A sensor framework for combined data streams and in-situ characterization of machining processes

• Machining vibrations is a critical phenomenon in the industry as they negatively affect the quality and tool-life. One common avoidance strategy for machining vibrations is the fine-tuning of process parameters, thus leading to longer production time. Our research addresses this challenge and uses different streams of data to classify problematic processes. Data streams of machining parameters, tool position, loads, vibration sensors, together with process plan data and cutting tool usage information, are visualized. Experiments are performed to derive classification criteria. These results are then used to observe vibrations in a five-axis machining center for further process adjustment.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)

Nyckelord

Cyber-physical manufacturing

Data fusion

Manufacturing data analytics

Sensor integration

Classification (of information)

Cutting tools

Machining

Machining centers

Manufacture

Classification criterion

Critical phenomenon

Five-axis machining centers

In-situ characterization

Machining parameters

Machining vibration

Process adjustments

Process parameters

Data streams

Publikations- och innehållstyp

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