Identification of machine tools linear axes performance using on-machine embedded inertia measurement units

QC 20180517
The current trend in manufacturing industry is from mass production towards flexible and adaptive manufacturing systems and cloud manufacturing. Self-learning machines and robot systems can play an essential role in the development of intelligent manufacturing systems and can be deployed to deal with a variety of tasks that can require flexibility and accuracy. However, in order for the machine tool (physical and control system) to deal with the desired task in a cognitive and efficient manner, the system must be "aware" of its capability and,most importantly,its limitations in order to avoid them and adjust itself to the desired task. Thus, characterization of machine tool accuracy and capability is necessary to realize that. In this study,data from a machine-embedded inertial measurement unit (IMU), consisting of accelerometers and rate gyroscopes,was used for identification of changes in linearand angular errormotions due to changes in operational conditionsor component degradation.The IMU-based results were validated against laser-based measurement results,demonstratingthat the IMU-based method is capable of detecting micrometer-level and microradian-level degradation of machine tool linearaxes.Thus, manufacturers could use themethod to efficientlyand robustly diagnose the condition of their machine tool linear axeswith minimal disruptions to production.

TEKNIK OCH TEKNOLOGIER Maskinteknik Produktionsteknik, arbetsvetenskap och ergonomi hsv//swe
ENGINEERING AND TECHNOLOGY Mechanical Engineering Production Engineering, Human Work Science and Ergonomics hsv//eng
Coordinate measuring machines
Engineering education
Gyroscopes
Intelligent robots
Learning systems
Manufacture
Units of measurement
Adaptive manufacturing
Cloud Manufacturing
Inertia measurement units
Inertial Measurement Unit (IMU)
Intelligent manufacturing system
Laser-based measurement
Machine tool accuracies
Manufacturing industries
Machine tools

Archenti, Andreas,1973-KTH,Maskin- och processteknologi,Design and Management of Manufacturing Systems, DMMS(Swepub:kth)u1lrx7kl (författare)
Donmez, M. A. (författare)
KTHMaskin- och processteknologi (creator_code:org_t)

Ingår i:Laser Metrology and Machine Performance XII - 12th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2017: euspen, s. 65-749780956679093

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