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Objectives

Sceintific and Technical Objectives

How they will be achieved

To maintain high welding quality, part-to-part gap will be controlled within max 0.3 mm for steel; within 0.05 mm for aluminium; and between 0.05 to 0.3 mm for galvanized steel

  • Error budgeting (MS7/MS8)
  • Laser parameter selection (MS10)
  • Fixture design (MS9)
  • Monitoring & root cause analysis (MS11)
  • Process adjustment (MS12)
  • Workstation level evaluation (MS50)

 

A 75% reduction of assembly time will be achieved with RLW when compared to assembly systems in general without RLW.

 

  • Analysis of hybrid part-to-part joining processes (MS3)
  • Selection of candidate stations of RLW (MS2)
  • System layout configuration (MS3)
  • Inputs analysed and scope defined (MS1)
  • RLW workstation layout configuration (MS4/MS5)
  • RLW robot simulation & OLP (MS)
  • Fixture design (MS9)
  • Workstation level evaluation (MS)

 

A 50% reduction of factory floor footprint will be achieved with RLW when compared to systems without RLW.

  • Analysis of hybrid part-to-part joining processes (MS3)
  • Selection of candidate stations for RLW (MS2)
  • System layout configuration (MS3)
  • Inputs analysed and scope defined (MS1)
  • RLW workstation layout configuration (MS4/MS5)
  • Workstation level evaluation (MS5)

A 60% reduction in energy use to create a joint will be achieved with the RLW Navigator as compared to traditional joining  methods.

  • Laser parameter selection (MS10)
  • RLW robot simulation  & OLP (MS6)
  • Workstation level evaluation (MS5)
  • Eco-efficiency evaluation (MS13/MS14)

The deviation of simulation results will be kept within 5% on first moment productivity performance (throughput); within 15% on second order productivity performance (output variance); within 2% on quality performance (system yield); and within 10% on energy performance (energy requirement).

  • Continuous time-discrete state general Markovian models of resources (MS2)
  • Learning and statistical inference (MS2)
  • Approximate analytic methods (MS2)
  • System decomposition (MS2)
  • Continous time production flow models (MS2)
  • Reward models (MS2)
  • Statistical Quality/Process Control (MS2)
  • Root Cause Analysis (MS11)

Inputs analysed and scope defined (MS1)

The proposed RLW Navigator will be able to assemble product families and be resilient to unforeseen process faults and product changes

  • Fixture design (MS9)
  • RLW workstation layout configuration (MS4/MS5)
  • RLW robot simulation & OLP (MS6)
  • Monitoring & root cause analysis (MS11)
  • Process adjustment

 

 

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RLW Navigator is a three year, €3.9M, project funded by the European Commission under the ICT-Factories of the Future programme.  The project has fourteen partners and began in January 2012.

The goal of the project is to develop an engineering platform for an emerging joining technology from the automotive industry, Remote Laser Welding (RLW), that will enable the exploitation of this technology and ultimately support other joining processes.

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