Manufacturing feature instances: which ones to recognize?

Manufacturing features and feature-based representations have become an integral part of research on manufacturing systems, largely due to their ability to model correspondences between design information and manufacturing operations. However, several research challenges still must be addressed in order to place feature technologies into a solid scientific and mathematical framework. One challenge is the issue of alternatives in feature-based planning. Even after one has decided upon an abstract set of features to use for representing manufacturing operations, the set of feature instances used to represent a part is by no means unique. For a complex part, many (sometimes infinitely many) different manufacturing operations can potentially be used to manufacture various portions of the part. Some of these feature instances will appear in useful manufacturing plans, and others will not. In order to reduce the number of alternative manufacturing plans that must be examined, we require a systematic means of specifying which feature instances are of interest. This paper addresses the issue of alternatives by introducing the notion of primary feature instances, which we contend are sufficient to generate all manufacturing plans of interest. To substantiate our argument, we describe how various instances in the primary feature set can be used to produce the desired plans. Furthermore, we discuss how this formulation overcomes computational difficulties faced by previous work, and present some complexity results for this approach in the domain of machined parts. “Also with: Nationaf Institute of Standards and Technology, Manufacturing Systems Integration Division Building 220, Room A-127, Gaithersburg, MD 20899. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advanta$e, the ACM copyright notice and the title of the publication and Its date appear, and notice is given that copying is by permission of the Association of Computing Machinery.To copy otherwise, or to republish, requires a fee and/or specific permission. Solid Modeling ’95, Salt Lake City, Utah USA

[1]  S. H. Chuang,et al.  Three-dimensional shape pattern recognition using vertex classification and vertex-edge graphs , 1990, Comput. Aided Des..

[2]  Fritz B. Prinz,et al.  Recognition of geometric forms using the differential depth filter , 1992, Comput. Aided Des..

[3]  Susan Finger,et al.  Parsing Features in Solid Geometric Models , 1990, ECAI.

[4]  Mark Richard Henderson.  EXTRACTION OF FEATURE INFORMATION FROM THREE-DIMENSIONAL CAD DATA , 1984 .

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

[6]  Satyandra K. Gupta,et al.  Building MRSEV models for CAM applications , 1994 .

[7]  M. J. Pratt.  Applications of feature recognition in the product life-cycle , 1993 .

[8]  Mark R. Henderson,et al.  Automatic form-feature recognition using neural-network-based techniques on boundary representations of solid models , 1992, Comput. Aided Des..

[9]  Dana S. Nau,et al.  Building a general approach to feature recognition of Material Removal Shape Element Volumes (MRSEVs) , 1993, Solid Modeling and Applications.

[10]  Hiroshi Sakurai,et al.  Definition and Recognition of Volume Features for Process Planning , 1994 .

[11]  Leila De Floriani.  Feature Extraction from Boundary Models of Three-Dimensional Objects , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Satyandra K. Gupta,et al.  Extracting alternative machining features: An algorithmic approach , 1995 .

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

[14]  Stelios Kafandaris,et al.  Expert Process Planning for Manufacturing , 1990 .

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

[16]  William C. Regli,et al.  Recognition of Volumetric Features from CAD Models: Problem Formalization and Algorithms , 1993 .

[17]  SakuraiHiroshi,et al.  Recognizing Shape Features in Solid Models , 1990 .

[18]  Dana S. Nau,et al.  Systematic approach to analysing the manufacturability of machined parts , 1995, Comput. Aided Des..

[19]  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..

[20]  N. N. Z. Gindy,et al.  A hierarchical structure for form features , 1989 .

[21]  David C. Gossard,et al.  Recognizing shape features in solid models , 1990, IEEE Computer Graphics and Applications.

[22]  Satyandra K. Gupta,et al.  A systematic approach for analyzing the manufacturability of machined parts , 1993 .

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

[24]  Jami J. Shah,et al.  Advances in Feature-Based Manufacturing , 1994 .

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

[26]  Rangasami L. Kashyap,et al.  Automatic construction of process plans from solid model representations , 1992, IEEE Trans. Syst. Man Cybern..