Sustainable Smart Manufacturing

Bringing Industries to Industry 4.0 and Beyond

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Robust fault detection of manufacturing machines enabling widespread deployment

Overarching Research Goals

Immediate

  • Defect detection and identification
  • Anomaly detection
  • Process-physics integrated learning

Midterm

  • Robust model development, robust training
  • Model explainability and model interpretability
  • Out-of-distribution (OOD) detection

Long-term

  • Life-long learning (LML)
  • Learning without forgetting (LwF)
  • OOD integrated learning

Research Team: Vignesh Selvaraj

Through this work we aim to develop systematic approaches to build robust and trustworthy AI for industrial applications. Our work focuses not only building reliable physics informed models for industrial applications and machines, but also ensures their reliability in field. Through our work, we aim to facilitate industries’ transition to industry 4.0 by enabling widespread deployment of the developed models.

Through the course of this research work we have been able to do the following,

  • Detect and identify defects for a manufacturing machine
    • Development reliable model architectures
    • Combining process-physics at the model development stage
  • Detect anomalies with less training data and less-supervision
    • Enable cost-effective model development process for manufacturing machines
    • Enable life-long learning for supervised models
  • Knowledge transfer between machines for widespread deployment and model augmentation (increase in learning tasks)
    • Strategies to deploy a single model across multitude of similar machines
    • Learning without forgetting
  • Explaining the model’s operation to build trust
  • OOD detection

Industrial Case Study

 

          Model Architecture exploiting both spatial and temporal resolution                                Defect Identification

   

 

 

 

 

 

 


Energy Monitoring

Overarching Research Goals

Immediate

  • Equipment status identification from energy consumption
  • Feed rate prediction from energy consumption
  • G-code interpretation, in relevance with energy consumption 

Midterm

  • G-code interpretation and energy prediction
  • Equipment anomaly detection

Long-term

  • Real-time energy consumption monitoring
  • Real-time equipment status identification and anomaly detection
  • Equipment operation optimization constrained by energy consumption

Research Team: Vignesh Selvaraj, Zhicheng Xu

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.

Through the course of this research work, we have been able to do the following,

  • Identify equipment operating status just from the energy data
    • Components active at any point in time
    • Axis in motion at any point in time
  • Develop an application that can interpret G-code and generate working status matrix and model training data
    • The application is open source
    • The application is deployed in AWS and will soon have the ability to predict the energy from G-code
  • Develop a multi-task model that can predict feed rate and simultaneously predict the axis in motion
    • The model can be expanded to carry out more related tasks 
    • The model has an accuracy of ~95%
  • Deploy a real-time monitoring system for a CNC machine at AWS 
    • Real-time monitoring of energy
    • It will soon be integrated with MTConnect

Framework for development and deployment

Model augmentation with multi-tasking


Digital-gradient Cascade Manufacturing of Core-multi-shell Cathode Materials

Research Team: Dr. Sangjin Maeng, in collaboration with Dr. Youngho Shin  @ Argonne National Laboratory

In order to overcome structural and surface instability of Ni-rich cathode materials which causes poor cyclability and thermal stability, cathode materials with a digital-gradient particle structure wrapping Ni-rich high-capacity core with Mn-rich high-stability multi-shells were synthesized successfully by Argonne National Laboratory. Mechanical properties of cathode particles is closely related to battery life and mechanical characterization of synthesized particles are being studied.