Wongpromrat, Patthranit

Loading...
Thumbnail Image
Preferred name
Wongpromrat, Patthranit
Main Affiliation
Email
patthranit.wo@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
    (2025-12-01)
    Chawuthai, Rathachai
    ;
    Promchan, Teeratat
    ;
    Rojsanga, Jularak
    ;
    Chandra-ambhorn, Somrerk
    ;
    Nilsonthi, Thanasak
    Material degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Monitoring of salinity of water on the THA CHIN River basin using portable Vis-NIR spectrometer combined with machine learning algorithms
    (2023-09-05)
    Wongpromrat, Patthranit
    ;
    Phuphanutada, Jirawat
    ;
    Lapcharoensuk, Ravipat
    The goal of this work is to study the alternative practices for monitoring the salinity of water using a combination of portable vis-NIR spectrometer and machine learning approachs. Along 80 km of the Tha Chin River basin from the Gulf of Thailand, the data of salinity and NIR spectrum were collected during winter and summer seasons of Thailand. Salinity of water samples was measured by using a handheld electrical conductivity meter and NIR spectra was recorded with portable FQA-NIR GUN in the wavelength range of 600 to 1100 nm. The 10 machine learning models including partial least square regression (PLS), support vector machine (SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), bagging meta-estimator (BME), extremely randomized trees (ERT), backpropagation neural networks (BPNN) and hybrid principal component analysis-neural network (PC[sbnd]NN) were applied to train the NIRs models for predicting salinity. All machine learning algorithms showed good prediction results which R<inf>p</inf><sup>2</sup> values were higher than 0.84. The models built by tree-based algorithms (DT, RF, AB, GB, BME and ERT) displayed higher performances of calibration set and prediction set than those of PLS, SVM, BPNN and PC[sbnd]NN. Among these, the ERT algorithm showed the best performance R<inf>p</inf><sup>2</sup> of 0.97, RMSEP of 0.41 g/L and RPD of 6.00. It was shown that NIR spectroscopy coupled with machine learning could be an alternative simpler way for predicting salinity of water.