Technical Name Efficiency Boosting System for Computer Numerical Control Milling Machine Based on AIBig Data Analytics
Project Operator National TsingHua University
Project Host 簡禎富
This technique combines more than one AI models to precisely predict current under noisy data from current sensoroptimizes speed rate based on this AI forecasting model. At the same time, it considers the practical requirementslimitations of workpiece surface roughness, tool machine current load, etc. This technique provides decision supports for optimizing parameters of CNC milling machine to keep high efficiencyenhance energy conservation.
Scientific Breakthrough
Our technology has been developed as a decision support system which is suitable for all numerical control machine, the optimized NC programs can be derived from the NC programs import to the system by NC programmer. Trough our technology, NC milling process can decrease 30 processing time, even those NC programs already optimized by NC programmer, our technoloy still can decrease 8 to 15 processing time.
Industrial Applicability
In metal milling industries, CNC machine is one of the key facilities. We integrate the current sensorCNC machine controller sensor to capture the detailed log during manufacturing processes.The technique we proposed can optimize the parameters of processing to enhance the manufacturing efficiency.  Furthermore, we build the DSS with the AI models to support manufacturing,which could potentially be applied to other industries.
Keyword Smart Sensor Artificial Intelligence Big Data Analytics Machine Learning Computer Numerical Control Milling Machine Parameters Optimization Milling Process Intelligent Manufacturing Industrial transformation Expert System
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