Heteroscedastic sparse Gaussian process regression-based stochastic material model for plastic structural analysis

Chen, Baixi; Shen, Luming; Zhang, Hao

doi:10.1038/s41598-022-06870-9

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…Therefore, the Gaussian regression model was deemed highly suitable for the data set collected. GP has also become a research hot spot in the field of machine learning and been successfully applied in many fields [ 20 , 21 , 22 , 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials

Wang,

Lu,

Chen

et al. 2023

Nanomaterials

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Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson’s correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error.

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Section: Methodsmentioning

confidence: 99%

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials

Wang,

Lu,

Chen

et al. 2023

Nanomaterials

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“…Graf et al (2012) used the fuzzy neural network to describe the uncertain stress-strain trends successfully based on the material data. Chen et al (2022aChen et al ( , 2021Chen et al ( , 2023) introduced a Bayesian-based ML algorithm, that is, the Gaussian process regression (GPR) model, for quantifying the material uncertainty directly from the experimental data. They achieved good application in both the metal and the rock.…”

Section: Data Availabilitymentioning

confidence: 99%

Manufacturing-induced stochastic constitutive behaviors of additive manufactured specimens: testing, data-driven modeling, and optimization

Chen,

Mao,

Lin

et al. 2024

RPJ

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Purpose Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process. Design/methodology/approach By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters. Findings As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R2 over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments. Practical implications The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies. Originality/value Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

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