Setup planning for machining the features of prismatic parts

2008 ◽  
Vol 46 (12) ◽  
pp. 3241-3257 ◽  
Author(s):  
S. S. Hebbal ◽  
N. K. Mehta
Keyword(s):  
Setup Planning ◽  
Prismatic Parts

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.

Adaptive Setup Planning of Prismatic Parts by Tool Accessibility Examination

2005 ◽  
Author(s):  
Ningxu Cai ◽  
Lihui Wang ◽  
Hsi-Yung Feng
Keyword(s):  
Machine Tools ◽  
Best Practice ◽  
Spherical Surface ◽  
Surface Patch ◽  
Setup Planning ◽  
Machining Features ◽  
Surface Areas ◽  
Machining Feature ◽  
Prismatic Parts ◽  
Tool Accessibility

Setup planning for machining a part is to determine the number and sequence of setups (including machining features grouping in setups) and the part orientation of each setup. Tool accessibility plays a key role in this process. An adaptive setup planning approach for different types of multi-axis machine tools is proposed in this paper by investigating Tool Access Directions (TADs) of machining features, Tool Orientation Spaces (TOSs) of machine tools, and Primary Locating Directions (PLDs) of workpieces. In our approach, feasible TADs of a machining feature are predefined based on feature geometry and best practice knowledge, and denoted by unit vectors; The TOS of a machine tool is generated according to its configuration through kinematic analysis, and represented by a unit spherical surface patch; Primary locating directions and their priorities of a workpiece are determined based on the surface areas and the surface accuracy grades of non-machining surfaces. Starting from a 3-axis based machining feature grouping, setups for a 3-, 4- (or 3-axis with indexing table), or 5-axis machine can be achieved effectively by tool accessibility examination. A so-generated setup plan can provide not only the best coverage of machining features but the optimal orientation for each setup. Prismatic parts are considered in the proof-of-concept phase. Algorithms introduced here are implemented in MATLAB, and a case study is used to show the results.

An Investigation Into the Compliance of SCARA Robots. Part II: Experimental and Numerical Validation

1988 ◽  
Vol 110 (1) ◽  
pp. 23-30 ◽  
Author(s):  
H. A. ElMaraghy ◽  
B. Johns
Keyword(s):  
Elastic Compliance ◽  
Servo Control ◽  
Compliance Matrix ◽  
End Effector ◽  
Prismatic Parts ◽  
Numerical Solution Methods ◽  
Solution Methods ◽  
Using Data ◽  
Assembly Performance ◽  
Robot Configurations

A model of inherent elastic compliance was developed for general position-controlled SCARA, with conventional joint feedback control, for both rotational and prismatic part insertion (Part I). The developed model was applied to the SKILAM and ADEPT I robots for validation. Experimental procedures and numerical solution methods are described. It was found that the ADEPT I robot employs a coupled control strategy between joints one and two which produces a constant, decoupled end effector compliance. The applicable compliance matrix, in this case, is presented and the experimental results are discussed. The model may be used to develop compliance maps that define the amount of end effector compliance, as a function of the joints compliance, as well as its variation for different robot configurations. This is illustrated using data for the SKILAM SCARA robot. Results are plotted and discussed. The most appropriate robot postures for assembly were found for both rotational and prismatic parts. The conditions necessary to achieve compliance or semicompliance centers with the SKILAM robot were examined. The results and methods demonstrated in these examples may be used to select appropriate robots for given applications. They can also guide robot designers in selecting joint servo-control gains to obtain the desired joints compliance ratio and improve assembly performance.

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.

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