Context sensitive recognition of abrupt changes in cutting process
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2010
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This paper presents a new generic approach to real-time monitoring of abrupt changes in cutting process. Proposed method is based on hierarchical fuzzy clustering of patterns obtained from discrete wavelet transform (DWT) of acquired signals correlated with cutting force variation in time. Cutting process is naturally highly dynamical and normally consists of mixture of various dynamic phenomena related to the chip formation process and dynamical responses of machining system, workpiece and tool itself. These phenomena are characterized by different time duration. The class of phenomena related to abrupt changes during short time interval is of special importance since they correspond to the most dramatic changes in cutting process, such as various kinds of tool failure or workpiece damage or even breakage. Due to their short time duration, discovery and recognition of these phenomena is extremely difficult. To solve given problem we have chosen DWT, fuzzy clustering and finite state a...utomata as a formal platform for its analysis. Beside its good time localization properties, DWT is, due to asymmetric and irregular shapes of wavelets, especially suitable for analysis of signals having sharp changes or even discontinuities. Given properties make DWT an efficient means for extraction of representative and reliable information contents, thus making good basis for extraction of discriminative and representative features (as DWT coefficients combinations) for classification that will follow. Robustness of specific pattern recognition and learning may be achieved only by taking into consideration wider context. Therefore, in tool condition pattern recognition we have considered the entire context of changes in cutting process state space that precedes and appears after the phenomenon which should be recognized. The cutting process behavior and its evolution in time are considered rather then momentary state which is represented as a point in adopted feature hyperspace of classification machine. Efficiency and practical applicability of developed method is evaluated by extensive experiments in laboratory conditions.
Ključne reči:
Dynamic pattern recognition / Cutting process monitoringIzvor:
Expert Systems With Applications, 2010, 37, 5, 3721-3729Izdavač:
- Pergamon-Elsevier Science Ltd, Oxford
DOI: 10.1016/j.eswa.2009.11.053
ISSN: 0957-4174
WoS: 000274594300023
Scopus: 2-s2.0-73349097841
Kolekcije
Institucija/grupa
Mašinski fakultetTY - JOUR AU - Petrović, Petar AU - Jakovljević, Živana AU - Milačić, Vladimir PY - 2010 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1054 AB - This paper presents a new generic approach to real-time monitoring of abrupt changes in cutting process. Proposed method is based on hierarchical fuzzy clustering of patterns obtained from discrete wavelet transform (DWT) of acquired signals correlated with cutting force variation in time. Cutting process is naturally highly dynamical and normally consists of mixture of various dynamic phenomena related to the chip formation process and dynamical responses of machining system, workpiece and tool itself. These phenomena are characterized by different time duration. The class of phenomena related to abrupt changes during short time interval is of special importance since they correspond to the most dramatic changes in cutting process, such as various kinds of tool failure or workpiece damage or even breakage. Due to their short time duration, discovery and recognition of these phenomena is extremely difficult. To solve given problem we have chosen DWT, fuzzy clustering and finite state automata as a formal platform for its analysis. Beside its good time localization properties, DWT is, due to asymmetric and irregular shapes of wavelets, especially suitable for analysis of signals having sharp changes or even discontinuities. Given properties make DWT an efficient means for extraction of representative and reliable information contents, thus making good basis for extraction of discriminative and representative features (as DWT coefficients combinations) for classification that will follow. Robustness of specific pattern recognition and learning may be achieved only by taking into consideration wider context. Therefore, in tool condition pattern recognition we have considered the entire context of changes in cutting process state space that precedes and appears after the phenomenon which should be recognized. The cutting process behavior and its evolution in time are considered rather then momentary state which is represented as a point in adopted feature hyperspace of classification machine. Efficiency and practical applicability of developed method is evaluated by extensive experiments in laboratory conditions. PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Expert Systems With Applications T1 - Context sensitive recognition of abrupt changes in cutting process EP - 3729 IS - 5 SP - 3721 VL - 37 DO - 10.1016/j.eswa.2009.11.053 ER -
@article{ author = "Petrović, Petar and Jakovljević, Živana and Milačić, Vladimir", year = "2010", abstract = "This paper presents a new generic approach to real-time monitoring of abrupt changes in cutting process. Proposed method is based on hierarchical fuzzy clustering of patterns obtained from discrete wavelet transform (DWT) of acquired signals correlated with cutting force variation in time. Cutting process is naturally highly dynamical and normally consists of mixture of various dynamic phenomena related to the chip formation process and dynamical responses of machining system, workpiece and tool itself. These phenomena are characterized by different time duration. The class of phenomena related to abrupt changes during short time interval is of special importance since they correspond to the most dramatic changes in cutting process, such as various kinds of tool failure or workpiece damage or even breakage. Due to their short time duration, discovery and recognition of these phenomena is extremely difficult. To solve given problem we have chosen DWT, fuzzy clustering and finite state automata as a formal platform for its analysis. Beside its good time localization properties, DWT is, due to asymmetric and irregular shapes of wavelets, especially suitable for analysis of signals having sharp changes or even discontinuities. Given properties make DWT an efficient means for extraction of representative and reliable information contents, thus making good basis for extraction of discriminative and representative features (as DWT coefficients combinations) for classification that will follow. Robustness of specific pattern recognition and learning may be achieved only by taking into consideration wider context. Therefore, in tool condition pattern recognition we have considered the entire context of changes in cutting process state space that precedes and appears after the phenomenon which should be recognized. The cutting process behavior and its evolution in time are considered rather then momentary state which is represented as a point in adopted feature hyperspace of classification machine. Efficiency and practical applicability of developed method is evaluated by extensive experiments in laboratory conditions.", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Expert Systems With Applications", title = "Context sensitive recognition of abrupt changes in cutting process", pages = "3729-3721", number = "5", volume = "37", doi = "10.1016/j.eswa.2009.11.053" }
Petrović, P., Jakovljević, Ž.,& Milačić, V.. (2010). Context sensitive recognition of abrupt changes in cutting process. in Expert Systems With Applications Pergamon-Elsevier Science Ltd, Oxford., 37(5), 3721-3729. https://doi.org/10.1016/j.eswa.2009.11.053
Petrović P, Jakovljević Ž, Milačić V. Context sensitive recognition of abrupt changes in cutting process. in Expert Systems With Applications. 2010;37(5):3721-3729. doi:10.1016/j.eswa.2009.11.053 .
Petrović, Petar, Jakovljević, Živana, Milačić, Vladimir, "Context sensitive recognition of abrupt changes in cutting process" in Expert Systems With Applications, 37, no. 5 (2010):3721-3729, https://doi.org/10.1016/j.eswa.2009.11.053 . .