive maintenance approach, a tool change according to predefined tool specifications. In either case, maintenance costs are high due to a loss of machine utilization or premature tool change. To find the optimal point of tool change, it is necessary to monitor CNC process parameters during machining and use advanced data analytics to predict the tool abrasion. However, data science expertise is limited in small-medium sized manufacturing companies. The long operating life of machines often does not justify investments in new machines before the end of operating life. The publication describes a cost-efficient approach to upgrade legacy CNC machines with a Tool Wear Prediction Upgrade Kit. A practical solution is presented with a holistic hardware/software setup, including edge device, and multiple sensors. The prediction of tool wear is based on machine learning. The user interface visualizes the machine condition for the maintenance personnel in the shop floor. The approach is conceptualized and discussed based on industry requirements. Future work is outlined.
KW  - milling
KW  - condition monitoring
KW  - it-architecture
KW  - predictive maintenance
Y1  - 2022
UR  - https://epub.fir.de/frontdoor/index/index/docId/1424
UR  - https://www.repo.uni-hannover.de/handle/123456789/12248
SN  - 2701-6277
SP  - 131
EP  - 140
PB  - publish-Ing.
CY  - Hannover
ER  -