Integrated process planning using tool/process capabilities and heuristic search

CAD–CAM integration has involved either design with standard manufacturing features (feature-based design), or interpretation of a solid model based on a set of predetermined feature patterns (automatic feature recognition). Thus existing approaches are limited in application to predefined features, and also disregard the dynamic nature of the process and tool availability in the manufacturing shop floor. To overcome this problem, we develop a process oriented approach to design interpretation, and model the shape producing capabilities of the tools into tool classes. We then interpret the part by matching regions of it with the tool classes directly. In addition, there could be more than one way in which a part can be interpreted, and to obtain an optimal plan, it is necessary for an integrated computer aided process planning system to examine these alternatives. We develop a systematic search algorithm to generate the different interpretations, and a heuristic approach to sequence operations (set-ups/tools) for the features of the interpretations generated. The heuristic operation sequencing algorithm considers features and their manufacturing constraints (precedences) simultaneously, to optimally allocate set-ups and tools for the various features. The modules within the design interpretation and process planner are linked through an abstracted qualitative model of feature interactions. Such an abstract representation is convenient for geometric reasoning tasks associated with planning and design interpretation.

[1]  Michael M. Marefat,et al.  A model and inferencing mechanism for spatial reasoning with manufactured components , 1997, Proceedings of International Conference on Robotics and Automation.

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

[3]  Qiang Ji,et al.  Machine interpretation of CAD data for manufacturing applications , 1997, CSUR.

[4]  Subbarao Kambhampati,et al.  Incremental and interactive geometric reasoning for fixture and process planning , 1991 .

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

[6]  Douglas E. R. Clark,et al.  Method for finding holes and pockets that connect multiple faces in 2 1/2D objects , 1991, Comput. Aided Des..

[7]  Qiang Yang,et al.  Handling feature interactions in process-planning , 1992, Appl. Artif. Intell..

[8]  Sunil Arya,et al.  Approximation Algorithm for Multiple-Tool Milling , 2001, Int. J. Comput. Geom. Appl..

[9]  R. H. Crawford,et al.  Intermediate geometric and interfeature relationships for automatic group technology part coding , 1992 .

[10]  Satyandra K. Gupta,et al.  Automated Manufacturability Analysis of Machined Parts , 1995 .

[11]  Paul K. Wright,et al.  Automating process planning: Using feature interactions to guide search , 1989 .

[12]  Subbarao Kambhampati,et al.  Approach toward incremental and interactive planning for concurrent product and process design , 1990 .

[13]  Mark R. Cutkosky,et al.  A methodology and computational framework for concurrent product and process design , 1990 .

[14]  Kenneth D. Forbus.  Qualitative physics: past present and future , 1988 .

[15]  Aristides A. G. Requicha,et al.  Spatial Reasoning for the Automatic Recognition of Machinable Features in Solid Models , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Hong-Chao Zhang,et al.  Computerized Manufacturing Process Planning Systems , 1994 .

[17]  Sanjay B. Joshi,et al.  Recognizing multiple interpretations of interacting machining features , 1994, Comput. Aided Des..

[18]  Hiroshi Sakurai,et al.  Volume decomposition and feature recognition: part 1 - polyhedral objects , 1995, Comput. Aided Des..

[19]  Johan de Kleer.  Multiple Representations of Knowledge in a Mechanics Problem-Solver , 1977, IJCAI.

[20]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[22]  Daniel M. Gaines,et al.  C-C: a customizable feature recognizer , 1999, Comput. Aided Des..

[23]  Piotr Sapiecha,et al.  A Novel Approach to the Minimal Cover Problem , 1993, RSKD.

[24]  Prasad S. Gavankar,et al.  Graph-based extraction of protrusions and depressions from boundary representations , 1990, Comput. Aided Des..

[25]  L. Floriani,et al.  Building a feature-based object description from a boundary model , 1989 .

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

[27]  Sreejit Chakravarty,et al.  Parallel and serial heuristics for the minimum set cover problem , 1992, The Journal of Supercomputing.

[28]  Chell A. Roberts,et al.  Generating resource based flexible form manufacturing features through objective driven clustering , 1999, Comput. Aided Des..

[29]  David S. Johnson,et al.  Approximation algorithms for combinatorial problems , 1973, STOC.

[30]  James F. Allen,et al.  Planning Using a Temporal World Model , 1983, IJCAI.

[31]  Amitabha Mukerjee,et al.  A Qualitative Model for Space , 1990, AAAI.

[32]  Thomas R. Kramer,et al.  A Library of Material Removal Shape Element Volumes [MRSEVs] | NIST , 1992 .

[33]  Daniel M. Gaines,et al.  Achieving Reconfigurable CAD/CAPP Integration by Reasoning from Shape Producing Capabilities , 1996 .

[34]  R. Geelink,et al.  An Integrated Cutting Tool Selection and Operation Sequencing Method , 1992 .

[35]  J. Shah,et al.  Determination Of Machining Volumes From Extensible Sets Of Design Features , 1994 .