A survey of knowledge representation methods and applications in machining process planning

Abstract

The machining process is the act of preparing the detailed operating instructions for changing an engineering design into an end product, which involves the removal of material from the part. Today, machining process faces new challenges from the external manufacturing environment, such as globalization and collaboration. Moreover, there has been a virtual explosion in the extent of raw data, and knowledge representation is essential to make sense of the data. Thus, there is an urgent need to ascertain the current status and future trends of knowledge representation in the machining process. This study describes the state of the art of knowledge representation methods and applications in the machining process planning, as well as providing breadth and depth in this area for experts or newcomers. Based on data gathered from the Web of Science, 698 publications related to knowledge representation methods are discussed and divided into nine categories: predicate logic-based, rule-based, semantic network-based, frame-based, script-based, Petri net-based, object-oriented-based, ontology-based, neural network-based. Based on these methods, some specific aspects of the machining process are introduced, including feature recognition, tool selection, setup planning, operation selection and sequencing, and numerical control machining planning generation. Finally, a statistic analysis of these established methods in process planning is discussed, and some trends identified.