Three Approaches to the Automation of Laser System Alignment and Their Resource Implications: A Case Study
This addresses the problem of reducing reliance on skilled operators for optical system alignment, but it is incremental as it compares existing methods in a specific case study.
The paper tackled automating laser system alignment by comparing three approaches—artificial neural networks, practice-led mimicking, and design-led modeling—through a case study of a two-mirror system, finding that they vary significantly in human resources and measurement sampling budgets.
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.