The application of genetic algorithms to operation sequencing for use in computer-aided process planning

1996 ◽  
Vol 30 (4) ◽  
pp. 999-1013 ◽  
Author(s):  
John M. Usher ◽  
Royce O. Bowden
Keyword(s):  
Genetic Algorithms ◽  
Process Planning ◽  
Operation Sequencing ◽  
Computer Aided Process Planning ◽  
Computer Aided

An ant colony approach to operation sequencing optimization in process planning

2016 ◽  
Vol 231 (3) ◽  
pp. 470-489 ◽  
Author(s):  
Quanwei Hu ◽  
Lihong Qiao ◽  
Guanwei Peng
Keyword(s):  
Ant Colony Optimization ◽  
Process Planning ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm ◽  
Operation Sequencing ◽  
Computer Aided Process Planning ◽  
Process Plans ◽  
Computer Aided

Computer-aided process planning is an important component for linking design and manufacturing in computer-aided design/computer-aided process planning/computer-aided manufacturing integrated manufacturing systems. Operation sequencing in computer-aided process planning is one of the most essential tasks. To solve the problem and acquire optimal process plans, operation sequencing is modeled as a combinatorial optimization problem with various constraints, and a novel modified ant colony optimization algorithm is developed to solve it. To ensure the feasibility of process plans, constrained relationships considered among operations are classified into two categories called precedence constraint relationships and clustering constraint relationships. Operation precedence graph based on constrained relationships is formed to get visual representation. To ensure good manufacturing economy, in the mathematical model for optimization, total weighted production cost or weighted resource transformation time related to machine changes, setup changes, tool changes, machines and tools is utilized as the evaluation criterion. To avoid local optimum and enhance global search ability, adaptive updating method and local search mechanism are embedded into the optimization algorithm. Case studies of three parts are carried out to demonstrate the feasibility and robustness of the modified ant colony optimization algorithm, and some comparisons between the modified ant colony optimization algorithm and previous genetic algorithm, simulated annealing algorithm, tabu search and particle swarm optimization algorithm are discussed. The results show that the modified ant colony optimization algorithm performs well in the operation sequencing problem.

scholarly journals A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

2021 ◽  
Vol 11 (5) ◽  
pp. 1981
Author(s):  
Mica Djurdjev ◽  
Robert Cep ◽  
Dejan Lukic ◽  
Aco Antic ◽  
Branislav Popovic ◽  
...  
Keyword(s):  
Process Planning ◽  
Computer Aided Design ◽  
Search Algorithm ◽  
Combinatorial Problem ◽  
Optimization Criterion ◽  
Total Production ◽  
Operation Sequencing ◽  
Computer Aided Process Planning ◽  
Sequencing Problem ◽  
Computer Aided

Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning.

Machining Feature-Based CAD/CAPP for STEP-NC

2014 ◽  
Vol 598 ◽  
pp. 591-594 ◽  
Author(s):  
Li Yan Zhang
Keyword(s):  
Process Planning ◽  
Manufacturing Process ◽  
Data Transfer ◽  
Operation Planning ◽  
Computer Aided Process Planning ◽  
Machining Feature ◽  
Computer Aided ◽  
Cad Cam ◽  
Feature Based ◽  
Cam Systems

ISO 14649, known as STEP-NC, is new model of data transfer between CAD/CAM systems and CNC machines. In this paper, the modeling based on machining feature is proposed. The machining feature comes from the manufacturing process considering the restriction of machining technology and machining resource. Then the framework for computer aided process planning is presented, where the algorithms of operation planning is studied. The practical example has been provided and results indicate that machining feature based model can integrate with CAPP and STEP-NC seamlessly.

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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