A study on automatic fixture design using reinforcement learning

Abstract

Fixtures are used to locate and secure workpieces for further machining or measurement process. Design of these fixtures remains a costly process due to the significant technical know-how required. Automated fixture design can mitigate much of these costs by reducing the dependence on skilled labour, making it an attractive endeavour. Historical attempts in achieving automated fixture design solutions predominantly relied on case-based reasoning (CBR) to generate fixtures by extrapolating from previously proven designs. These approaches are limited by their dependence on a fixturing library. Attempts in using rule-based reasoning (RBR) has also shown to be difficult to implement comprehensively. Reinforcement learning, on the other hand, does not require a fixturing library and instead builds experience and learns through interacting with an environment. This paper discusses the use of reinforcement learning to generate optimized fixturing solutions. Through a proposed reinforcement learning driven fixture design (RL-FD) framework, reinforcement learning was used to generate optimized fixturing solutions. In response to the fixturing environment, adjustments to the reinforcement learning process in the exploration phase is studied. A case study is presented, comparing a conventional exploration method with an adjusted one. Both agents show improved average results over time, with the adjusted exploration model exhibiting faster performance.