Minimal-effort planning of active alignment processes for beam-shaping optics

Abstract

In science and industry, the alignment of beam-shaping optics is usually a manual procedure. Many industrial applications utilizing beam-shaping optical systems require more scalable production solutions and therefore effort has been invested in research regarding the automation of optics assembly. In previous works, the authors and other researchers have proven the feasibility of automated alignment of beam-shaping optics such as collimation lenses or homogenization optics. Nevertheless, the planning efforts as well as additional knowledge from the fields of automation and control required for such alignment processes are immense. This paper presents a novel approach of planning active alignment processes of beam-shaping optics with the focus of minimizing the planning efforts for active alignment. The approach utilizes optical simulation and the genetic programming paradigm from computer science for automatically extracting features from a simulated data basis with a high correlation coefficient regarding the individual degrees of freedom of alignment. The strategy is capable of finding active alignment strategies that can be executed by an automated assembly system. The paper presents a tool making the algorithm available to end-users and it discusses the results of planning the active alignment of the well-known assembly of a fast-axis collimator. The paper concludes with an outlook on the transferability to other use cases such as application specific intensity distributions which will benefit from reduced planning efforts

Cognitive Self-Optimization in Industrial Assembly

“…It has been shown for specific use cases that unambiguous models and corresponding data acquisition strategies can be found based on a data basis retrieved either from optical simulation or from empirical data. [12] Regarding uncertainties from part feeding, calibration routines for mapping the manipulation movements to features in camera images are being used. Future research addresses retrieving calibration data from the data acquisition allowing for online optimization of the model accuracy.…”