A Graph Grammar Based Approach to Automated Manufacturing Planning

In this paper, a new graph grammar representation is proposed to reason about the manufacturability of solid models. The knowledge captured in the graph grammar rules serves as a virtual machinist in its ability to recognize arbitrary geometries and match them to various machine operations. Firstly, a given part is decomposed into multiple sub-volumes, where each sub-volume is assumed to be machined in one operation or to be non-machinable. The decomposed part is converted into a graph so that graph grammar rules can determine the machining details. For each operation, rules determine the face on the part that the tool enters, the type of tools used, the type of machine used, and how the part is fixed within the machine. A candidate plan is a feasible sequence of all of the necessary machining operations needed to manufacture this part. If a given geometry is not machinable, the rules will fail to find operations for all of the partitions.As a result of this representation, designers can quickly get insights into how a part can be made and how it can be improved (e.g. change features to reduce time and cost). A variety of tests of this algorithm on both simple and complex engineering parts show its effectiveness and efficiency.Copyright © 2012 by ASME

[1]  Jami J. Shah Assessment of features technology , 1991, Comput. Aided Des..

[2]  Matthew I. Campbell,et al.  Convex Decomposition of 3D Solid Models for Automated Manufacturing Process Planning Applications , 2012 .

[3]  N. P. Suh,et al.  An Integrated Approach to Process Planning and Scheduling , 1985 .

[4]  Kristina Shea,et al.  An Application of Shape Grammars to Planning for CNC Machining , 2009, DAC 2009.

[5]  Behrokh Khoshnevis,et al.  Use of artificial intelligence in automated process planning , 1986 .

[6]  Rangasami L. Kashyap,et al.  Geometric Reasoning for Recognition of Three-Dimensional Object Features , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  JungHyun Han,et al.  Manufacturing feature recognition from solid models: a status report , 2000, IEEE Trans. Robotics Autom..

[8]  D. Levandier,et al.  Approved for Public Release; Distribution Unlimited , 1994 .

[9]  Edgar L. Russell Automated manufacturing planning , 1967 .

[10]  Rangasami L. Kashyap,et al.  Geometric Reasoning for Extraction of Manufacturing Features in Iso-Oriented Polyhedrons , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Martti Mäntylä,et al.  Feature modelling by incremental feature recognition , 1993, Comput. Aided Des..

[12]  T. C. Chang,et al.  Graph-based heuristics for recognition of machined features from a 3D solid model , 1988 .

[13]  Hartmut Ehrig,et al.  Handbook of graph grammars and computing by graph transformation: vol. 3: concurrency, parallelism, and distribution , 1999 .

[14]  Richard A. Wysk,et al.  A knowledge-based approach for automated process planning , 1988 .

[15]  Jami J. Shah,et al.  A Discourse on Geometric Feature Recognition From CAD Models , 2001, J. Comput. Inf. Sci. Eng..

[16]  R. D. Allen,et al.  The application of STEP-NC using agent-based process planning , 2005 .

[17]  R. Sharma,et al.  IMPLEMENTATION OF STEP 224 IN AN AUTOMATED MANUFACTURING PLANNING SYSTEM , 2002 .

[18]  Grzegorz Rozenberg,et al.  Handbook of Graph Grammars and Computing by Graph Transformations, Volume 1: Foundations , 1997 .

[19]  Angappa Gunasekaran,et al.  Computer-aided process planning: A state of art , 1998 .

[20]  Matthew I. Campbell,et al.  Automated Estimation of Time and Cost for Determining Optimal Machining Plans , 2012 .