A procedure for planning acyclic setups on the basis of simultaneous sequencing of setups and features

Manafi, D.; Nategh, Mohammad Javad

doi:10.1007/s00170-015-7740-8

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…The machining features are identified using the graph-based method. The technological rules (TRs) and geometrical rules (GRs) considered in this work are as follows: 9…”

Section: Manufacturing Information For Fms and Setup Determinationmentioning

confidence: 99%

“…The machining features are identified using the graph-based method. The technological rules (TRs) and geometrical rules (GRs) considered in this work are as follows: 9 TR 1 : If the locating/clamping surface required for the feature F 1 is removed as the feature F 2 is machined, then the feature F 1 should be machined before F 2 . TR 2 : If the surface necessary for positioning the cutting tool of feature F 1 is eliminated by machining the feature F 2 , then F 1 machining is prior to F 2 machining.…”

Section: Manufacturing Information For Fms and Setup Determinationmentioning

confidence: 99%

“…1,2 The methods for FMS or operation machining sequence (OMS) and setup determination by the researchers are divided into two general categories. In the first category, the researchers introduce novel concepts, such as the enriched machining feature (EMF) 3,4 and employ analytical approaches, such as the graph theory 5,6 and permutation method, [7][8][9] for setup and FMS determination. In the second category, the researchers assume the setup planning and FMS determination as a constrained optimization problem which can be solved by heuristic algorithms, such as the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and ant colony (AC).…”

Section: Introductionmentioning

confidence: 99%

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Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

Manafi

Nategh

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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One of the main objectives of computer-aided process planning is to determine the optimum machining sequences and setups. Among the different methods to implement this task, it can be named the constrained optimization algorithms. The immediate drawback of these algorithms is usually a large space needed to be searched for the solution. This can easily hinder the convergence of the solution and increase the possibility of getting trapped in the local minima. A novel approach has been developed in this work with the objective of reducing the search space. It is based on consolidating the decisive factors influencing the consecutive features. This helps prevent creation of sequences which need the change of setup, machine tool, and cutting tool. The proposed method has been applied to three different optimization methods, including genetic, particle swarm, and simulated annealing algorithms. It is shown that these algorithms with reduced search spaces outperform those reported in the literature, with respect to the convergence rate. The best results are found in the genetic algorithm from the viewpoint of the obtained solution and the convergence rate. The worst results belong to the particle swarm algorithm in connection with the strategy of generating new solutions.

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“…The machining features are identified using the graph-based method. The technological rules (TRs) and geometrical rules (GRs) considered in this work are as follows: 9…”

Section: Manufacturing Information For Fms and Setup Determinationmentioning

confidence: 99%

Section: Manufacturing Information For Fms and Setup Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

Manafi

Nategh

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…The case in Figure 6 is a part adopted from existing case study by Manafi and Nategh [20]. There are seven TADs in this figure, which are ±x, ±y, ±z and -a respectively and they are numbered from 1 to 7.…”

Section: Case Studymentioning

confidence: 99%

A hybrid optimization approach for setup planning with tolerance constraints

Zeng

Huang

2018

MATEC Web Conf.

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Computer-aided process planning (CAPP) plays an important role in integrated manufacturing system and it can serve as a bridge between CAD and CAM. As a crucial part of CAPP, setup planning is a multi-constraint problem, in which the precision takes priority over efficiency. However, instead of precision constraints, traditional optimization methods have paid much more attention to efficiency requirements. This leads to the reduction in the precision of the final parts. This paper develops an optimization approach for solving computer-aided setup planning problem, which takes into account various constraints, especially the precision requirements specified by designers. First, objective function of the optimization model is formulated and a series of constraints, including feature precedence, tool approaching direction (TAD), and precision requirements are systematically created. Next, the model is solved by using a hybrid particle swarm optimization algorithm. In order to overcome the local optimum trap, mutation and exchange operations are adopted from the genetic algorithm. Finally, a part is tested in the case study and the validation of this method is proved.

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An optimization approach for setup planning and operation sequencing with tolerance constraints

Huang

et al. 2020

Int J Adv Manuf Technol

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A procedure for planning acyclic setups on the basis of simultaneous sequencing of setups and features

Cited by 8 publications

References 27 publications

Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

A hybrid optimization approach for setup planning with tolerance constraints

An optimization approach for setup planning and operation sequencing with tolerance constraints

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