Intelligent machining: real-time tool condition monitoring and intelligent adaptive control systems

• Autorzy: Hassan M. , Sadek A. , Attia M. H. , Thomson V.

Identyfikatory

DOI

10.5604/01.3001.0010.8811

• Języki publikacji: EN

Abstrakty

EN

Unmanned manufacturing systems has recently gained great interest due to the ever increasing requirements of optimized machining for the realization of the fourth industrial revolution in manufacturing ‘Industry 4.0’. Real-time tool condition monitoring (TCM) and adaptive control (AC) machining system are essential technologies to achieve the required industrial competitive advantage, in terms of reducing cost, increasing productivity, improving quality, and preventing damage to the machined part. New AC systems aim at controlling the process parameters, based on estimating the effects of the sensed real-time machining load on the tool and part integrity. Such an aspect cannot be directly monitored during the machining operation in an industrial environment, which necessitates developing new intelligent model-based process controllers. The new generations of TCM systems target accurate detection of systematic tool wear growth, as well as the prediction of sudden tool failure before damage to the part takes place. This requires applying advanced signal processing techniques to multi-sensor feedback signals, in addition to using ultra-high speed controllers to facilitate robust online decision making within the very short time span (in the order of 10 ms) for high speed machining processes. The development of new generations of Intelligent AC and TCM systems involves developing robust and swift communication of such systems with the CNC machine controller. However, further research is needed to develop the industrial internet of things (IIOT) readiness of such systems, which provides a tremendous potential for increased process reliability, efficiency and sustainability.

Słowa kluczowe

EN

adaptive control; tool condition monitoring; intelligent machining

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2018
• Tom: Vol. 18, No. 1
• Strony: 5--17
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Hassan M.

• Department of Mechanical Engineering, McGill University, Montreal, QC, Canada

autor

Sadek A.

• Aerospace Structures, Materials and Manufacturing, National Research Council Canada, Montreal, QC, Canada

autor

Attia M. H.

• Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
• Aerospace Structures, Materials and Manufacturing, National Research Council Canada, Montreal, QC, Canada

autor

Thomson V.

• Department of Mechanical Engineering, McGill University, Montreal, QC, Canada

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).