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Volume 4 Nano Research & Applications
ISSN: 2471-9838
JOINT EVENT
October 04-05, 2018 Moscow, Russia
&
2nd Edition of International Conference on
26th International Conference on Advanced Nanotechnology
Materials Technology and Manufacturing Innovations
Advanced Nanotechnology 2018
& Materials-Manufacturing 2018
October 04-05, 2018 Page 36
Sergey A Shevchik
Swiss Federal Laboratories for Material Science and Technology, Switzerland
Sergey A Shevchik, Nano Res Appl 2018, Volume 4
DOI: 10.21767/2471-9838-C5-020
Machine learning: A new paradigm for process monitoring in industry 4.0
Industry 4.0 is a new concept that incorporates multiple cutting edge technologies into a single environment and promises
fast, high quality and cheap manufacturing capabilities. In this philosophy the human participation in routine tasks of
the manufacturing process is minimized, or even excluded, and the process monitoring and decision making is delegated
to machines. From this perspective the recent advances in machine learning (ML) are materializing the ideas of Industry
4.0 and give the directions for the further developments. The present work is an Industry 4.0 approach that employs the
latest advances in ML creating monitoring systems for several industrial processes. In particular, the quality monitoring
for laser welding, additive manufacturing, fracture mechanics and friction of mechanical parts is presented. Our technique
relies on the measurements of the versatile physical parameters of the real processes that are unified in a single parameter
space within ML framework. This information further is processed to obtain higher context information. This implies
the search of the unique signatures of the momentary, quality-critical events that are extracted by the algorithms from a
continuous signals flow. This approach, combined with the high sensitive detectors, allowed observing separate groups of
the momentary events with a time resolution within (10-500) millisecond range thus discovering a promising precision
of such systems in the future. The realization of this approach for real-time monitoring was investigated as well and the
feasibility of this on the example of the laser welding was shown.
Biography
Shevchik S got his background in laser physics and bio photonics and later enriched it participating in a number of projects in machine learning and artificial
intelligence. This dual expertise allowed creating a number of unique techniques, in which the artificial intelligence is employed to understand the physics of
a number of industrial processes and to go beyond the human possibilities in process monitoring.