An automatic assembly CAD system of plastic profile calibrating die based on feature recognition

2015 ◽  
Vol 85 (9-12) ◽  
pp. 2577-2587 ◽  
Author(s):  
Qiao Guo ◽  
Hongtao Tang ◽  
Shunsheng Guo ◽  
Yibing Li ◽  
Jinting Zhang
Keyword(s):  
Feature Recognition ◽  
Automatic Assembly ◽  
Cad System ◽  
Plastic Profile

Geometric Reasoning for the Coding of CAD Models

1995 ◽  
Author(s):  
A. Z. Qamhiyah ◽  
B. Benhabib ◽  
R. D. Venter
Keyword(s):  
Computer Aided Design ◽  
Feature Recognition ◽  
Effective Means ◽  
Boundary Representation ◽  
Cad Model ◽  
Cad System ◽  
Cad Models ◽  
Form Features ◽  
Feature Based Design ◽  
Design Retrieval

Abstract Many of today’s concurrent product-development cycles depend on the utilization of intelligent Computer-Aided Design (CAD) systems. Thus, it would be essential to provide CAD users with effective means for interacting with the CAD system and its database. This paper addresses the development of a boundary-based coding procedure for CAD models. Coding the geometric and processing characteristics of objects, based on their CAD model representation, has been long recognized as an effective approach that allows convenient design retrieval on the one hand and process-planning automation on the other. Our work is based on the assumption that form features are recognizable and extractable from the CAD model by current feature-recognition, feature extraction and feature-based-design approaches. The coding procedure is applicable to the boundary representation of the object and its extracted form features.

A Hybrid Approach to Feature Recognition Using Approximate and Partial Exact CAD Models

2002 ◽  
Author(s):  
Nagesh Belludi ◽  
Derek Yip-Hoi
Keyword(s):  
Feature Recognition ◽  
Hybrid Approach ◽  
Parameter Extraction ◽  
Feature Identification ◽  
Cad System ◽  
Processing Times ◽  
File Formats ◽  
Cad Models ◽  
Manufacturing Applications ◽  
Simple Surface

Several CAD system independent feature recognition techniques have been developed to drive manufacturing applications. Commercial implementations of these techniques require translating CAD models using STEP or other neutral file formats. With large CAD models found in some application domains; e.g., powertrain machining, corresponding STEP files are also large. This leads to large processing times. Another approach is to use lightweight formats such as STL or VRML. Here, complete & accurate parameter extraction is difficult because these formats approximate surfaces as tessellations. This paper discusses a new methodology for feature recognition, in which a VRML file is used for feature identification. To some extent, parameters of faces with simple surface-types are recovered from the tessellated model. If identified features consist of faces whose parameters are not recovered from the tessellated model, a partial STEP file translation is used for extracting exact parameters. This CAD system independent algorithmic development and implementation reduces the amount of data exported to neutral files, thus leading to more efficient feature recognition.

scholarly journals Automating Redesign of Sheet-Metal Parts in Automotive Industry Using KBE and CBR

2006 ◽  
Author(s):  
Marcus Sandberg ◽  
Tobias Larsson
Keyword(s):  
Product Development ◽  
Sheet Metal ◽  
Automotive Industry ◽  
Feature Recognition ◽  
Case Based Reasoning ◽  
Research Issues ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Cad System ◽  
Industrial Implementation

Automating redesign is an approach for engineering designers to prevent design related manufacturability problems in early product development and thus reduce costly design iterations. A vast amount of work exists, with most research findings seemingly staying within the research community rather than finding its way into use in industrial settings where research issues have often evolved from the concerned applied research. The aim of this paper is to present an approach with industrial implementation potential regarding automating redesign of sheet-metal components in early product development to avoid manufacturing problems due to design flaws and non-optimal designs. Geometry, generated by a knowledge-based engineering (KBE) system, gives input to the case-based reasoning (CBR) governed manufacturing planning. If geometry is found non-manufacturable or enhancement of already manufacturable geometry is possible, the CBR system will suggest redesign actions to resolve the problem. CBR extends the capabilities of the rule-based KBE-system by enabling plan-based evaluation. The approach has the potential for industrial implementation, since KBE is often closely coupled to an industrial CAD-system, hence enabling technology is at the industry. Also, combining KBE and CBR reduces the coding effort compared to coding the whole design support with CBR, as feature recognition is simplified by means of KBE. A case study of development of sheet-metal manufactured parts at a Swedish automotive industry partner presents the method in use. As it is shown that redesign can be automated for sheet-metal parts there is a potential for reducing costly design and manufacturing iterations.

Feature Recognition

2009 ◽  
pp. 90-108
Author(s):  
Xun Xu
Keyword(s):  
Feature Recognition ◽  
Geometric Model ◽  
Feature Model ◽  
Design System ◽  
Cad System ◽  
Domain Specific ◽  
Cad Models ◽  
Feature Based ◽  
High Level ◽  
Feature Based Design

Conventional CAD models only provide pure geometry and topology for mechanical designs such as vertices, edges, faces, simple primitives, and the relationship among them. Feature recognition is then required to interpret this low-level part information into high-level and domain-specific features such as machining features. Over the years, CAD has been undergoing fundamental changes toward the direction of feature-based design or design by features. Commercial implementations of FBD technique became available in the late 1980’s. One of the main benefits of adopting feature- based approach is the fact that features can convey and encapsulate designers’ intents in a natural way. In other words, the initial design can be synthesized quickly from the high-level entities and their relations, which a conventional CAD modeller is incapable of doing. However, such a feature-based design system, though capable of generating feature models as its end result, lacks the necessary link to a CAPP system, simply because the design features do not always carry the manufacturing information which is essential for process planning activities. This type of domain-dependent nature has been elaborated on in the previous chapter. In essence, feature recognition has become the first task of a CAPP system. It serves as an automatic and intelligent interpreter to link CAD with CAM, regardless of the CAD output being a pure geometric model or a feature model from a FBD system. To be specific, the goal of feature recognition systems is to bridge the gap between a CAD database and a CAPP system by automatically recognizing features of a part from the data stored in the CAD system, and based on the recognized features, to drive the CAPP system which produces process plans for manufacturing the part. Human interpretation of translating CAD data into technological information required by a CAPP system is thus minimized if not eliminated.

Design and implementation of an integral design CAD system for plastic profile extrusion die

2016 ◽  
Vol 89 (1-4) ◽  
pp. 543-559 ◽  
Author(s):  
Li Li ◽  
Hongtao Tang ◽  
Shunsheng Guo ◽  
Lang Huang ◽  
Yaohua Xu
Keyword(s):  
Cad System ◽  
Design And Implementation ◽  
Extrusion Die ◽  
Plastic Profile ◽  
Profile Extrusion Die

scholarly journals Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Ahsan Bin Tufail ◽  
Yong-Kui Ma ◽  
Mohammed K. A. Kaabar ◽  
Francisco Martínez ◽  
A. R. Junejo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
Computer Aided Design ◽  
Feature Recognition ◽  
Cancer Prognosis ◽  
Generic Model ◽  
Prognosis Prediction ◽  
Cad System ◽  
Diagnosis And Prognosis ◽  
Cancer Diagnosis And Prognosis

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

Automatic Assembly Feature Recognition and Disassembly Sequence Generation

2001 ◽  
Vol 1 (4) ◽  
pp. 291-299 ◽  
Author(s):  
Raymond C. W. Sung ◽  
Jonathan R. Corney ◽  
Doug E. R. Clark
Keyword(s):  
Feature Recognition ◽  
Automatic Recognition ◽  
Automatic Assembly ◽  
Sequence Generation ◽  
Disassembly Sequence ◽  
Wide Range ◽  
Assembly Features ◽  
Assembly Feature ◽  
State Tree ◽  
Octree Representation

This paper describes a system for the automatic recognition of assembly features and the generation of disassembly sequences. The paper starts by reviewing the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features which arise from “spatial” and “contact” face adjacency relationships (known as s-adjacency and c-adjacency respectively). The algorithm uses an octree representation of a B-rep model to support the geometric reasoning required to locate assembly features on disjoint bodies. A pointerless octree representation is generated by recursively sub-dividing the assembly model’s bounding box into octants which are used to locate: 1. Those portions of faces which are c-adjacent (i.e. they effectively touch within the tolerance of the octree). 2. Those portions of faces which are s-adjacent to a nominated face. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and at different resolutions. The assembly features located are recorded as attributes in the B-rep model and are then used to generate a disassembly sequence plan for the assembly. This sequence plan is represented by a transition state tree which incorporates knowledge of the availability of feasible gripping features. By way of illustration, the algorithm is applied to several trial components

Research on Aero Engine Blade Information Model Based on Manufacturability Evaluation

2011 ◽  
Vol 346 ◽  
pp. 294-300 ◽  
Author(s):  
Qing Ming Fan ◽  
Yan Cao ◽  
Hong Jun Liu
Keyword(s):  
Information Integration ◽  
Feature Recognition ◽  
Information Model ◽  
Secondary Development ◽  
Cad System ◽  
Model Based ◽  
Aero Engine ◽  
Engine Blade ◽  
Automatic Feature Recognition ◽  
Manufacturability Evaluation

According to the engineering demands of the manufacturability evaluation, the Aero engine blade part information model built up based on the generalized feature and STEP standard, and based on the secondary development of UG software, a CAD system based on features is realized. The system can realize automatic feature recognition. Based on the part information model that has been built up, the system can generate STEP document automatically, can provide various blade part information for the subsequent manufacturability evaluation of the design. This helps to realize the integration and parallel of CAD and the subsequent process. The CAD/CPAPP/DFM information integration can be realized by using the blade part information that has been built up.

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