Classification and Automatic Feature-Based Extraction Approach for Cylindrical and Milling Parts

2021 ◽  
Vol 11 (3) ◽  
pp. 55-73
Author(s):  
Sathish Kumar Adapa ◽  
Dowluru Sreeramulu ◽  
Jagadish
Keyword(s):  
Case Studies ◽  
Process Planning ◽  
Feature Recognition ◽  
Recognition Algorithm ◽  
Machining Process ◽  
Automatic Extraction ◽  
Java Program ◽  
Feature Based ◽  
Logical Rules ◽  
Conventional Machining

This paper reports classification and automatic extraction of various cylindrical and milling features in conventional machining process parts. In this work, various algorithms like hole recognition algorithm (HRA) and milling feature recognition algorithm (MFRA) have been used for identification of different cylindrical and milling features. A cylindrical feature is identified based on specific logical rules, and milling feature is identified based on the concept of concave decomposition of edges. In-house developed JAVA program is used to write algorithm, and then validation of the algorithm is done through two case studies. The HRA and MFRA algorithms extract the cylindrical features (through holes, blind holes, taper holes, and bosses) and milling features (slot, blind slot, step, blind step, pockets) precisely. The current work is well suitable to extract the features in conventional machining parts and thereby improve the downstream applications likes process planning, CAPP, CAM, etc.

Machining Feature Modeling and Process Intermediate Model Generation in Process Planning

2013 ◽  
Author(s):  
Xu Zhang ◽  
Chao Liang ◽  
Tiedong Si ◽  
Ding Ding
Keyword(s):  
Process Planning ◽  
Feature Recognition ◽  
3D Models ◽  
Machining Process ◽  
Model Generation ◽  
Machining Features ◽  
Intermediate Model ◽  
Machining Feature ◽  
Feature Based ◽  
Solid Modeler

In process planning of machined part, machining feature recognition and representation, feature-based generative process planning, and the process intermediate model generation are the key issues. While many research results have been achieved in recent years, the complete modeling of machining features, process operations, and the 3D models in process planning are still need further research to make the techniques to be applied in practical CAPP systems. In this paper, a machining feature definition and classification method is proposed for the purpose of process planning based on 3D model. Machining features are defined as the surfaces formed by a serious of machining operation. The classification scheme of machining features is proposed for the purpose of feature recognition, feature-based machining operations reasoning, and knowledge representation. Recognized from B-Rep representation of design model, machining features are represented by adjacent graph and organized by feature relations. The machining process plan is modeled as operations and steps, which is the combination and sequencing of machining feature’s process steps. The process intermediate models (PIM) are important for process documentation, analysis and NC programming. An automatic PIM generation approach is proposed using local operations directly on B-Rep model. The proposed data structure and algorithm is adopted in the development of CAPP tool on solid modeler ACIS/HOOPS.

Feature-Based Machining Precedence Reasoning and Sequence Planning

1998 ◽  
Author(s):  
Yong Se Kim ◽  
Eric Wang ◽  
Choong Soo Lee ◽  
Hyung Min Rho
Keyword(s):  
Feature Recognition ◽  
Search Space ◽  
Machining Process ◽  
Planning System ◽  
Process Information ◽  
Computer Aided Process Planning ◽  
Sequence Planning ◽  
Precedence Relations ◽  
Feature Based ◽  
Form Features

Abstract This paper presents a feature-based method to support machining sequence planning. Precedence relations among machining operations are systematically generated based on geometric information, tolerance specifications, and machining expertise. The feature recognition method using Alternating Sum of Volumes With Partitioning (ASVP) Decomposition is applied to obtain a Form Feature Decomposition (FFD) of a part model. Form features are classified into a taxonomy of atomic machining features, to which machining process information has been associated. Geometry-based precedence relations between features are systematically generated using the face dependency information obtained by ASVP Decomposition and the features’ associated machining process information. Multiple sets of precedence relations are generated as alternative precedence trees, based on the feature types and machining process considerations. These precedence trees are further enhanced with precedence relations from tolerance specifications and machining expertise. Machining sequence planning is performed for each of these precedence trees, applying a matrix-based method to reduce the search space while minimizing the number of tool changes. The precedence trees may then be evaluated based on machining cost and other criteria. The precedence reasoning module and operation sequence planning module are currently being implemented within a comprehensive Computer-Aided Process Planning system.

Integration of Feature Based Design and Feature Recognition

1995 ◽  
Author(s):  
JungHyun Han ◽  
Aristides A. G. Requicha
Keyword(s):  
Artificial Intelligence ◽  
Process Planning ◽  
Feature Recognition ◽  
Uncertain Reasoning ◽  
Machining Features ◽  
Artificial Intelligence Techniques ◽  
On Demand ◽  
Machined Parts ◽  
Feature Based ◽  
Feature Based Design

Abstract Process planning for machined parts typically requires that a part be described through machining features such as holes, slots and pockets. This paper presents a novel feature finder, which automatically generates a part interpretation in terms of machining features, by utilizing information from a variety of sources such as nominal geometry, tolerances and attributes, and design features. The feature finder strives to produce a desirable interpretation of the part as quickly as possible. If this interpretation is judged unacceptable by a process planner, alternatives can be generated on demand. The feature finder uses a hint-based approach, and combines artificial intelligence techniques, such as blackboard architecture and uncertain reasoning, with the geometric completion procedures first introduced in the OOFF system previously developed at USC.

A Case Retrieving System for Machining Processes of Axisymmetric Parts Based on Geometric Indexing and Process Hierarchical Structure

1998 ◽  
Author(s):  
Hsin-chi Chang ◽  
Wen F. Lu ◽  
Xiaoqing Frank Liu
Keyword(s):  
Hierarchical Structure ◽  
Process Planning ◽  
Global Market ◽  
Machining Process ◽  
Manufacturing Processes ◽  
Machining Processes ◽  
Similarity Metric ◽  
The Core ◽  
Feature Based ◽  
Cutting Processes

Abstract To develop a new machining process plan in the competitive global market, one effective way is to retrieve a relevant case similar to the new desired case and then adapt it to meet the new situations. This paper proposes a retrieving system for process planning cases from two aspects — the representation and manufacturing processes of a part. The core of the retrieving system contains 1) the feature-based representation of a part, 2) indexing of a part based on two-dimensional peripheral indexing and feature indexing, 3) a hierarchical structure of cutting processes from part cutting history, 4) a similarity metric. The similarity metric is used to measure the similarity between the new desired part and one old case based on the indexes of the parts. The application domain here is the process planning for axisymmetric parts. The proposed retrieving system is implemented in a Sun workstation using ACIS 3D-Toolkit and C++.

Feature Extraction Techniques by Optimal Volume Decomposition

1996 ◽  
Author(s):  
Jian Dong ◽  
Sreedharan Vijayan
Keyword(s):  
Feature Extraction ◽  
Process Planning ◽  
Feature Recognition ◽  
Optimization Methods ◽  
Manufacturing Cost ◽  
Extraction Techniques ◽  
Manufacturing Planning ◽  
Cad Cam ◽  
Feature Based ◽  
Manufacturing Feature

Abstract The elements of Computer-Aided Manufacturing, do not make full use of the part description stored in a CAD model because it exists in terms of low-level faces, edges and vertices or primitive volumes related to the manufacturing planning task. Consequently manufacturing planning still depends upon human expertise and input to interpret the part definition according to manufacturing needs. Feature-based technology is becoming an important tool to resolve this and other related problems. One approach is to design the part using Features directly. Another approach is Manufacturing Feature Extraction and Recognition. Manufacturing Feature Extraction consists of searching for the part description, recognizing cavity features, extracting those features as solid volumes of material to be removed. Feature Recognition involves raising this information to the level of part features which can be read by a process planning program. The feature extraction can be called optimal if the manufacturing cost of the component using those features can be minimized. An optimized feature extraction technique using two powerful optimization methods viz., Simulated Annealing and Genetic Algorithm is presented in this paper. This work has relevance in the areas of CAD/CAM linking, process planning and manufacturability assessment.

Generative Process Planning of Prismatic Parts by Feature Relaxation

1989 ◽  
Author(s):  
M. Mantyla ◽  
J. Opas ◽  
J. Puhakka
Keyword(s):  
Process Planning ◽  
Feature Recognition ◽  
Fundamental Problem ◽  
Generative Process ◽  
User Input ◽  
Prismatic Parts ◽  
Milling Operations ◽  
Product Models ◽  
Generative Process Planning ◽  
Feature Based

Abstract Feature-based product models have recently been proposed as a basis for generative process planning systems for mechanical applications. In this approach, the part is broken into a set of manufacturing features which are associated with various kinds of technological information useful for process planning. A fundamental problem of this approach is the fact that a given part usually has several interpretations as features. Ideally, all these interpretations should be taken into account in the process planner in order to achieve globally optimal plans. Hence, any planner that starts from a fixed collection of features created by feature recognition or by user input has already committed itself to a limited view of the part, and cannot take into account manufacturing opportunities corresponding with the other views. As a solution to this problem of premature commitment, we propose the use of what we call relaxed feature models. In this approach, features can be reinterpreted by the process planner to take into account manufacturing possibilities from a wider range than what any particular selection would make possible. As an example of the benefits of this approach, we describe the manufacturability analysis component of our experimental generative process planner for 3-axis milling operations of prismatic parts.

Material Efficiency and Economics of Hybrid Additive Manufacturing

2021 ◽  
Author(s):  
Peter Francis Reginald Elvis ◽  
Senthilkumaran Kumaraguru
Keyword(s):  
Additive Manufacturing ◽  
Case Studies ◽  
Cost Effective ◽  
Machining Process ◽  
Total Production ◽  
Material Efficiency ◽  
Aerospace Applications ◽  
Feature Based ◽  
Near Net Shaping ◽  
Net Shaping

Abstract In the past few years, Hybrid Additive Manufacturing has emerged to take advantage of both Additive Manufacturing and Subtractive Manufacturing processes and also to overcome the limitation of one process with the other. In aerospace applications, material wastage has become an issue in conventional machining process which reflects in total production cost and time. Especially, when dealing with expensive materials, conventional processes lack material efficiency with high buy-to-fly ratio which results in increased material cost. This paper deals with Hybrid Additive Manufacturing involving two different volume partitioning strategies — (i) Feature-based volume partitioning method (ii) Stock-based near net-shaping volume partitioning method to discuss the economics and material efficiency of Hybrid Additive Manufacturing process via simple cost estimator (formulated from the existing literature) by comparing these two volume partitioning strategies through suitable case studies — (i) Turbine blade and (ii) Impeller. From the results, it was found that the feature-based volume partitioning method was found to be material efficient and cost effective than the stock based near net shaping volume partitioning method in both the case studies.

An Object-Oriented Feature-Based CAD/CAPP/CAM Integration Framework

1991 ◽  
Author(s):  
Ming-Tzong Wang
Keyword(s):  
Process Planning ◽  
Feature Recognition ◽  
Production Control ◽  
Object Oriented ◽  
Secondary Process ◽  
Integration Framework ◽  
Planning Stage ◽  
Primary Level ◽  
Feature Based ◽  
Part Model

Abstract This paper presents an object-oriented feature-based CAD/CAPP/CAM integration framework in a concurrent engineering environment. The framework which is still under development at Yuan-Ze’s automation center is a layered hierarchy based on an object-oriented feature-based part model. The part model serves as a central database for design, process planning, and manufacturing related activities. Feature-based design approach in conjunction with feature recognition approach is incorporated in this integration framework. This dual approach can balance overall computational efficiency and modeling complexity of the interface between design and process planning. There are three levels at the process planning stage : primary level, secondary level and detailing level. The primary level is concerned with the issues of intermediate shape generation. At the secondary process planning level the intermediate specifications of features and workpieces are determined. The detailing level elaborates on the selection of corresponding facilities and parameters for each manufacturing operation. This manufacturing information is used to drive the downstream CAM activities such as NC part programming, production control and scheduling, operation setup and so forth.

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