レーザーシステムアライメント自動化の3つのアプローチとリソースへの影響：ケーススタディ
Three Approaches to the Automation of Laser System Alignment and Their Resource Implications: A Case Study
2枚のミラーからなる光学系のアライメント自動化を題材に、ニューラルネットワーク・手順模倣・設計モデルベースの3手法を比較し、必要な人的資源と計測サンプル数が大きく異なることを示した。
著者: David A. Robb, Donald Risbridger, Ben Mills, Ildar Rakhmatulin, Xianwen Kong, Mustafa Erden, M. J. Daniel Esser, Richard M. Carter, Mike J. Chantler
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
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.
