Three Approaches to the Automation of Laser System Alignment and Their Resource Implications: A Case Study

TL;DR

Through a case study of a simple two mirror system, three different automation approaches are identified and examined: artificial neural networks; practice-led, which mimics manual alignment practices; and design-led, modelling from first principles.

Abstract

The alignment of optical systems is a critical step in their manufacture. Alignment normally requires considerable knowledge and expertise of skilled operators. The automation of such processes has several potential advantages, but requires additional resource and upfront costs. Through a case study of a simple two mirror system we identify and examine three different automation approaches. They are: artificial neural networks; practice-led, which mimics manual alignment practices; and design-led, modelling from first principles. We find that these approaches make use of three different types of knowledge 1) basic system knowledge (of controls, measurements and goals); 2) behavioural skills and expertise, and 3) fundamental system design knowledge. We demonstrate that the different automation approaches vary significantly in human resources, and measurement sampling budgets. This will have implications for practitioners and management considering the automation of such tasks.

Figures (10)

• Figure 1: (a) Schematic diagram of the system showing components including two computer vision cameras. (b) An aperture with incident laser beam (1) and the corresponding processed computer vision image allowing precise location of the beam in relation to the aperture centre (2).
• Figure 2: Implementation of the example system. The dashed line traces the laser beam path from the laser source to the collector/power meter. A) motorised mirror mounts, B) apertures and C) computer vision cameras.
• Figure 3: Diagram modelling the example system in three dimensions. The laser beam emanates from a laser and encounters the components: first mirror $m_1$, then mirror $m_2$, next aperture $A_1$ and finally aperture $A_2$. The layout and notaion is inspired by Yuan:11.
• Figure 4: Diagram of the ANN trained in the reverse model with the aperture measurements as inputs and the mirror controls as outputs. (See Fig. \ref{['fig:3DSystemDiag']}).
• Figure 5: Chart of ANN training epochs v.s. mean $R$$^2$ model goodness of fit showing that training for 10000 epochs produced the highest goodness of fit to the training data (see text).