Double-machine-learning-based data-driven stochastic flow stress model for aluminium alloys at elevated temperatures

Chen, Baixi; Yang, Yibo; Yang, Lei; Li, Wei

doi:10.1016/j.mtcomm.2022.104506

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…Recent advances in machine learning (ML) and data-driven approaches provide a promising way to describe the material behaviors (Yang et al, 2023, Guo et al, 2024 and quantify the associated uncertain (Chen et al, 2022b) from experimental datasets. Compared with traditional methods, the data-driven approach is more efficient and direct.…”

Section: Data Availabilitymentioning

confidence: 99%

“…Compared with traditional methods, the data-driven approach is more efficient and direct. The double ML-based framework proposed by Chen et al (2022b) accurately captured the stochastic flow stress behaviors of the aluminum alloys at rising temperatures. Graf et al (2012) used the fuzzy neural network to describe the uncertain stress-strain trends successfully based on the material data.…”

Section: Data Availabilitymentioning

confidence: 99%

“…, 2023, Guo et al. , 2024) and quantify the associated uncertain (Chen et al. , 2022b) from experimental datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with traditional methods, the data-driven approach is more efficient and direct. The double ML-based framework proposed by Chen et al. (2022b) accurately captured the stochastic flow stress behaviors of the aluminum alloys at rising temperatures.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Data Availabilitymentioning

confidence: 99%

Section: Data Availabilitymentioning

confidence: 99%

“…, 2023, Guo et al. , 2024) and quantify the associated uncertain (Chen et al. , 2022b) from experimental datasets.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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