Using Automatic Feature Recognition to Interface CAD to CAPP

1992 ◽  
Author(s):  
Eric Wang
Keyword(s):  
Pattern Matching ◽  
Feature Recognition ◽  
Scale Up ◽  
Fundamental Difference ◽  
Computer Integrated Manufacturing ◽  
Semantic Gap ◽  
Recognition Method ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Automatic Feature Recognition

Abstract Interfacing CAD to CAPP (computer-aided process planning) is crucial to the eventual success of a fully-automated computer-integrated manufacturing (CIM) environment. Current CAD and CAPP systems are separated by a “semantic gap” that represents a fundamental difference in the ways in which they represent information. This semantic gap makes the interfacing of CAD to CAPP a non-trivial task. This paper argues that automatic feature recognition is an indispensable technique in interfacing CAD to CAPP. It then surveys the current literature on automatic feature recognition methods and systems, and analyzes their suitability as CAD/CAPP interfaces. It also describes a relatively recent automatic feature recognition method based on volumetric decomposition, using Kim’s alternating sum of volumes with partitioning (ASVP) algorithm. The paper’s main theses are: (1) that most previous automatic feature recognition approaches are ultimately based on pattern-matching; (2) that pattern-matching approaches are unlikely to scale up to the real world; and (3) that volumetric decomposition is an alternative to pattern-matching that avoids its shortcomings. The paper concludes that automatic feature recognition by volumetric decomposition is a promising approach to the interfacing of CAD to CAPP.

An oriented feature extraction and recognition approach for concave-convex mixed interacting features in cast-then-machined parts

2018 ◽  
Vol 233 (4) ◽  
pp. 1269-1288 ◽  
Author(s):  
Haichao Wang ◽  
Jie Zhang ◽  
Xiaolong Zhang ◽  
Changwei Ren ◽  
Xiaoxi Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Computer Aided Design ◽  
Feature Recognition ◽  
Freeform Surface ◽  
Layer By Layer ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Machined Parts ◽  
Aided Design ◽  
Interacting Features

Feature recognition is an important technology of computer-aided design/computer-aided engineering/computer-aided process planning/computer-aided manufacturing integration in cast-then-machined part manufacturing. Graph-based approach is one of the most popular feature recognition methods; however, it cannot still solve concave-convex mixed interacting feature recognition problem, which is a common problem in feature recognition of cast-then-machined parts. In this study, an oriented feature extraction and recognition approach is proposed for concave-convex mixed interacting features. The method first extracts predefined features directionally according to the rules generated from attributed adjacency graphs–based feature library and peels off them from part model layer by layer. Sub-features in an interacting feature are associated via hints and organized as a feature tree. The time cost is reduced to less than [Formula: see text] by eliminating subgraph isomorphism and matching operations. Oriented feature extraction and recognition approach recognizes non-freeform-surface features directionally regardless of the part structure. Hence, its application scope can be extended to multiple kinds of non-freeform-surface parts by customizing. Based on our findings, implementations on prismatic, plate, fork, axlebox, linkage, and cast-then-machined parts prove that the proposed approach is applicable on non-freeform-surface parts and effectively recognize concave-convex mixed interacting feature in various mechanical parts.

scholarly journals Optimization of Operation Sequencing in CAPP Using Superhybrid Genetic Algorithms-Simulated Annealing Technique

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
G. Nallakumarasamy ◽  
PSS. Srinivasan ◽  
K. Venkatesh Raja ◽  
R. Malayalamurthi
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Solution Space ◽  
Computer Integrated Manufacturing ◽  
Computational Time ◽  
Shop Floor ◽  
Production Environment ◽  
Computer Aided Process Planning ◽  
Space Reduction ◽  
Computer Aided

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an overconstrained overall solution space, and the final solution is normally far from optimal or even nonfeasible. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of setups, maximizing machine utilization, and minimizing number of tool changes. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. The main contribution of this work is to develop an intelligent CAPP system for shop-floor use that can be used by an average operator and to produce globally optimized results. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix (PCM) and reward-penalty matrix (REPMAX) using superhybrid genetic algorithms-simulated annealing technique (S-GENSAT), a hybrid metaheuristic. Also, solution space reduction methodology based on PCM and REPMAX upgrades the procedure to superhybridization. In this work, a number of benchmark case studies are considered to demonstrate the feasibility and robustness of the proposed super-hybrid algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Also, the proposed S-GENSAT integrates solution space reduction, hybridization, trapping out of local minima, robustness, and convergence; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm.

Study on Integration of Computer Aided Process Planning System and PDM System

2013 ◽  
Vol 416-417 ◽  
pp. 919-924
Author(s):  
Hong Xia Yang ◽  
Wei Dong Chen ◽  
Hua Sheng Feng
Keyword(s):  
Product Design ◽  
Process Planning ◽  
Rapid Development ◽  
Computer Integrated Manufacturing ◽  
Planning System ◽  
Strong Basis ◽  
Computer Technique ◽  
Computer Aided Process Planning ◽  
Process Planning System ◽  
Computer Aided

With the rapid development of modern science and technology and computer technique, modern enterprise faces new challenges for product design, production, management, market planning and sales. The products of enterprises develop towards diversification, serialization and individualization. Technological design is important in product manufacturing process and is a bond of product design and actual production. Therefore, modern enterprises need to develop computer aided process planning system to improve the quality and efficiency of process design of the enterprise. Starting from the requirements of enterprises on computer aided process planning systems and combining the existing Web technology, the paper proposes the study on integration of computer aided process planning system and PDM system. The development and application of the system not only provides strong support for enterprises realizing rapid design and manufacture and strong basis for enterprises realizing computer integrated manufacturing system, but also makes informationization degree, economic benefit and social benefit of enterprises improve greatly.

Automatic feature recognition using artificial neural networks to integrate design and manufacturing: Review of automatic feature recognition systems

2010 ◽  
Vol 25 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Bojan R. Babić ◽  
Nenad Nešić ◽  
Zoran Miljković
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Feature Recognition ◽  
System Development ◽  
Planning System ◽  
Computer Aided Process Planning ◽  
Design And Manufacturing ◽  
Automatic Feature Recognition ◽  
Feature Technology ◽  
Artificial Neural

AbstractFeature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.

Computer Aided Process Planning for Freeform Surface Sheet Metal Features in Automotive Industry

2013 ◽  
Vol 392 ◽  
pp. 931-935
Author(s):  
M.A. Saleh ◽  
H.M.A. Hussein ◽  
H.M. Mousa
Keyword(s):  
Sheet Metal ◽  
Process Planning ◽  
Cost Estimation ◽  
Data Exchange ◽  
Feature Recognition ◽  
Freeform Surface ◽  
Free Form ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Surface Sheet

This paper describes computer aided process planning for a freeform surface; sheet metal features. Automotive body panels are always manufactured using thin forming sheets; the developed CAPP system consists of two modules which are feature recognition module based on STEP AP203 and a process plan module; two additional modules for automotive panel CAPP system and cost estimation module are also incorporated in the system of punch and bending operation. Stamped or punched features in generative shape design are used to design automotive panels; the generative CAPP system is written in visual basic 2008 language and implemented in several case studies demonstrated in the present work. Feature recognition of punched; stamped internal features in free form surface recognized in base of data exchange files using STEP AP203 ISO-10303-21.

The data explosion: tackling the taboo of automatic feature recognition in airborne survey data

Antiquity ◽  
2014 ◽  
Vol 88 (341) ◽  
pp. 896-905 ◽  
Author(s):  
Rebecca Bennett ◽  
Dave Cowley ◽  
Véronique De Laet
Keyword(s):  
Feature Detection ◽  
Feature Recognition ◽  
Remote Sensing Data ◽  
New Wave ◽  
Computer Aided ◽  
Automatic Feature Recognition ◽  
Photographic Survey ◽  
Types Of Information ◽  
Airborne Survey ◽  
New Generation

The increasing availability of multi-dimensional remote-sensing data covering large geographical areas is generating a new wave of landscape-scale research that promises to be as revolutionary as the application of aerial photographic survey during the twentieth century. Data are becoming available to historic environment professionals at higher resolution, greater frequency of acquisition and lower cost than ever before. To take advantage of this explosion of data, however, a paradigm change is needed in the methods used routinely to evaluate aerial imagery and interpret archaeological evidence. Central to this is a fuller engagement with computer-aided methods of feature detection as a viable way to analyse airborne and satellite data. Embracing the new generation of vast datasets requires reassessment of established workflows and greater understanding of the different types of information that may be generated using computer-aided methods.

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