Towards an efficient process planning of the V-bending process: an enhanced automated feature recognition system

Abstract This paper describes an open-architecture system for computer-aided process planning called OSCAP. The system is different in architecture from traditional integrated process planning systems, since it is designed specifically to integrate with existing partial planning software with little effort. It does provide all functions of design and process planning for machining of mechanical parts on 3-axis machining centers. Special features of the system include a sophisticated feature recognition system, an optimal machining planner, automated fixture synthesis, setup planning with operation sequencing, and a knowledge based system organizer called the OSCAP core which orchestrates the functioning of all modules. The system can be arbitrarily extended or collapsed by adding or removing functional modules.

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Development and performance evaluation of a web-based feature extraction and recognition system for sheet metal bending process planning operations

International Journal of Computer Integrated Manufacturing ◽

10.1080/0951192x.2021.1891570 ◽

2021 ◽

pp. 1-23

Author(s):

Eriyeti Murena ◽

Khumbulani Mpofu ◽

Alfred T Ncube ◽

Olasumbo Makinde ◽

John A Trimble ◽

...

Keyword(s):

Feature Extraction ◽

Performance Evaluation ◽

Sheet Metal ◽

Process Planning ◽

Recognition System ◽

Web Based ◽

Bending Process ◽

Sheet Metal Bending ◽

And Performance

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Integration of Commercial CAD With Commercial NC Programming Packages

Volume 1: 20th Computers and Information in Engineering Conference ◽

10.1115/detc2000/cie-14636 ◽

2000 ◽

Author(s):

Huikang K. Miao ◽

Nandakumar Sridharan ◽

Jami J. Shah

Keyword(s):

Process Planning ◽

Feature Recognition ◽

Recognition System ◽

Tool Selection ◽

Operation Sequencing ◽

Planning Decisions ◽

Nc Programming ◽

Machining Feature ◽

Cad Cam ◽

Geometry Engine

Abstract This paper focuses on the issues in automating the various tasks in process planning and on the issues in integrating the process-planning task with commercial CAD/CAM software. Automated process planning involves two important tasks; machining feature extraction and feature-based process planning. The integration of CAD and NC may be done by two alternative approaches: external or internal. This study uses the external approach. The CAD model of the part and the stock is exported to a format compatible with the external geometry engine. The machining feature recognition system communicates with the external geometry engine through APIs to obtain geometric and topological information required for feature recognition. The machining knowledge embedded in the recognized features is used by the process-planner to chalk out a process plan for the part. The machining features are classified into three broad categories each with machining significance specific to NC machining, so that when extracted they are useful in making process-planning decisions. Setup Planning, Operation Sequencing and Tool Selection is performed automatically based on criteria such as feature shapes, feature locations, tool access directions and feasibility of workpiece locating and clamping. The detailed process planning is based on a commercial CAD/CAM/CAE package, I-DEAS.

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Feature Recognition Using Combined Convex and Maximal Volume Decompositions

Volume 3: 25th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2005-85522 ◽

2005 ◽

Cited By ~ 1

Author(s):

Eric Wang ◽

Yong Se Kim ◽

Yoonhwan Woo

Keyword(s):

Process Planning ◽

Feature Recognition ◽

Recognition System ◽

Feature Representation ◽

Planning System ◽

Feature Interaction ◽

Generation Process ◽

Maximal Volume ◽

Volume Decomposition ◽

Product Manufacturing

Next generation process planning systems should be capable of dealing with industrial demands of versatility, flexibility, and agility for product manufacturing. Development of process planning system is heavily dependent on feature recognition, but presently there is no satisfactory feature recognition system relying on a single method. In this paper, we describe a hybrid feature recognition method for machining features that combines three feature recognition technologies: graph-based, convex volume decomposition, and maximal volume decomposition. Based on an evaluation of the strengths and weaknesses of these methods, we integrate them in a sequential workflow, such that each method recognizes features according to its strengths, and successively simplifies the part model for the following methods. We identify two anomalous cases arising from the application of maximal volume decomposition, and discuss their cure by introducing limiting halfspaces. All recognized features are combined into a unified hierarchical feature representation, which captures feature interaction information, including geometry-based machining precedence relations.

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A Cloud Based Feature Recognition System to Support Collaborative and Adaptive Process Planning

Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing ◽

10.1115/msec2014-4088 ◽

2014 ◽

Author(s):

Arivazhagan Anbalagan ◽

Sheng Wang ◽

Li Weidong

Keyword(s):

Process Planning ◽

Feature Recognition ◽

Sustainable Manufacturing ◽

Complete Information ◽

Recognition System ◽

Cloud Manufacturing ◽

Downstream Process ◽

Adaptive Process ◽

Manufacturing Environments ◽

Server Architecture

This paper presents a Cloud Based Feature Recognition Module (CB-FRM) developed to support Collaborative and Adaptive Process Planning for Sustainable Manufacturing Environments. In the work, the CB-FRM is developed for a Cloud-based environment and based on an innovative ‘pattern-strings’ feature recognition concept. The Cloud system is designed in a light client and heavy server architecture, where the ‘pattern-strings’-based feature recognition approach is developed and deployed. Through recognized ‘pattern-strings’, the feature recognition process is able to extract the complete information of features with its location in the plane. The detailed information of the features is then shared in a developed Cloud environment for the downstream process planning activities. The implementation aspect is explained with the help of a sample industrial part by emphasizing the importance of Cloud manufacturing environment with process planning activities.

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Reconfigurable Feature Recognition for an Adaptable, Maintainable CAD/CAPP Integration

Volume 1: Design for Manufacturing Conference ◽

10.1115/96-detc/dfm-1287 ◽

1996 ◽

Author(s):

Daniel M. Gaines ◽

Fernando Castaño ◽

Caroline C. Hayes

Keyword(s):

Feature Recognition ◽

Recognition System ◽

Manufacturing Processes ◽

Manufacturing Equipment ◽

Recognition Systems

Abstract This paper presents MEDIATOR, a feature recognition system which is designed to be maintainable and extensible to families of related manufacturing processes. A problem in many feature recognition systems is that they are difficult to maintain. One of the reasons may be because they depend on use of a library of feature-types which are difficult to update when the manufacturing processes change due to changes in the manufacturing equipment. The approach taken by MEDIATOR is based on the idea that the properties of the manufacturing equipment are what enable manufacturable shapes to be produced in a part. MEDIATOR’S method for identifying features uses a description of the manufacturing equipment to simultaneously identify manufacturable volumes (i.e. features) and methods for manufacturing those volumes. Maintenance of the system is simplified because only the description of the equipment needs to be updated in order to update the features identified by the system.

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Modeling and Representation of Manufacturing Knowledge for DFM Systems

Volume 4: 24th Computers and Information in Engineering Conference ◽

10.1115/detc2004-57724 ◽

2004 ◽

Cited By ~ 2

Author(s):

Zuozhi Zhao ◽

Jami Shah

Keyword(s):

Process Planning ◽

Cost Estimation ◽

Manufacturing Process ◽

Feature Recognition ◽

Design For Manufacturing ◽

Time Cost ◽

Computational Framework ◽

Different Types ◽

Manufacturing Knowledge ◽

Manufacturing Time

The manufacturing knowledge today spans a vast spectrum, from manufacturing process capability/constraint, precedence, algorithms/heuristics of performing feature recognition, process planning and manufacturing time/cost estimation, to Design for Manufacturing (DfM) tactics and strategies. In this paper, different types of manufacturing knowledge have been identified and the ways to represent and apply them are described. An information model is developed as the backbone to integrate other existing tools into the framework. A computational framework is presented to help the manufacturing knowledge engineers formulize their knowledge and store it into the computer, and help the designers systematically analyze the manufacturability of the design.

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