Machining planning: a model of an expert level planning process

Understanding and automating planning is a problem that has been of interest and of economic importance in manufacturing over the past 25 years. In recent years, there has been an increasing need for the automation of manufacturing planning for machining processes due to the reduction in the number of new trainees entering the profession. Studies of large scale planning domains like this one, and the techniques that make them tractable are very important since Chapman's results (Chapman 85) show that classical planning models for small problems are unlikely to scale up successfully to domains of any complexity due to the combinatorial nature of the computation required. There are several motivations behind the work in this thesis: (1) To provide a model of the planning process for machining. (2) Provide tools to process planners which allow plans for small batch manufacturing to be produced in a more timely and cost effective manner. This will make the production of both prototype parts and custom manufacturing faster and more affordable. (3) Provide tools to designers which will allow designers to make more cost effective designs by providing them with manufacturing cost and feasibility information early in the design process. (4) To gain a better understanding of the structure of planning processes as they exist in real domains, and so that they can be compared to classical planners. This thesis presents a model of a process for producing manufacturing plans for prismatic milled parts. It is implemented in the Machinist program, a rule-based system written in OPS5, which produces correct manufacturing plans judged in a blind test by experts to be equivalent in quality plans produced by human machinists with 7 to 8 years of experience. Additionally, an application of the Machinist program for use in part design has been implemented. In this application, a design is analyzed by first producing a manufacturing plan for it and then using the structure of the manufacturing plan to find cost reducing design modifications.