Laboratory for Machine Tools WZL at RWTH Aachen University. Flexible ... Strategies for the Automated Assembly of Solid
Flexible Combination of Optical Metrology Strategies for the Automated Assembly of Solid State Lasers Prof. Dr.-Ing. Robert Schmitt, M. Eng. Alberto Pavim* Laboratory for Machine Tools WZL at RWTH Aachen University Vision of Integrative Production Technology resolution of the polylemma of production reduced dilemma
2020
Introduction
Inside the scope of the “Aachen House of Integrative Production” research initiative, new integrative production technologies for the resolution of the production 2009 polylemma are being developed at the RWTH Aachen University. The assembly dilemma of a miniaturised diode-pumped solid-state (DPSS) laser for marking applications is one example of a research project. Especially the field of precision asvaluescope timeline orientation sembly of optical components is still characterised by a significant amount of Figure 1: The polylemma of production manual procedures. These can reach up to 80% of the total manufacturing costs which states a strong demand for automation in this sector. The project goal is the reduction of manual manufacturing processes for a cost-effective assembly through automated and self-optimising assembly strategies. These strategies are achieved by the combination of flexible manufacturing, sensing and information technologies. scale
planningorientation
Laser design
Assembly tool
The miniaturised laser system is arranged in a compact and planar configuration (Figure 2, right) that facilitates the automated assembly. All the optical components can be positioned and assembled from above onto the laser coated ceramic carrier plate.
Figure 2: From manual to automated assembly allowed by a new planar laser design knowledge base manipulation soldering robot
Automated assembly concept
illumination robot
reference marks camera-based microlaser beam positioning analysis unit
laser baseplate
robot-based vision system
The automated assembly approach is based on a flexible assembly module consisting of three robots (Figure 3): 1) a manipulation and soldering robot for picking, positioning and soldering the components on the laser plate, 2) a machine vision robot for different measurement purposes and 3) an illumination robot to support the image acquisition tasks. Sensor data fusion is required to handle the different information acquired and processed by the system. This process occurs in three distinct levels.
Figure 3: Automated assembly strategy with three robots assisted by optical measurement systems Fusion levels 3rd level (decision level) Combined decisions
Examples X
2nd
level (information level) Extracted object features
Model
Failure State
Interpretation
Action
ideal state
measurement deviation
reason for the failure
resolution of the failure
Reference value: perfect laser beam at the CCD Sensor
Effective value: unexpected behaviour of the laser beam
Laser beam is strongly reflected by the crystal housing
e.g. identification and resolution of Failure States (expert system)
e.g. image correlation for a complete laser baseplate image acquisition (reference marks)
1st level (data level) Raw data from sensors
Choose adequate optic elements
camera-based laser beam analysis
robot-based vision system
Laser reflection pattern on crystal housing
Laser beam
Laser baseplate
e.g. filtering noise, different illumination strategies
Crystal housing Laser diode
Unexpected laser behaviour
Figure 4: Data fusion strategy for different measurement purposes. The third level looks for and solves assembly failure states coordinated by an expert system.
Fault tolerance capabilities The biggest challenge is to identify and interpret the reasons of assembly failure states, as well as provide feedback to return to a normal assembly state. The acquisition of information from both optical systems is required and supported by an expert system, which retrieves possible solutions for the identified failure states. * Scholarship holder of the Brazilian CNPq
