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

TL;DR

This paper compares three automation methods for laser system alignment, analyzing their resource requirements and knowledge types, to guide practitioners in choosing suitable automation strategies.

Contribution

It introduces and evaluates three distinct automation approaches—neural networks, practice-led, and design-led—for laser system alignment, highlighting their resource and knowledge implications.

Findings

Neural networks require extensive data and computational resources.

Practice-led approach mimics manual skills with moderate resource use.

Design-led approach relies on fundamental principles, reducing human resource needs.

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.