A Thermal–Elastic–Plastic Constitutive Model using the Radial Basis Function Neural Network and Application for an Energy Efficient Warm Forming Process

Park, Soo-Hyun; Lee, Eun‐Ho; Choi, Heeyoul; Lee, Jae Young

doi:10.1007/s12541-020-00460-3

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The aforementioned literature indicates that ANN is an efficient service provider selection model. The RBFNN is one of the most popular ANN methods, due to its convenient and fast training with accurate performance [35]. It has also been successfully applied to the problem of service provider selection.…”

Section: Artificial Neural Network Application In Service Provider Se...mentioning

confidence: 99%

Service Provider Portfolio Selection across the Project Life Cycle Considering Synergy Effect

Zhang,

Zheng,

et al. 2023

Buildings

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During the project life cycle, selecting the appropriate service provider portfolio (SPP) is essential to guaranteeing the successful implementation of manufacturing tasks. However, the existence of the synergy effect among service providers poses a challenge for decision makers in selecting the most suitable SPP. To effectively address this challenge, this study constructs a novel service provider portfolio selection (SPPS) model across the project life cycle, considering the synergy effect. The model is based on the integration of a radial basis function neural network (RBFNN), the technique for order preference by similarity to ideal solution (TOPSIS), and the entropy method (EM). First, the evaluation criteria for service provider selection are defined, followed by the identification of alternative service providers and feasible SPPs based on project life cycle division. Subsequently, a quantitative analysis of the synergy effect among service providers within the same stage, as well as between different stages, throughout the project life cycle, is carried out. This analysis helps to determine the input variables and expected output variables that will be utilized in the model. Additionally, the feasibility and applicability of the proposed model are illustrated through an example. Finally, a comparison between the proposed hybrid model and the BPNN is conducted to validate the model’s accuracy and efficiency. This study contributes to making sound decisions in the SPPS process from a project life cycle perspective.

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Section: Artificial Neural Network Application In Service Provider Se...mentioning

confidence: 99%

Service Provider Portfolio Selection across the Project Life Cycle Considering Synergy Effect

Zhang,

Zheng,

et al. 2023

Buildings

View full text Add to dashboard Cite

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A Review: Double Multivariate Nonlinear Regression Constitutive Model of High-Speed Train Axle Steel 40CrNiMo during Hot Tension

Jia

Huo

et al. 2022

JOM

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Specimen Size Effect on Behavior of Mg–3Al–1Zn Magnesium Alloy in Macro to Micro-scale Deformation

Khandani,

Faraji,

Karimpour

2023

Int. J. Precis. Eng. Manuf.

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A Thermal–Elastic–Plastic Constitutive Model using the Radial Basis Function Neural Network and Application for an Energy Efficient Warm Forming Process

Cited by 5 publications

References 32 publications

Service Provider Portfolio Selection across the Project Life Cycle Considering Synergy Effect

Service Provider Portfolio Selection across the Project Life Cycle Considering Synergy Effect

A Review: Double Multivariate Nonlinear Regression Constitutive Model of High-Speed Train Axle Steel 40CrNiMo during Hot Tension

Specimen Size Effect on Behavior of Mg–3Al–1Zn Magnesium Alloy in Macro to Micro-scale Deformation

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