Solving the Inverse Heat Conduction Problem in Using Long Square Pulse Thermography to Estimate Coating Thickness by Using SVR Models Based on Restored Pseudo Heat Flux (RPHF) In-Plane Profile

Wang, Hongjin; Hsieh, Sheng‐Jen

doi:10.1007/s10921-018-0535-8

Cited by 9 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where ω ∈ R is the weight vector, and b is the threshold. To include more model samples within the boundary conditions, the tolerance deviation ε and slack variables ξ i ≥ 0, ξ * i ≥ 0 are introduced [18]:…”

Section: Support Vector Regressionmentioning

confidence: 99%

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

et al. 2022

View full text Add to dashboard Cite

Particle gluing operating parameters in particleboard (PB) production have an important influence on the mechanical properties of PBs. This study developed a multi-objective optimization model based on support vector regression (SVR) optimized by the non-dominated sorted genetic algorithm-II (NSGA2) to realize the multi-objective accurate prediction of PB mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bonding (IB) strength) by adjusting particle gluing operating parameters. The NSGA2-SVR multi-objective prediction model was trained by 496 groups of experimental data of particle gluing operating parameters and PB mechanical properties. The prediction results of the NSGA2-SVR multi-objective prediction model were evaluated by 124 groups of experimental data and compared with the prediction results of the back propagation neural network (BPNN) model, general regression neural network (GRNN) model, and SVR model. The mean absolute percentage errors (MAPEs) of the NSGA2-SVR model were 49.11%, 33.64%, and 24.20% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The Theil’s inequality coefficients (TICs) of the NSGA2-SVR model were 40.93%, 27.39%, and 18.58% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The results showed that the multi-objective prediction model based on NSGA2-SVR has a superior fitting and higher prediction accuracy for the prediction performance of particle gluing operating parameters, and the NSGA2-SVR model can be applied to the multi-objective synchronous prediction of particle gluing operating parameters in the PB production line.

show abstract

Section: Support Vector Regressionmentioning

confidence: 99%

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In contrast, Wang et.al. determined coating thicknesses without knowledge about the actual value of the thermal diffusivity by using a calibration data set for an artificial intelligence approach [5]. Since porosity strongly influence thermal properties [6] some recent research work was focused on the consideration of heat propagation by means of transit times of virtual waves [7].…”

Section: Introductionmentioning

confidence: 99%

Determination of Thermal Parameters of Concrete by Active Thermographic Measurements

2022

View full text Add to dashboard Cite

The knowledge of the thermal parameters of a particular concrete is essential for thermal design of a building, but also could help to identify and assess the state of a concrete structure. Active thermography has the potential to be applied onsite and to provide a fast investigation of thermal properties. In this work, three different concrete samples were investigated by active thermography in reflection and in transmission setup. It was found that this method yields the same results without direct contact as the Transient Plane Source (TPS) method as an established inspection tool.

show abstract

One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Zhang,

Xu,

Liu

et al. 2024

Composites Part B: Engineering

View full text Add to dashboard Cite

Solving the Inverse Heat Conduction Problem in Using Long Square Pulse Thermography to Estimate Coating Thickness by Using SVR Models Based on Restored Pseudo Heat Flux (RPHF) In-Plane Profile

Cited by 9 publications

References 35 publications

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

Determination of Thermal Parameters of Concrete by Active Thermographic Measurements

One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Contact Info

Product

Resources

About