Machine learning-based prediction of pitting corrosion resistance in stainless steels exposed to chloride environments

Qiao, Chunyu; Luo, Hong; Wang, Xuefei; Cheng, Hongxu; Bi, Da; Li, Xiaogang

doi:10.1016/j.colsurfa.2023.132274

Cited by 10 publications

(1 citation statement)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Manufacturing experts and researchers have been giving a lot of attention lately to machine learning (ML) algorithms for modeling and process optimization [20][21][22]. With the procedure of machine learning (ML) algorithms, complex relationships between input factors and output responses can be efficiently seized, agreeing for exact prediction-making and the identification of primary processes [23][24][25]. The machine learning algorithms are used for the development of models that predicts the output values of AWJ drilling of Inconel 718 superalloy coated with YSZ and also to determine the best set of process parameters to attain the desired goals [26,27].…”

Section: Introductionmentioning

confidence: 99%

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

Siva Kumar,

Nagarajan,

Vasudevan

2024

Eng. Res. Express

View full text Add to dashboard Cite

The purpose of this research is to ascertain the optimal abrasive waterjet drilling parameters for making holes on Inconel 718 superalloy coated with yttrium-stabilized zirconia, namely waterjet pressure (JP), standoff distance (SD), abrasive flow rate (FR), and angle of impact (AI). The study explores the predictive modeling of the entry diameter (DN) and exit diameter (DX) of the drilled holes using an amalgamation of experimental analysis, response optimization and machine learning approaches. Eight different ML techniques are used to predict DN and DX. Better values of DN=1.31352 mm and DX=1.00515 mm are obtained through Random Forest for the setting of JP=175 MPa, FR=250 g/min, SD=1.45455 mm, and AI=0.909091 deg. Further, the tuning of hyperparameters of Random Forest algorithm is performed to study the improvement in measuring DN and DX. The least value of total absolute error=0.468 is observed while finding the DN and DX using Random Forest algorithm than the traditional response optimization method with reference to the confirmation test results. The work provides important insights for enhancing the machinability of YSZ-coated Inconel 718 superalloy utilizing the AWJ drilling process by bridging the gap between manufacturing research, machine learning, and real-world applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

Siva Kumar,

Nagarajan,

Vasudevan

2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization

Jin,

Luo,

Wang

et al. 2024

Materials Genome Engineering Advances

Self Cite

View full text Add to dashboard Cite

This article proposed a design strategy that integrated machine learning models based on random forest and genetic algorithm (GA) for the rapid screening of hardness in the AlCoCrCuFeMoNiTi high‐entropy alloys system. Through feature engineering and modeling, valence electron concentration, atomic size difference (δr), Pauling electronegativity difference (Δχ), geometric parameters (Λ), and the Cr content were identified as the five key features in the database. The GA was employed to search for alloys with superior hardness and guided synthesis. After three iterations, the HEA Al18Co21Cr23Fe23Mo15 exhibiting the highest predicted hardness (868.8 HV) was identified. The alloy was predominantly composed of BCC, ordered B2, and σ phases, with an experimental hardness of 899.8 ± 9.9 HV, which as approximately 5.38% greater than the maximum hardness observed in the original dataset. The design strategy can also solve other regression problems and pave the way for optimizing material performance in various engineering applications.

show abstract

Machine Learning-Based Prediction of Intergranular Corrosion Resistance in Austenitic Stainless Steels Exposed to Various Heat Treatments

Sheik,

Mohammed,

Teeparthi

et al. 2024

J. Inst. Eng. India Ser. D

View full text Add to dashboard Cite

Machine learning-based prediction of pitting corrosion resistance in stainless steels exposed to chloride environments

Cited by 10 publications

References 78 publications

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

Data mining accelerated the design strategy of high‐entropy alloys with the largest hardness based on genetic algorithm optimization

Machine Learning-Based Prediction of Intergranular Corrosion Resistance in Austenitic Stainless Steels Exposed to Various Heat Treatments

Contact Info

Product

Resources

About