The University of Massachusetts Amherst
University of Massachusetts Amherst

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Process Automation Lab Research Projects

Current Projects:

  • Dynamic model development by symbolic regression, William LaCava
  • Development of wind turbine models by genetic search, William LaCava
  • Development of models for °uid-structure interaction, William LaCava
  • Gradient-based adaptation of dynamic models, William LaCava
  • Damage isolation in wind turbines by shape analysis of forced vibration, Dylan Chase


Potential Projects

  • Development/adaptation of dynamic macroeconomic models by symbolic regression
  • Development/adaptation of musculoskeletal models by symbolic regression
  • Development/adaptation of controller structures by symbolic regression
  • Adaptation of dynamic models by signature-based analysis


Past Projects:

  • Detection of cracks in aircraft fuselage by dynamic response measurements
  • Signature-based parameter estimation of multi-output dynamic models
  • Sensor location selection for distributed parameter systems
  • In-Flight isolation of degraded turbo-jet engine components
  • Selection of accelerometer locations in civil structures
  • Direct damage localization of civil structures
  • Input profiling in injection molding machines
  • Continuous infeed cylindrical plunge grinding
  • Automatic tuning and regulation of injection molding by the virtual search method
  • Model-based sensor selection for fault diagnosis of helicopter gearboxes
  • Robust residual generation for model-based fault diagnosis
  • Improved manufacturing productivity with recursive constraint bounding
  • Structure-based fault diagnosis of helicopter gearboxes
  • Cycle-time reduction in cylindrical plunge grinding
  • Fuzzy control design of a high speed robot arm
  • Unsupervised pattern classifier for tool breakage detection in turning
  • Helicopter track and balance with artificial neural nets
  • Application of the MVIM method to tool breakage detection in turning
  • Effect of flank wear on the topography of machined surfaces
  • Turning process identification through force transients