Open AccessCONTEMPORARY CHALLENGES IN TOOL CONDITION MONITORING
Author(s) -
Krzysztof Jemielniak
Publication year - 2019
Publication title -
journal of machine engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 7
eISSN - 2391-8071
pISSN - 1895-7595
DOI - 10.5604/01.3001.0013.0448
Subject(s) - computer science , signal (programming language) , condition monitoring , feature (linguistics) , segmentation , feature extraction , signal processing , artificial intelligence , data mining , industrial engineering , control engineering , engineering , computer hardware , digital signal processing , linguistics , philosophy , electrical engineering , programming language
Implementation of robust, reliable tool condition monitoring (TCM) systems in one of the preconditions of introducing of Industry 4.0. While there are a huge number of publications on the subject, most of them concern new, sophisticated methods of signal feature extraction and AI based methods of signal feature integration into tool condition information. Some aspects of TCM algorithms, namely signal segmentation, selection of useful signal features, laboratory measured tool wear as reference value of tool condition – are nowadays main obstacles in the broad application of TCM systems in the industry. These aspects are discussed in the paper, and some solutions of the problems are proposed.