Optimum fixture locating layout for sheet metal part by integrating kriging with cuckoo search algorithm

Yang, Bo; Wang, Zhongqi; Yang, Yuan; Kang, Young Jin; Li, Xining

doi:10.1007/s00170-016-9638-5

Cited by 31 publications

(13 citation statements)

References 33 publications

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“…Path planning for mobile robots was done successfully [42]. Multiobjective design optimization was also investigated with various case studies by multiobjective cuckoo search [30], and the fixture layout of metal parts was optimized by cuckoo search [79].…”

Section: Design Optimizationmentioning

confidence: 99%

Cuckoo search: State-of-the-art and opportunities

Yang

Deb

2017

2017 IEEE 4th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

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Section: Design Optimizationmentioning

confidence: 99%

Cuckoo search: State-of-the-art and opportunities

Yang

Deb

2017

2017 IEEE 4th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

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“…Sundararaman et al [12] optimized the layout of the xture element combined the surface response method, which modeled the relationship between the maximum workpiece deformation and the locator position, and evolutionary techniques including GA and PSO methods. Yang et al [13] also optimized the locating layout of the xture element. The method is based on cuckoo search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Thin floor milling using moving support

Fei

Lin

Huang

et al. 2022

Int J Adv Manuf Technol

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“…The workpiece behavior under mechanical load was also calculated with FEM. In [50], the Cuckoo algorithm was utilized to determine an optimal clamping configuration. The commercial software OptiS-Lang in combination with ANSYS made it possible to optimize the design parameters for the development machine tools using regression analysis, in order to save time, personnel capacities and the corresponding costs [51].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a clamping concept based on machine learning

Feng

Maier

Stehle

et al. 2021

Prod. Eng. Res. Devel.

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Fixtures are an important element of the manufacturing system, as they ensure productive and accurate machining of differently shaped workpieces. Regarding the fixture design or the layout of fixture elements, a high static and dynamic stiffness of fixtures is therefore required to ensure the defined position and orientation of workpieces under process loads, e.g. cutting forces. Nowadays, with the increase in computing performance and the development of new algorithms, machine learning (ML) offers an appropriate possibility to use regression methods for creating realistic, rapid and reliable equivalent ML models instead of simulations based on the finite element method (FEM). This research work introduces a novel method that allows an optimization of clamping concepts and fixture design by means of ML, in order to reduce manufacturing errors and to obtain an increased stiffness of fixtures and machining accuracy. This paper describes the preparation of a dataset for training ML models, the systematic selection of the most promising regression algorithm based on relevant criteria, the implementation of the chosen algorithm Extreme Gradient Boosting (XGBoost) and other comparable algorithms, the analysis of their regression results, and the validation of the optimization for a selected clamping concept.

show abstract

Optimum fixture locating layout for sheet metal part by integrating kriging with cuckoo search algorithm

Cited by 31 publications

References 33 publications

Cuckoo search: State-of-the-art and opportunities

Cuckoo search: State-of-the-art and opportunities

Thin floor milling using moving support

Optimization of a clamping concept based on machine learning

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