Machining fixture layout design using ant colony algorithm based continuous optimization method

Padmanaban, K.; Arulshri, K. P.; Prabhakaran, G.

doi:10.1007/s00170-009-2035-6

Cited by 83 publications

(41 citation statements)

References 24 publications

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“…A comparison was made between both the algorithms on the basis of minimum objective function value, and it was found that ACA solutions are better than those of GA [28]. By applying ACA, Padmanaban et al [34] recently optimized the machining fixture layout to minimize the workpiece elastic deformation discrete-based and continuous-based optimization methods. The dynamic response of the workpiece with respect to machining and clamping forces was determined using FEM.…”

Section: Intelligent Approaches To Fixture Design Systemsmentioning

confidence: 99%

Recent Developments on Computer Aided Fixture Design: Case Based Reasoning Approaches

Hashemi

Shaharoun

Izman

et al. 2014

Advances in Mechanical Engineering

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Fixture design is an important issue in the process of manufacturing. As a critical design activity process, automation in fixture design plays an integral role in linking computer aided designs and computer aided manufacturing. This paper carries out a literature review of computer aided fixture design (CAFD) developments using intelligent methods that have been commonly utilized in automation in the last two decades. The first part of this review considers the steps of fixture design along with the significant researches and requirements of fixtures over time. After that, the paper presents important and relevant research carried out in the field of CAFD using intelligent approaches and the working principles surrounding this issue. The following section concentrates on the details of case based reasoning (CBR) approach, the most successful approach in CAFD. The examination of this approach is carried out based on applications, stages of CBR based systems fixture design, working principles, and pertinent proposed approaches. Lastly, the present drawbacks of the current methods and shortfalls in research are identified for future studies.

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Section: Intelligent Approaches To Fixture Design Systemsmentioning

confidence: 99%

Recent Developments on Computer Aided Fixture Design: Case Based Reasoning Approaches

Hashemi

Shaharoun

Izman

et al. 2014

Advances in Mechanical Engineering

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show abstract

“…Similarly, with the help of FEA, Prabhaharan et al 22 presented a fixture layout optimization method that used GAs and ant colony algorithm (ACA) separately to decrease the dimensional and form errors. Padmanaban et al 23 applied the ACA-based discrete and continuous optimization methods coupled with FEA for the optimization of the fixture layout so that the elastic deformation of the workpiece is minimized. Xing 24 conducted the design and optimization of the sheet metal locating points based on the improved fruit fly optimization algorithm (IFOA).…”

Section: Introductionmentioning

confidence: 99%

Optimization of fixture locating layout for sheet metal part by cuckoo search algorithm combined with finite element analysis

Yang

Wang

Yang

et al. 2017

Advances in Mechanical Engineering

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Fixture layout has a direct and influential impact on sheet metal manufacturing quality. Much attention has been paid to sheet metal fixture locating layout in the fields of academy, engineering, and industry in the past few years. However, how to obtain the optimal fixture locating layout so as to effectively reduce the sheet metal part deformation is still a complex and challenging task. To that end, this article proposes a new approach to optimizing the sheet metal fixture locating layout based on the ''N-2-1'' locating principle by coupling cuckoo search algorithm with finite element analysis. First, the optimization model of the sheet metal locating layout based on the ''N-2-1'' locating principle is constructed. Second, the cuckoo search algorithm is employed to search the optimum layout of the ''N'' fixture locators for the minimum deformation through iterative calls to the finite element analysis solver. Finally, two cases are respectively studied and conducted to verify the proposed method, and the comparison results with genetic algorithms and particle swarm optimization show that the cuckoo search algorithm outperforms the genetic algorithms and particle swarm optimization in terms of the quality of the solutions, robustness, and usability in the sheet metal fixture locating layout optimization.

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“…Liu et al [3] determined the initial number and positions of the locators by adding locators on the datum plane at the position with the maximum deformation repeatedly until the deformation was reduced within the range of milling accuracy. Prabhaharan et al [4,5] presented the fixture layout optimization methods that used GA and ant colony algorithm (ACA) separately combined with FEA to reduce the dimensional and form errors of the deformable workpiece. Through employing FEA to compute the workpiece deformation for a given fixture layout, Dou et al [6] conducted the applications and comparisons of GA, improved GA, particle swarm optimization (PSO) and improved PSO for the fixture layout locating optimization to minimize the elastic deformation of the workpiece.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

et al. 2017

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Abstract. In the process of the traditional fixture design for sheet metal part based on the "N-2-1" locating principle, the number of fixture locating points is determined by trial and error or the experience of the designer. To that end, a new design method based on grey theory is proposed to determine the number of sheet metal fixture locating points in this paper. Firstly, the training sample set is generated by Latin hypercube sampling (LHS) and finite element analysis (FEA). Secondly, the GM(1, 1) grey model is constructed based on the established training sample set to approximate the mapping relationship between the number of fixture locating points and the concerned sheet metal maximum deformation. Thirdly, the final number of fixture locating points for sheet metal can be inversely calculated under the allowable maximum deformation. Finally, a sheet metal case is conducted and the results indicate that the proposed approach is effective and efficient in determining the number of fixture locating points for sheet metal.

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Machining fixture layout design using ant colony algorithm based continuous optimization method

Cited by 83 publications

References 24 publications

Recent Developments on Computer Aided Fixture Design: Case Based Reasoning Approaches

Recent Developments on Computer Aided Fixture Design: Case Based Reasoning Approaches

Optimization of fixture locating layout for sheet metal part by cuckoo search algorithm combined with finite element analysis

Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

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