Automated Manufacturing Planning Approach Based on Volume Decomposition and Graph-Grammars

A new graph grammar based reasoning is proposed to reason about the manufacturability of 3D 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 machining operations. For a given part, its 3D CAD geometry is first decomposed into multiple subvolumes, where each is assumed to be machined in one operation. The decomposed part is then converted into a graph so that the graph-grammar rules can perform further reasoning and determine the machining details. A candidate plan is a feasible sequence of all of the necessary machining operations needed to manufacture this part. For each operation, the 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. If a given geometry is not machinable, the rules will fail to find a complete manufacturing plan for all of the subvolumes. As a result of this reasoning, 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) based upon the feedback of the rules. A variety of tests on this algorithm on both simple and complex engineering parts show its effectiveness and efficiency.

[1]  R. Dechter,et al.  AND/OR Tree Search for Constraint Optimization , 2004 .

[2]  Matthew I. Campbell,et al.  Automatic Reasoning for Defining Lathe Operations for Mill-Turn Parts , 2013, DAC 2013.

[3]  P. V. Mohanram,et al.  A Generative Computer-Aided Process Planning System for Prismatic Components , 2002 .

[4]  Alan E. Middleditch,et al.  Convex Decomposition of Simple Polygons , 1984, TOGS.

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

[6]  Michael J. Wozny,et al.  An overview of automatic feature recognition techniques for computer-aided process planning , 1995 .

[7]  A. P. Rockwood Geometric primitives , 1996, CSUR.

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

[9]  Antony R Mileham,et al.  The development of a generative CAPP system for prismatic components , 1989 .

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

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

[12]  Vadim Shapiro,et al.  Well-Formed Set Representations of Solids , 1999, Int. J. Comput. Geom. Appl..

[13]  C. S. P. Rao,et al.  Automatic Extraction of Three Dimensional Prismatic Machining Features from CAD Model , 2011 .

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

[15]  Rohit Sharma,et al.  Implementation of STEP Application Protocol 224 in an automated manufacturing planning system , 2002 .

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

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

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

[19]  Yong Se Kim,et al.  Recognition of form features using convex decomposition , 1992, Comput. Aided Des..

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

[21]  Mark Henderson,et al.  Computer recognition and extraction of form features: A CAD/CAM link , 1984 .

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

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

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

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

[26]  Zoran Miljkovic,et al.  A review of automated feature recognition with rule-based pattern recognition , 2008, Comput. Ind..

[27]  S. S. Pande,et al.  Automatic recognition of features from freeform surface CAD models , 2008, Comput. Aided Des..

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

[29]  Yong Se Kim,et al.  A Convex Decomposition Using Convex Hulls and Local Cause of Its Non-Convergence , 1992 .

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

[31]  S. Sarhan,et al.  COMPUTER AIDED PROCESS PLANNING FOR PRISMATIC PARTS , 2014 .

[32]  Leonidas J. Guibas,et al.  An efficient algorithm for finding the CSG representation of a simple polygon , 1988, Algorithmica.

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

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

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