Laser cutting path optimization using simulated annealing with an adaptive large neighborhood search

Hajad, Makbul; Tangwarodomnukun, Viboon; Jaturanonda, Chorkaew; Dumkum, Chaiya

doi:10.1007/s00170-019-03569-6

Cited by 35 publications

(10 citation statements)

References 28 publications

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“…The ASA algorithm is well suited for solving highly nonlinear problems with short running analysis codes, when finding the global optimum is more important than a quick improvement in the design. Theoretically, the simulated annealing algorithm can always find the global optimal solution of a problem [ 31 ].…”

Section: Experimental Results Analysis Of X12 Ferritic Heat-resistant Steelmentioning

confidence: 99%

Identification of the Constitutive Model Parameters by Inverse Optimization Method and Characterization of Hot Deformation Behavior for Ultra-Supercritical Rotor Steel

Chen

et al. 2021

Materials

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X12 (X12CrMoWVNbN10-1-1) ferritic heat resistant steel is an important material for the production of new-generation ultra-supercritical generator rotors. Hot compression tests of X12 ferritic heat-resistant steel were performed via a Gleeble-1500D testing machine under temperatures of 1050–1250 °C and strain rates of 0.05–5 s−1. In order to provide material model data for finite element simulations and accurately predict the hot deformation behavior, a reverse optimization method was proposed to construct elevated temperature constitutive models of X12 ferritic heat-resistant steel in this paper, according to the Hansel–Spittel constitutive model. To verify the accuracy of the model, the predicted and experimental values of the constitutive model were compared. The results indicated that the model had a high prediction accuracy. Meanwhile, the correlation coefficient between the experimental value and the predicted value of constitutive model was 0.97833. For further verification of the accuracy of the model, it was implemented in finite element FORGE@ software to simulate the compression tests of different samples under different conditions. Comparing actual displacement–load curves with displacement–load curves acquired through finite element simulations, the results indicated that displacement–load curves predicted by the model were very consistent with actual displacement–load curves, which verified the accuracy of the model. Moreover, to research the optimal processing parameters of the material, hot processing maps were drawn according to the dynamic material model. In terms of microstructure evolution, a characteristic area distribution map of the hot processing map was established. Therefore, the optimal hot forming parameters regions were in the range of 1150–1200 °C/0.05–0.62 s−1 for X12 ferritic heat-resistant steel.

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Section: Experimental Results Analysis Of X12 Ferritic Heat-resistant Steelmentioning

confidence: 99%

Identification of the Constitutive Model Parameters by Inverse Optimization Method and Characterization of Hot Deformation Behavior for Ultra-Supercritical Rotor Steel

Chen

et al. 2021

Materials

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“…The following 25 articles were excluded because the weight adjustment mechanism was not described in detail: [9,10,17,20,29,36,40,41,44,45,47,57,61,65,77,86,90,106,112,116,117,119,122,130,132]. An additional 5 articles [1,22,92,98,133] were excluded because the adaptive layer cannot be removed by setting a parameter to a certain value, and [58] was excluded as a roulette wheel principle is not employed (rather, a heuristic with the highest weight is chosen).…”

Section: Identification Selection and Main Properties Of Studiesmentioning

confidence: 99%

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Turkeš

Sörensen

Hvattum

2021

European Journal of Operational Research

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Research on metaheuristics has focused almost exclusively on (novel) algorithmic development and on competitive testing, both of which have been frequently argued to yield very little generalizable knowledge. The main goal of this paper is to promote meta-analysis-a systematic statistical examination that combines the results of several independent studies-as a more suitable way to obtain problem-and implementation-independent insights on metaheuristics. Meta-analysis is widely used in several scienti c domains, most notably the medical sciences (e.g., to establish the e cacy of a certain treatment). To the best of our knowledge this is the rst meta-analysis in the eld of metaheuristics.

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“…Therefore, the SA algorithm is very suitable for the temperature compensation process of the MEMS gyroscope. The SA algorithm is as follows [34]:…”

Section: Sa-elm Compensation Modelmentioning

confidence: 99%

A Parallel Denoising Model for Dual-Mass MEMS Gyroscope Based on PE-ITD and SA-ELM

Cao

et al. 2019

IEEE Access

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In a bid to solve the gyroscope temperature drift problem, a parallel denoising model based on PE-ITD and SA-ELM has been proposed in this paper, wherein, Intrinsic timescale decomposition (ITD) is an effective signal decomposition algorithm, Permutation entropy (PE) is a entropy to accurately determine the complexity of the signal, Extreme Learning machine (ELM) is a machine learning algorithm for predicting, and Simulated Annealing(SA) for finding the optimal parameter set. First, ITD is employed to decompose the output signal of the gyroscope and the PE can distinguish the decomposition results into noise-only component, mixed component and trend component. Secondly, the mixed component is filtered by Forward liner prediction (FLP), the denoised signal can be obtained after processing. Finally, the trend component is compensated by SA-ELM, through reconstruction, the compensation result can be obtained. As is shown in experimental results, the parallel model proposed in this paper is able to effectively eliminate the temperature error compared with the traditional serial model. Compared with the EMD analysis method, the ITD is superior to the EMD analysis method in terms of computational efficiency and instantaneous information. The proposed SA-ELM algorithm takes simulated annealing algorithm to find hidden layer neurons' optimal number adaptively, which can improve the model's accuracy and generalization. This paper demonstrates the superiority of this method.

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Laser cutting path optimization using simulated annealing with an adaptive large neighborhood search

Cited by 35 publications

References 28 publications

Identification of the Constitutive Model Parameters by Inverse Optimization Method and Characterization of Hot Deformation Behavior for Ultra-Supercritical Rotor Steel

Identification of the Constitutive Model Parameters by Inverse Optimization Method and Characterization of Hot Deformation Behavior for Ultra-Supercritical Rotor Steel

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

A Parallel Denoising Model for Dual-Mass MEMS Gyroscope Based on PE-ITD and SA-ELM

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