Machining process sequencing and machine assignment in generative feature-based CAPP for mill-turn parts

Abstract Process selection and sequencing, as one of the most complex issues when evaluated from a mathematical point of view and crucial in CAPP, still attract research attention. For the current trend of intelligent manufacturing, machining features (MFs) are the information carriers for workpiece geometry and topology representation. They are basically derived from CAD models and are used by downstream engineering applications. A feature-based reasoning approach for generating machining sequences in terms of part setups and the assignment of machine alternatives is presented. The approach suggested in this research assumes a heavy reliance on a data input model incorporating functional requirements for parts and in particular GD&T references. An extended feature taxonomy corresponding to the needs of the rational process plan selection for the addressed category of part types is proposed. It is meant to be applicable to machining of both rotational and prismatic features using machines of various configurations. The developed taxonomy is based on the working directions for MFs and includes the identification of their location with respect to datum references. The developed taxonomy that involves feature tolerance relationships is at the core of the information data model utilised by the original algorithm which was aimed at generic process sequencing for the definite category of mechanical parts. Through the developed algorithm, adequate process alternatives can be generated by adaptive setup merging on a single machine or across multiple available machines under consideration of their respective process capabilities. The approach has been validated through an illustrative case study using a sample mill-turn part of considerable complexity.