GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts

2006 ◽  
Vol 33 (5-6) ◽  
pp. 594-603 ◽  
Author(s):  
Guang-ru Hua ◽  
Xiong-hui Zhou ◽  
Xue-yu Ruan
Keyword(s):  
Operation Sequencing ◽  
Prismatic Parts ◽  
Scheme Selection ◽  
Synthesis Approach ◽  
Machining Scheme

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.

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.

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

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.

A Feasible Sequence Oriented Discrete Particle Swarm Optimization for Operation Sequencing in CAPP

2017 ◽  
Author(s):  
Jianping Dou ◽  
Xia Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Discrete Particle Swarm Optimization ◽  
Operation Sequencing ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Prismatic Parts ◽  
Sequencing Problems

Operation sequencing is one of crucial tasks for process planning in any CAPP system. In this study, a novel discrete particle swarm optimization (DPSO) approach is proposed to solve the operation sequencing problems in CAPP. To find the process plan with lowest machining cost efficiently, the DPSO only searches the feasible operation sequences (FOSs) satisfying precedence constraints among operations. In the DPSO, a FOS is directly represented by a permutation via a particle and the fragment crossover based updating mechanism is developed to evolve the particles. Furthermore, the fragment mutation for altering FOS and the uniform mutation for changing machine, cutting tool and tool access direction for each operation are incorporated into the DPSO to improve exploration ability. A case study involving two prismatic parts are used to verify the performance and efficiency of the DPSO. The comparison between the DPSO and two existing PSOs as well as an existing genetic algorithm shows promising higher performance of the DPSO with respect to solution quality for operation sequencing.

Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process

2014 ◽  
Vol 28 (1) ◽  
pp. 71-82 ◽  
Author(s):  
Yan-Juan Hu ◽  
Yao Wang ◽  
Zhan-Li Wang ◽  
Yi-Qiang Wang ◽  
Bang-Cheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Analytic Hierarchy Process ◽  
Decision Maker ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Scheme Selection ◽  
Hierarchy Process ◽  
Machining Scheme

AbstractThe goal of machining scheme selection (MSS) is to select the most appropriate machining scheme for a previously designed part, for which the decision maker must take several aspects into consideration. Because many of these aspects may be conflicting, such as time, cost, quality, profit, resource utilization, and so on, the problem is rendered as a multiobjective one. Consequently, we consider a multiobjective optimization problem of MSS in this study, where production profit and machining quality are to be maximized while production cost and production time must be minimized, simultaneously. This paper presents a new discrete method for particle swarm optimization, which can be widely applied in MSS to find out the set of Pareto-optimal solutions for multiobjective optimization. To deal with multiple objectives and enable the decision maker to make decisions according to different demands on each evaluation index, an analytic hierarchy process is implemented to determine the weight value of evaluation indices. Case study is included to demonstrate the feasibility and robustness of the hybrid algorithm. It is shown from the case study that the multiobjective optimization model can simply, effectively, and objectively select the optimal machining scheme according to the different demands on evaluation indices.

Multiple Access using Time Modulated Array with Single RF Chain

2019 ◽  
Vol 12 (5) ◽  
pp. 395-401
Author(s):  
Xu He ◽  
Yuan Ding ◽  
Gaojian Huang
Keyword(s):  
Power Efficiency ◽  
Multiple Access ◽  
Narrow Band ◽  
Average Power ◽  
Access Technology ◽  
Signal Peak ◽  
Single Radio ◽  
Rf Chain ◽  
Synthesis Approach ◽  
Single Rf Chain

Background: A new wireless multiple access technology enabled by using Time Modulated Arrays (TMAs) is proposed in this paper. Methods: It benefits due to the requirement of only a single Radio Frequency (RF) chain, compared with other multiple-RF-chain schemes. Results: As a result, it is able to greatly reduce the system cost, energy consumption, and complexity. Conclusion: In addition, the signal through the single RF chain is narrow-band modulated, reducing the signal Peak-to-Average-Power-Ratio (PAPR), thus, further enhancing the power efficiency of the RF chain, especially for power amplifiers. The operation principle and synthesis approach are elaborated in this paper, and are demonstrated with two examples.

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