Machining Feature Recognition for Cast Then Machined Parts

1999 ◽  
Author(s):  
Yong Se Kim ◽  
Eric Wang
Keyword(s):  
Feature Recognition ◽  
Machining Process ◽  
Recognition Method ◽  
Machining Features ◽  
Surface Machining ◽  
Sufficient Detail ◽  
Machined Parts ◽  
Machining Operations ◽  
Machining Process Planning ◽  
Part Model

Abstract We present a method to recognize machining features for the domain of cast-then-machined parts. Non-interacting volumetric machining features are recognized through a face pattern based recognition approach, and are filtered out of the part model. From the filtered part model and the specification of part surfaces as being cast or machined, we systematically generate the surface machining features and the starting workpiece, which represents the casting output in sufficient detail to support machining process planning. By subtracting the filtered part from its starting workpiece, we obtain the removal volume that is to be realized through machining operations. We apply the feature recognition method using Alternating Sum of Volumes With Partitioning (ASVP) Decomposition to decompose this removal volume into volumetric machining features.

POWERTRAIN MACHINING FEATURE IDENTIFICATION

2004 ◽  
Vol 03 (01) ◽  
pp. 103-110 ◽  
Author(s):  
SANGCHUL PARK
Keyword(s):  
Feature Recognition ◽  
Geometric Algorithms ◽  
Machining Process ◽  
Feature Identification ◽  
Machining Features ◽  
Efficient Procedure ◽  
Machining Feature ◽  
Intuitive Idea ◽  
Machining Process Planning ◽  
Part Model

Presented in this paper is a procedure to identify machining features of powertrain components. Machining feature recognition is one of the most important steps for machining process planning. In the case of powertrain components, the first step is to compare a machined model (finished part model) and the corresponding rough part model to identify the volume which should be removed from the rough part model. In regard to the comparison, the most intuitive idea is to use a 3D BOOLEAN operation. Although this approach looks fine, it might not take advantage of the inherent attributes of powertrain component machining. This paper focuses on two important attributes of powertrain machining: (1) a machined model and the corresponding rough part model are very similar and have many identical faces and (2) a rough part model always contains the machined model. Based on these two attributes, we develop an efficient procedure for identifying powertrain machining features. Since the proposed procedure employs well-known 2D geometric algorithms instead of 3D BOOLEAN operations, it is very efficient and robust.

Recognition of Freeform Surface Machining Features

2010 ◽  
Vol 10 (4) ◽  
Author(s):  
Jun Wang ◽  
Zhigang Wang ◽  
Weidong Zhu ◽  
Yingfeng Ji
Keyword(s):  
Critical Points ◽  
Computer Aided Design ◽  
Tool Path ◽  
Machining Process ◽  
Freeform Surface ◽  
Critical Surface ◽  
Machining Features ◽  
Computer Aided ◽  
Freeform Surface Machining ◽  
Machining Process Planning

This paper describes a method of machining feature recognition from a freeform surface based on the relationship between unique machining patches and critical points on a component’s surface. The method uses Morse theory to extract critical surface points by defining a scalar function on the freeform surface. Features are defined by region growing between the critical points using a tool path generation algorithm. Several examples demonstrate the efficiency of this approach. The recognized machining features can be directly utilized in a variety of downstream computer aided design/computer aided manufacturing (CAM) applications, such as the automated machining process planning.

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.

scholarly journals T-Spline Surface Toolpath Generation Using Watershed-Based Feature Recognition

2020 ◽  
Vol 10 (19) ◽  
pp. 6790
Author(s):  
Yazui Liu ◽  
Gang Zhao ◽  
Pengfei Han
Keyword(s):  
Feature Recognition ◽  
Machining Process ◽  
Freeform Surface ◽  
Local Refinement ◽  
Surface Machining ◽  
Spline Surface ◽  
Toolpath Generation ◽  
Comparison Results ◽  
Bounding Boxes ◽  
Freeform Surface Machining

The freeform surface is treated as a single machining region for most traditional toolpath generation algorithms. However, due to the complexity of a freeform surface, it is impossible to produce a high-quality surface using one unique machining process. Hence, region-based methods are widely investigated for freeform surface machining to achieve an optimized toolpath. The Non-Uniform Rational B-spline Surface (NURBS) represented freeform surface is not suitable for region-based toolpath generation because of the surface gaps caused by NURBS trimming and merging operations. To solve the limitation of the NURBS, T-spline is proposed with the advantages of being gap-free, having less control points, and local refinement, which is an ideal tool for region-based toolpath generation. Thus, T-spline is introduced to represent a freeform surface for its toolpath generation in the paper. A region-based toolpath generation method for the T-spline surface is proposed based on watershed technology. Firstly, watershed-based feature recognition is presented to divide the T-spline surface into a set of sub-regions. Secondly, the concept of a PolyBoundingBox that consists of a set of minimum bounding boxes is proposed to describe the sub-regions, and Manufacturing-Suitable Regions are constructed with the help of T-spline local refinement and the PolyBoundingBox. In the end, an optimized multi-rectangles toolpath generation algorithm is applied for sub-regions. The proposed method is tested using three synthetic T-spline surfaces, and the comparison results show the advantage in toolpath length and toolpath reversing number.

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.

scholarly journals Development of product complexity index in 3D models using a hybrid feature recognition method with rule-based and graph-based methods

2021 ◽  
Vol 3 (1 (111)) ◽  
pp. 47-61
Author(s):  
Hendri Dwi Saptioratri Budiono ◽  
Finno Ariandiyudha Hadiwardoyo
Keyword(s):  
Real Time ◽  
Production Cost ◽  
Feature Recognition ◽  
Production Costs ◽  
Machining Process ◽  
Design Stage ◽  
Product Complexity ◽  
Recognition Method ◽  
Rule Based ◽  
Complexity Index

A machining process is very dependent on the model created. The more complicated the model, the greater the design difficulty and the greater the machining process. Reduced production costs can help a company increase profits. A focus on production cost can be achieved in a number of ways, the first of which is by replacing materials or changing the design. It is better to reduce product costs during the design stage than during the manufacturing stage. The main objective of this research is to develop an application that can recognize features in a CAD program and calculate the complexity index of shapes in real time. In this study, the prismatic features and slab features classified by Jong-Yun Jung were used. The feature recognition method applied in this study is a hybrid of the rule-based and graph-based methods, which uses the STL file developed by Sunil and Pande to obtain all the information needed. Then, the results are extracted from feature recognition data and are used to calculate the product complexity index of the model being studied. This study applied the product complexity index, following the model developed earlier by El Maraghy. Validation is performed by comparing the software count with the complexity index calculated with the STEP method by Hendri and Sholeh et al. This research develops a program that recognizes features in CAD software and calculates the index complexity of shapes in real time. This will allow designers to calculate the expected complexity value during the design process. As a result, the estimated production cost can be seen early on. Finally, this software is tested for calculating the index values for the complexity of a combined features model. The use of eight slots and eight pockets as a benchmark scoring for shape produces a more accurate product complexity index

Recognition of Machining Features Based on HSPCE Decomposition, Feature Composition, and Process Centered Classification

1998 ◽  
Vol 120 (4) ◽  
pp. 668-677 ◽  
Author(s):  
Y. Shen ◽  
J. J. Shah
Keyword(s):  
Feature Recognition ◽  
Geometric Model ◽  
General Purpose ◽  
Process Selection ◽  
Space Partitioning ◽  
Machining Features ◽  
Machining Feature ◽  
Computational Procedures ◽  
Tool Motion ◽  
Machining Operations

A comprehensive set of computational procedures have been developed for transforming a geometric model based on design features to alternative sets of machining features. In the paper we only discuss a sub-set of these procedures; those related to machining feature recognition. There are three phases in the recognition process: 1) orthonormal decomposition of the removal volumes based on half space partitioning at concave edges (HSPCE) to determine regions of interaction and to recover portions of features lost by interaction 2) concatenation of decomposed cells into candidate machining features based on cycles in cell adjacency graphs, 3) classification of these volumes with respect to accessibility and tool motion for subsequent feasible process selection. This strategy is process centered—it recognizes all features that can be produced by common machining operations in a uniform way. It is not restricted to a few predefined features that each require specific, predefined rules. It deals with many types of feature interactions with one general purpose algorithm and produces alternative sets of machining feature sequences. Several measures have been incorporated to reduce computational complexity.

Graph-Based Feature Recognition and Combination Method for Automatic Process Planning System

2020 ◽  
Author(s):  
Da Xie ◽  
Jiang Zhu ◽  
Tomohisa Tanaka
Keyword(s):  
Process Planning ◽  
Feature Recognition ◽  
Numerical Control ◽  
Machining Process ◽  
Combination Method ◽  
Planning System ◽  
Feature Combination ◽  
Computer Aided ◽  
Machining Process Planning ◽  
Manufacturing Features

Abstract Generating the Numerical Control (NC) tool path for machining a complex shaped component is highly dependent on the proficiency of a Computer-Aided Manufacturing (CAM) programmer in manufacturing field, although the CAM systems now are highly integrated. A Computer-Aided Process Planning (CAPP) system, which can automatically extract the manufacturing features from the Computer-Aided Design (CAD) model and generate the machining process planning, has been expected for a long time. In this research, a graph-based CAPP system was proposed. It mainly includes four modules, data conversion module, feature classification module, feature combination module and process planning module. The first two modules claim a graph-based feature recognition method, output the recognized manufacturing features which are classified into four classes and defined as specific types. The feature combination module generates different paths to combine manufacturing features from a goal model into raw material shape by four kinds of combination methods corresponding to the four classes. Finally, the process planning module will give a cost estimation of all those paths with the consideration of manufacturing resources and time cost. A relatively optimized machining method and machining sequence will be generated as the output of this proposed system.

scholarly journals Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples

2021 ◽  
Vol 13 (14) ◽  
pp. 2697
Author(s):  
Bo Liu ◽  
Qi Xiao ◽  
Yuhao Zhang ◽  
Wei Ni ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Feature Recognition ◽  
Spectral Characteristics ◽  
Transmission Model ◽  
Sample Type ◽  
Recognition Method ◽  
Atmospheric Transmission ◽  
K Nearest Neighbors ◽  
Imaging Simulation ◽  
Low Altitude ◽  
Intelligent Recognition

To address the problem of intelligent recognition of optical ship targets under low-altitude squint detection, we propose an intelligent recognition method based on simulation samples. This method comprehensively considers geometric and spectral characteristics of ship targets and ocean background and performs full link modeling combined with the squint detection atmospheric transmission model. It also generates and expands squint multi-angle imaging simulation samples of ship targets in the visible light band using the expanded sample type to perform feature analysis and modification on SqueezeNet. Shallow and deeper features are combined to improve the accuracy of feature recognition. The experimental results demonstrate that using simulation samples to expand the training set can improve the performance of the traditional k-nearest neighbors algorithm and modified SqueezeNet. For the classification of specific ship target types, a mixed-scene dataset expanded with simulation samples was used for training. The classification accuracy of the modified SqueezeNet was 91.85%. These results verify the effectiveness of the proposed method.

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