Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts

2012 ◽  
pp. 102-129
Author(s):  
T. Srikanth Reddy ◽  
M. S. Shunmugam
Keyword(s):  
Feature Recognition ◽  
Geometric Dimension ◽  
Connectivity Matrix ◽  
Setup Planning ◽  
Priority Rules ◽  
Operation Sequencing ◽  
Prismatic Parts ◽  
Manufacturing Activity ◽  
Edge Features ◽  
Planning And Operation

An automated planning system extracts data from design models and processes it efficiently for transfer to manufacturing activity. Researchers have used face adjacency graphs and volume decomposition approaches which make the feature recognition complex and give rise to multiple interpretations. The present work recognizes the features in prismatic parts considering Attributed Adjacency Matrix (AAM) for the faces of delta volume that lie on rawstock faces. Conceptually, intermediate shape of the workpiece is treated as rawstock for the next stage and tool approach direction is used to recognize minimum, yet practically feasible, set of feature interpretations. Edge-features like fillets/undercuts and rounded/chamfer edges are also recognized using a new concept of Attributed Connectivity Matrix (ACM). In the first module, STEP AP-203 format of a model is taken as the geometric data input. Datum information is extracted from Geometric Dimension and Tolerance (GD&T) data. The second module uses features and datum information to arrive at setup planning and operation sequencing on the basis of different criteria and priority rules.

Feature Recognition and Datum Extraction for Setup Planning and Operation Sequencing for Prismatic Parts

2011 ◽  
Vol 1 (2) ◽  
pp. 19-45
Author(s):  
T. Srikanth Reddy ◽  
M. S. Shunmugam
Keyword(s):  
Feature Recognition ◽  
Geometric Dimension ◽  
Planning System ◽  
Connectivity Matrix ◽  
Setup Planning ◽  
Priority Rules ◽  
Operation Sequencing ◽  
Prismatic Parts ◽  
Edge Features ◽  
Planning And Operation

An automated planning system extracts data from design models and processes it efficiently for transfer to manufacturing activity. Researchers have used face adjacency graphs and volume decomposition approaches which make the feature recognition complex and give rise to multiple interpretations. The present work recognizes the features in prismatic parts considering Attributed Adjacency Matrix (AAM) for the faces of delta volume that lie on rawstock faces. Conceptually, intermediate shape of the workpiece is treated as rawstock for the next stage and tool approach direction is used to recognize minimum, yet practically feasible, set of feature interpretations. Edge-features like fillets/undercuts and rounded/chamfer edges are also recognized using a new concept of Attributed Connectivity Matrix (ACM). In the first module, STEP AP-203 format of a model is taken as the geometric data input. Datum information is extracted from Geometric Dimension and Tolerance (GD&T) data. The second module uses features and datum information to arrive at setup planning and operation sequencing on the basis of different criteria and priority rules.

Automatic Setup Planning and Operation Sequencing for Satisfying Tolerance Requirements

2001 ◽  
Author(s):  
Yuguang Wu ◽  
Shuming Gao ◽  
Zichen Chen
Keyword(s):  
Machine Tools ◽  
High Efficiency ◽  
Feature Model ◽  
Setup Planning ◽  
Operation Sequencing ◽  
Feature Sets ◽  
Novel Approach ◽  
Machining Operations ◽  
Planning And Operation

Abstract In this paper, a novel approach to automatic setup planning and operation sequencing of a part is proposed. The critical tolerance feature sets of the part are first identified by comparing the tolerance specifications with the capabilities of machine tools. Then, based on the concepts of critical tolerance feature set, the feature model of the part is decomposed into a series of identical setup feature (ISF) sets, each of which corresponds to a setup candidate. Finally, according to geometric precedence and tolerance precedence among features, the reasonable machining operations of features with multiple machining operations are determined, the ISF sets are sequenced and merged. Compared to previous work, the approach can generate the setup plan by which the part can be machined to satisfy the required tolerances. The approach also has high efficiency.

Application of genetic algorithm in integrated setup planning and operation sequencing

2011 ◽  
Author(s):  
Sajad Kafashi ◽  
Mohsen Shakeri ◽  
Francisco Chinesta ◽  
Yvan Chastel ◽  
Mohamed El Mansori
Keyword(s):  
Genetic Algorithm ◽  
Setup Planning ◽  
Operation Sequencing ◽  
Planning And Operation

A Hybrid Approach to Computer-Aided Process Planning for Prismatic Parts

1994 ◽  
Author(s):  
Y. F. Zhang ◽  
A. Y. C. Nee ◽  
J. Y. H. Fuh
Keyword(s):  
Process Planning ◽  
Hybrid Approach ◽  
Optimization Method ◽  
Optimal Operation ◽  
Operation Sequencing ◽  
Computer Aided Process Planning ◽  
Prismatic Parts ◽  
Minimum Number ◽  
Set Up

Abstract One of the most difficult tasks in automated process planning is the determination of operation sequencing. This paper describes a hybrid approach for identifying the optimal operation sequence of machining prismatic parts on a three-axis milling machining centre. In the proposed methodology, the operation sequencing is carried out in two levels of planning: set-up planning and operation planning. Various constraints on the precedence relationships between features are identified and rules and heuristics are created. Based on the precedence relationships between features, an optimization method is developed to find the optimal plan(s) with minimum number of set-ups in which the conflict between the feature precedence relationships and set-up sequence is avoided. For each set-up, an optimal feature machining sequence with minimum number of tool changes is also determined using a developed algorithm. The proposed system is still under development and the hybrid approach is partially implemented. An example is provided to demonstrate this approach.

A GA-based approach to machining operation sequencing for prismatic parts

2000 ◽  
Vol 38 (14) ◽  
pp. 3283-3303 ◽  
Author(s):  
L. Qiao ◽  
X.-Y. Wang ◽  
S.-C. Wang
Keyword(s):  
Operation Sequencing ◽  
Prismatic Parts
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