The development of a new feature based technique for automated manufacturability evaluation (ME) of machined parts is reported in this article. Key to this approach is a new type of feature called a resource based flexible form manufacturing feature. This type of manufacturing feature incorporates available factory resources and permits unlimited variations in the geometric form as dictated by tool accessibility. A ME system based on this new feature definition is overviewed. Through a process of automatic feature recognition, a manufacturing feature based description of a part is generated which is then used as a form of high level operation plan on which accurate estimates of production cost and time can be made. This paper focuses on the feature recognition algorithm, which is termed Objective Driven Clustering. The recognition algorithm consists of generating feature primitives, which are operational subplans for subregions of a part. Subsequently, primitives are intelligently selected and grouped in a clustering process that uses heuristics, constraints and a user defined evaluation objective to form manufacturing features. The methodology accommodates parts with complex surfaces and interacting form features. It is also sensitive to a variety of part, factory and evaluation related parameters including the evaluation objective, accessibility, part material, D&T, available machines and tools, tool cost, tool change time and setup change time. A prototype system Arizona State University Manufacturability Evaluator (ASUME) used in validating the methodology is discussed.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
- Industrial and Manufacturing Engineering