Project Goals
Immediate
- Equipment status identification
- Feed rate prediction from energy
- G-code interpretation
Mid-term
- G-code interpretation and energy prediction
- Equipment anomaly detection
- Realization of developed methodologies - IIoT
Long-term
- Real-time energy monitoring
- Real-time anomaly detection
- Equipment's operations optimization
IIoT Dashboards
Real-time energy data from FANUC ROBONANO located at MINLAB - Energy Monitoring Dashboard
Real-time anomaly detection of FANUC ROBONANO located at MINLAB - Equipment Status
Why do we do it?
Manufacturing is responsible for almost 45% of total energy consumption in the US and a similar portion in other countries. Machine energy consumption accounts for a big portion of 45% and thus understanding how machine tools consume energy helps to minimize energy consumption by design optimization of the machine tools, strategic operation control, energy balance on the production line, and energy footprint and control of supply chain. Monitoring and control of horizontal and vertical integration of total manufacturing infra can be achieved by IIoT (Industrial Internet of Thing) with a similar manufacturing paradigm called smart manufacturing, digital manufacturing, and industry 4.0.
More detailsSome Case Studies
Identifying the equipment state just from the energy consumption data.
- Setup for data acquisition
- Development of application to correlate the G-code with equipment's energy
- Framework for model development and training
- Inference on edge and/or cloud
A real-time anomaly detection system for manufacturing machines.
- Custom designed DAQ device to retrofit the CNC machines
- Data pipeline design to handle, process and infer at AWS cloud
- Correlating the energy components with the anomaly identification capability
- Open sourced the code for further development - https://github.com/vigneshuw/machine_fault_identification
- Current Work - Reinforcement learning approach to enable model monitoring and model updates in real-time
