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.

Laser baseplate Crystal Driver electronic Laser diode

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