- Home
- /
- Angsumalin Senjuntichai
Assoc. Prof. Angsumalin Senjuntichai, D.Eng.
- 6th Floor of Engineering 4 Bldg., Room 611
- +66-2218-6827
- angsumalin.s@chula.ac.th
Education
D.Eng Industrial Engineering
Asian Institute of Technology, Thailand, 2012
M.S.I.E. Industrial Engineering
University of Minnesota, United States, 1999
B.Eng. Industrial Engineering
Chulalongkorn University, Thailand, 1994
Chulalongkorn University, Thailand, 1994
Expertise
Economics & Financial Engineering
Statistics & Data Analysis
Publications
2025
Barami Phulsawat, Teerapong Senjuntichai, Angsumalin Senjuntichai, Wichirat Kaewjuea
Data-Driven Solutions for Backcalculating Elastic Moduli of Flexible Pavements from FWD Test Journal Article
In: Engineering Journal, vol. 29, no. 3, pp. 27 – 44, 2025, ISSN: 01258281, (Cited by: 0).
@article{Phulsawat202527,
title = {Data-Driven Solutions for Backcalculating Elastic Moduli of Flexible Pavements from FWD Test},
author = {Barami Phulsawat and Teerapong Senjuntichai and Angsumalin Senjuntichai and Wichirat Kaewjuea},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002421417&doi=10.4186%2fej.2025.29.3.27&partnerID=40&md5=a4ea276d6851bef476543747d306014a},
doi = {10.4186/ej.2025.29.3.27},
issn = {01258281},
year = {2025},
date = {2025-01-01},
journal = {Engineering Journal},
volume = {29},
number = {3},
pages = {27 – 44},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Traditional methods for calculating pavement layers elastic moduli from falling weight deflectometer (FWD) tests often rely on computationally intensive iterative processes and lack struggle to capture complex variable relationships. This article highlights the utilization of machine learning (ML) algorithms, which include artificial neural networks (ANN), long-short-term memory (LSTM), and random forests (RF), to predict the elastic moduli of multi-layered flexible pavement based on FWD test. All ML algorithms were developed using synthetic databases derived from the exact stiffness matrix scheme, which was employed for the analysis of multi-layered pavements under axisymmetric surface loading. The development of ML models involves preprocessing of data, hyperparameter optimization, and performance evaluation. The input variables consist of the FWD surface deflections, the magnitude of applied loading, and the layer thicknesses, while the output variables represent the predicted layered elastic moduli of the pavement structure. The ANN and LSTM models capture complicated relations more effectively than the RF model in the backcalculation of the layered elastic modulus based on the FWD test. Among the two, LSTM achieves higher accuracy, with the average values across all layer moduli of R2 and MAPE being 99.04% and 2.41%, respectively, in the test set. The applicability of LSTM model is further demonstrated by comparing with the backcalculated elastic modulus based on the FWD field experiments performed on the infrastructure of roads in Thailand. Furthermore, a sensitivity analysis reveals that deflections near the center of loading predominantly impact the predictions of upper layer moduli, while the moduli of lower layers are influenced by deflections across all geophones. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Traditional methods for calculating pavement layers elastic moduli from falling weight deflectometer (FWD) tests often rely on computationally intensive iterative processes and lack struggle to capture complex variable relationships. This article highlights the utilization of machine learning (ML) algorithms, which include artificial neural networks (ANN), long-short-term memory (LSTM), and random forests (RF), to predict the elastic moduli of multi-layered flexible pavement based on FWD test. All ML algorithms were developed using synthetic databases derived from the exact stiffness matrix scheme, which was employed for the analysis of multi-layered pavements under axisymmetric surface loading. The development of ML models involves preprocessing of data, hyperparameter optimization, and performance evaluation. The input variables consist of the FWD surface deflections, the magnitude of applied loading, and the layer thicknesses, while the output variables represent the predicted layered elastic moduli of the pavement structure. The ANN and LSTM models capture complicated relations more effectively than the RF model in the backcalculation of the layered elastic modulus based on the FWD test. Among the two, LSTM achieves higher accuracy, with the average values across all layer moduli of R2 and MAPE being 99.04% and 2.41%, respectively, in the test set. The applicability of LSTM model is further demonstrated by comparing with the backcalculated elastic modulus based on the FWD field experiments performed on the infrastructure of roads in Thailand. Furthermore, a sensitivity analysis reveals that deflections near the center of loading predominantly impact the predictions of upper layer moduli, while the moduli of lower layers are influenced by deflections across all geophones. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Kongtawan Sangjinda, Suraparb Keawsawasvong, Pitthaya Jamsawang, Angsumalin Senjuntichai, Teerapong Senjuntichai
Optimized ANN-based surrogate models for evaluating the stability of trapdoors in Hoek‒Brown rock masses Journal Article
In: Earth Science Informatics, vol. 18, no. 1, 2025, ISSN: 18650473, (Cited by: 4).
@article{Sangjinda2025,
title = {Optimized ANN-based surrogate models for evaluating the stability of trapdoors in Hoek‒Brown rock masses},
author = {Kongtawan Sangjinda and Suraparb Keawsawasvong and Pitthaya Jamsawang and Angsumalin Senjuntichai and Teerapong Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213036933&doi=10.1007%2fs12145-024-01550-w&partnerID=40&md5=bf614b214226b941490f5886137f6df3},
doi = {10.1007/s12145-024-01550-w},
issn = {18650473},
year = {2025},
date = {2025-01-01},
journal = {Earth Science Informatics},
volume = {18},
number = {1},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {One of the main concerns of underground engineering works, such as subsurface structures and mining in rock formations, is ensuring their safety. The objective of this work is to present the stability analysis of trapdoors in Hoek-Brown (HB) rock masses, and to propose an innovative soft-computing approach utilizing optimized ANN-based surrogate models for evaluating the stability of trapdoors. The stability factor serves as a key parameter in formulating both lower bound (LB) and upper bound (UB) solutions for two-dimensional trapdoor through the finite element limit analysis (FELA). Furthermore, this paper introduces hybrid machine learning models that integrate artificial neural networks (ANNs) with diverse optimization algorithms (OAs), such as the ant lion optimizer (ALO), imperialist competitive algorithm (ICA), shuffled complex evolution algorithm (SCE), and teaching learning-based optimization (TLBO). Rigorous optimization ensures the accuracy and efficiency of these models in capturing the intricate dynamics of stability investigation. The performance of the proposed models is rigorously evaluated using metrics, convergence curves, regression plot, Taylor diagram, and rank analysis. Consequently, The ANN-SCE model achieved the highest performance (Testing Set), with R2 of 0.9630, MAE of 2.7416, RMSE of 0.3696, VAF(%) of 96.2834, IOS of 0.0269, and RSR of 0.0172, respectively. These results demonstrate the accuracy and efficiency of the proposed models in capturing the complex dynamics of stability investigations. This research provides practical tools for engineers to assess road stability, plan mitigation for sinkholes, and account for rock strength using the Hoek-Brown criterion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.},
note = {Cited by: 4},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
One of the main concerns of underground engineering works, such as subsurface structures and mining in rock formations, is ensuring their safety. The objective of this work is to present the stability analysis of trapdoors in Hoek-Brown (HB) rock masses, and to propose an innovative soft-computing approach utilizing optimized ANN-based surrogate models for evaluating the stability of trapdoors. The stability factor serves as a key parameter in formulating both lower bound (LB) and upper bound (UB) solutions for two-dimensional trapdoor through the finite element limit analysis (FELA). Furthermore, this paper introduces hybrid machine learning models that integrate artificial neural networks (ANNs) with diverse optimization algorithms (OAs), such as the ant lion optimizer (ALO), imperialist competitive algorithm (ICA), shuffled complex evolution algorithm (SCE), and teaching learning-based optimization (TLBO). Rigorous optimization ensures the accuracy and efficiency of these models in capturing the intricate dynamics of stability investigation. The performance of the proposed models is rigorously evaluated using metrics, convergence curves, regression plot, Taylor diagram, and rank analysis. Consequently, The ANN-SCE model achieved the highest performance (Testing Set), with R2 of 0.9630, MAE of 2.7416, RMSE of 0.3696, VAF(%) of 96.2834, IOS of 0.0269, and RSR of 0.0172, respectively. These results demonstrate the accuracy and efficiency of the proposed models in capturing the complex dynamics of stability investigations. This research provides practical tools for engineers to assess road stability, plan mitigation for sinkholes, and account for rock strength using the Hoek-Brown criterion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Kittiphan Yoonirundorn, Teerapong Senjuntichai, Angsumalin Senjuntichai, Suraparb Keawsawasvong
Predicting Bearing Capacity Factors of Multiple Shallow Foundations Using Finite Element Limit Analysis and Machine Learning Approaches Journal Article
In: Transportation Infrastructure Geotechnology, vol. 12, no. 3, 2025, ISSN: 21967202, (Cited by: 0).
@article{Yoonirundorn2025,
title = {Predicting Bearing Capacity Factors of Multiple Shallow Foundations Using Finite Element Limit Analysis and Machine Learning Approaches},
author = {Kittiphan Yoonirundorn and Teerapong Senjuntichai and Angsumalin Senjuntichai and Suraparb Keawsawasvong},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219550535&doi=10.1007%2fs40515-025-00560-5&partnerID=40&md5=f64787ca0b73879af42609a3a7481b38},
doi = {10.1007/s40515-025-00560-5},
issn = {21967202},
year = {2025},
date = {2025-01-01},
journal = {Transportation Infrastructure Geotechnology},
volume = {12},
number = {3},
publisher = {Springer},
abstract = {This study presents the prediction of bearing capacity factors for multiple square shallow foundations in cohesive-frictional soils, utilizing finite element limit analysis (FELA), and machine learning (ML) techniques. The footings are considered to be of equal spacing s, and constant width B. Results from FELA, based on upper and lower bound theorems, were presented in dimensionless charts, showing the correlation between three bearing capacity factors (Nc, Nq, and Nγ), the angle of internal friction (ϕ), and the spacing ratio (S/B). ML techniques, namely ANN and XGBoost, were employed to estimate bearing capacity factors using ϕ and S/B as inputs. The developed models were assessed against FELA data through various metrics, with both ML models showing good agreement with FELA. Among the two models, XGBoost demonstrates slightly higher consistency with FELA data, with R2 values exceeding 99.9% across all datasets. Besides, a feature importance analysis identified the friction angle as the dominant parameter with permutation importance of more than 85% in the estimation of three bearing capacity factors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study presents the prediction of bearing capacity factors for multiple square shallow foundations in cohesive-frictional soils, utilizing finite element limit analysis (FELA), and machine learning (ML) techniques. The footings are considered to be of equal spacing s, and constant width B. Results from FELA, based on upper and lower bound theorems, were presented in dimensionless charts, showing the correlation between three bearing capacity factors (Nc, Nq, and Nγ), the angle of internal friction (ϕ), and the spacing ratio (S/B). ML techniques, namely ANN and XGBoost, were employed to estimate bearing capacity factors using ϕ and S/B as inputs. The developed models were assessed against FELA data through various metrics, with both ML models showing good agreement with FELA. Among the two models, XGBoost demonstrates slightly higher consistency with FELA data, with R2 values exceeding 99.9% across all datasets. Besides, a feature importance analysis identified the friction angle as the dominant parameter with permutation importance of more than 85% in the estimation of three bearing capacity factors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
2024
Barami Phulsawat, Angsumalin Senjuntichai, Teerapong Senjuntichai
Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches Journal Article
In: Transportation Infrastructure Geotechnology, 2024, (Cited by: 0).
@article{Phulsawat2024,
title = {Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches},
author = {Barami Phulsawat and Angsumalin Senjuntichai and Teerapong Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185673039&doi=10.1007%2fs40515-024-00370-1&partnerID=40&md5=6732e8acf05eae16ff2b915458a8fbf9},
doi = {10.1007/s40515-024-00370-1},
year = {2024},
date = {2024-01-01},
journal = {Transportation Infrastructure Geotechnology},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Barami Phulsawat, Angsumalin Senjuntichai, Teerapong Senjuntichai
Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches Journal Article
In: Transportation Infrastructure Geotechnology, vol. 11, no. 4, pp. 2348 – 2381, 2024, ISSN: 21967202, (Cited by: 8).
@article{Phulsawat20242348,
title = {Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches},
author = {Barami Phulsawat and Angsumalin Senjuntichai and Teerapong Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185673039&doi=10.1007%2fs40515-024-00370-1&partnerID=40&md5=6732e8acf05eae16ff2b915458a8fbf9},
doi = {10.1007/s40515-024-00370-1},
issn = {21967202},
year = {2024},
date = {2024-01-01},
journal = {Transportation Infrastructure Geotechnology},
volume = {11},
number = {4},
pages = {2348 – 2381},
publisher = {Springer},
abstract = {The application of supervised machine learning algorithms to provide solutions for various civil engineering problems is an emerging trend. This paper presents the utilization of artificial neural network (ANN) and random forest regression (RFR) for the prediction of the elastic moduli of multi-layered pavement based on the falling weight deflectometer (FWD) test. The establishment of ML models includes data preprocessing, hyperparameter optimization, and performance evaluations. The ML models are created from both ANN and RFR techniques using 122,500 datasets from a theoretical model of the FWD test, generated by employing an exact stiffness matrix method for the analysis of multi-layered flexible pavement. The performance measures of both ML models, developed from the synthetic dataset, indicate that the output variables (the predicted pavement moduli) are precisely explained by the input parameters (the measured surface displacements). Both ML solutions are then compared with the FWD test results performed on the road infrastructures in Thailand, showing good agreement with the predicted moduli from the FWD tests. Between the two ML solutions, RFR displays better accuracy in predicting the pavement moduli from the FWD tests with the R2 values of the predicted elastic moduli exceeding 90%. Besides, a sensitivity analysis is carried out to illustrate the impact of surface deflections recorded at each geophone on the predicted pavement moduli. The present study demonstrates the efficacy of ML techniques in assessing road infrastructures and highlights the significance of sensitivity analysis in enhancing the accuracy of pavement performance prediction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.},
note = {Cited by: 8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The application of supervised machine learning algorithms to provide solutions for various civil engineering problems is an emerging trend. This paper presents the utilization of artificial neural network (ANN) and random forest regression (RFR) for the prediction of the elastic moduli of multi-layered pavement based on the falling weight deflectometer (FWD) test. The establishment of ML models includes data preprocessing, hyperparameter optimization, and performance evaluations. The ML models are created from both ANN and RFR techniques using 122,500 datasets from a theoretical model of the FWD test, generated by employing an exact stiffness matrix method for the analysis of multi-layered flexible pavement. The performance measures of both ML models, developed from the synthetic dataset, indicate that the output variables (the predicted pavement moduli) are precisely explained by the input parameters (the measured surface displacements). Both ML solutions are then compared with the FWD test results performed on the road infrastructures in Thailand, showing good agreement with the predicted moduli from the FWD tests. Between the two ML solutions, RFR displays better accuracy in predicting the pavement moduli from the FWD tests with the R2 values of the predicted elastic moduli exceeding 90%. Besides, a sensitivity analysis is carried out to illustrate the impact of surface deflections recorded at each geophone on the predicted pavement moduli. The present study demonstrates the efficacy of ML techniques in assessing road infrastructures and highlights the significance of sensitivity analysis in enhancing the accuracy of pavement performance prediction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
2021
Chawa-at Intarapalit, Angsumalin Senjuntichai
Process yields improvement of filter presses in rabies immunoglobulin production Journal Article
In: Engineering Journal, vol. 25, no. 3, pp. 95 – 103, 2021, ISSN: 01258281, (Cited by: 1; All Open Access, Gold Open Access, Green Open Access).
@article{Intarapalit202195,
title = {Process yields improvement of filter presses in rabies immunoglobulin production},
author = {Chawa-at Intarapalit and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105402392&doi=10.4186%2fej.2021.25.3.95&partnerID=40&md5=795ad4c7ea0ae0942160d93e825dc833},
doi = {10.4186/ej.2021.25.3.95},
issn = {01258281},
year = {2021},
date = {2021-01-01},
journal = {Engineering Journal},
volume = {25},
number = {3},
pages = {95 – 103},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {The objective of this study is to improve the performance of the Equine Rabies Immunoglobulin (ERIG) process by using the caprylic acid as a single precipitated agent and the replacement plan for the filter press machine to support higher demand in ERIG. The experiments based on the face-center central composite design are performed to investigate the relationship of yield recovery with 1. the concentration of caprylic acid used in the purification process, 2. the flow rate, and 3. the pore size of the filter press machine used in the filtration process. The regression analysis shows no relationship on linear, quadratic, or interactions between yield recovery and the three factors. Fortunately, according to the Analysis of Variance (for comparing means), there is a significant effect of interaction between the flow rate and pore size and the main effect of concentration of caprylic acid on the yield recovery at a 90% confidence level. From the interaction plot, at a flow rate of 10.5 ml/s with a filter media pore size of 6-15 micron, the process has the maximum yield recovery of 15.5% that is significantly superior to those of the other two sizes of filter media at the same flow rate. With a flow rate of 16.8 ml/s, the yield recovery is not significantly different for any pore size. At a flow rate of 4.2 ml/s, the yield recovery is higher when using the pore size of 4-9 and 6-15 micron than those from 5-12 micron. Nevertheless, with a 90% confidence interval for average yield recovery by Tukey comparison, the average yield recovery received from 16.8 ml/s at every pore size and 4.2 ml/s at 4-9 micron are not statistically different. Therefore, with the current size, 5-12 micron, of filter media used in the current process, the flow rate is suggested to be 16.8 ml/s whereas, the caprylic acid concentration of 1% is preferred due to the highest yield recovery compared to other concentration levels and the lowest production cost. © 2021, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 1; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The objective of this study is to improve the performance of the Equine Rabies Immunoglobulin (ERIG) process by using the caprylic acid as a single precipitated agent and the replacement plan for the filter press machine to support higher demand in ERIG. The experiments based on the face-center central composite design are performed to investigate the relationship of yield recovery with 1. the concentration of caprylic acid used in the purification process, 2. the flow rate, and 3. the pore size of the filter press machine used in the filtration process. The regression analysis shows no relationship on linear, quadratic, or interactions between yield recovery and the three factors. Fortunately, according to the Analysis of Variance (for comparing means), there is a significant effect of interaction between the flow rate and pore size and the main effect of concentration of caprylic acid on the yield recovery at a 90% confidence level. From the interaction plot, at a flow rate of 10.5 ml/s with a filter media pore size of 6-15 micron, the process has the maximum yield recovery of 15.5% that is significantly superior to those of the other two sizes of filter media at the same flow rate. With a flow rate of 16.8 ml/s, the yield recovery is not significantly different for any pore size. At a flow rate of 4.2 ml/s, the yield recovery is higher when using the pore size of 4-9 and 6-15 micron than those from 5-12 micron. Nevertheless, with a 90% confidence interval for average yield recovery by Tukey comparison, the average yield recovery received from 16.8 ml/s at every pore size and 4.2 ml/s at 4-9 micron are not statistically different. Therefore, with the current size, 5-12 micron, of filter media used in the current process, the flow rate is suggested to be 16.8 ml/s whereas, the caprylic acid concentration of 1% is preferred due to the highest yield recovery compared to other concentration levels and the lowest production cost. © 2021, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Issada Manohorratad, Angsumalin Senjuntichai
A Study on the Single Caprylic Acid Fractionation and Centrifugal Separation of Equine Rabies Immunoglobulin Journal Article
In: Engineering Journal, vol. 25, no. 11, pp. 33 – 43, 2021, ISSN: 01258281, (Cited by: 0; All Open Access, Gold Open Access, Green Open Access).
@article{Manohorratad202133,
title = {A Study on the Single Caprylic Acid Fractionation and Centrifugal Separation of Equine Rabies Immunoglobulin},
author = {Issada Manohorratad and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123002433&doi=10.4186%2fej.2021.25.11.33&partnerID=40&md5=4f0eb34fcbf6b22b73d50f2b486b6420},
doi = {10.4186/ej.2021.25.11.33},
issn = {01258281},
year = {2021},
date = {2021-01-01},
journal = {Engineering Journal},
volume = {25},
number = {11},
pages = {33 – 43},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This study proposes alternative caprylic acid precipitation and centrifugal separation for the equine rabies Immunoglobulin manufacturing process. The objective is to determine the optimal setting associated with the centrifugal machine and the optimal amount of caprylic acid for the maximum process yield (%). The experiments were designed based on the central composite design and performed to analyze the relationship of three factors which are the caprylic acid (1%-5%V/V), the rotation speed (7,500-12,500 rpm), and centrifugal time (20-40 min) on the yield of the process. For the first time, the prediction model as a second-degree polynomial regression is presented and developed by a response surface method (RSM) with R2 approximately 51%. RSM model also reveals that the process yield is affected by the concentration of caprylic acid and the amount of time to centrifuge the precipitated plasma but not by the rotation speed of the centrifugal machine. With the predicted process yield of about 12.97%, the optimal setting by RSM suggests the concentration of caprylic acid at 2.82% and the centrifugal time at 28 minutes. © 2021, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study proposes alternative caprylic acid precipitation and centrifugal separation for the equine rabies Immunoglobulin manufacturing process. The objective is to determine the optimal setting associated with the centrifugal machine and the optimal amount of caprylic acid for the maximum process yield (%). The experiments were designed based on the central composite design and performed to analyze the relationship of three factors which are the caprylic acid (1%-5%V/V), the rotation speed (7,500-12,500 rpm), and centrifugal time (20-40 min) on the yield of the process. For the first time, the prediction model as a second-degree polynomial regression is presented and developed by a response surface method (RSM) with R2 approximately 51%. RSM model also reveals that the process yield is affected by the concentration of caprylic acid and the amount of time to centrifuge the precipitated plasma but not by the rotation speed of the centrifugal machine. With the predicted process yield of about 12.97%, the optimal setting by RSM suggests the concentration of caprylic acid at 2.82% and the centrifugal time at 28 minutes. © 2021, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
2020
Somkiat Tangjitsitcharoen, Jettanong Klaewsongkram, Angsumalin Senjuntichai, Supranee Buranapraditkun, Sutthinee Thongnop
Development of prototype kit for portable drug allergy testing Conference
vol. 51, Elsevier B.V., 2020, ISSN: 23519789, (Cited by: 2; All Open Access, Gold Open Access).
@conference{Tangjitsitcharoen2020975,
title = {Development of prototype kit for portable drug allergy testing},
author = {Somkiat Tangjitsitcharoen and Jettanong Klaewsongkram and Angsumalin Senjuntichai and Supranee Buranapraditkun and Sutthinee Thongnop},
editor = {Vosniakos G.-C. and Pellicciari M. and Benardos P. and Markopoulos A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099817824&doi=10.1016%2fj.promfg.2020.10.137&partnerID=40&md5=d04cb1664fd61bb9d8993e165fd19327},
doi = {10.1016/j.promfg.2020.10.137},
issn = {23519789},
year = {2020},
date = {2020-01-01},
journal = {Procedia Manufacturing},
volume = {51},
pages = {975 – 980},
publisher = {Elsevier B.V.},
abstract = {The prototype kit for portable drug allergy testing is proposed and developed for easy-to-use in provincial hospitals where the standard equipment for in vitro drug allergy diagnosis is still lacking. The Enzyme-Linked Immunosorbent Spot (ELISpot) technique is adopted for this purpose due to its high sensitivity and specificity to detect cytokine-releasing cells, which is precise and suitable for non-instantaneously allergic tests. A developed prototype for drug allergy testing has been considered in various aspects of the three main designs, comprising of the proposed testing procedure kit, the heat generating system to control temperature, and the prototype structure. All parts and prototype kits are modeled by using 3D printing technology. The electrical circuit system developed and employed in the prototype kit could generate the heat to control the required temperature between 36.5 °C and 37.5 °C, which is the optimum temperature for cull culture. Compared to the standard equipment, our portable prototype kit yielded good diagnostic values (80% sensitivity, 100% specificity, and 90% accuracy), which are satisfactory and acceptable as a practical tool to identify the culprit drugs. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.},
note = {Cited by: 2; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The prototype kit for portable drug allergy testing is proposed and developed for easy-to-use in provincial hospitals where the standard equipment for in vitro drug allergy diagnosis is still lacking. The Enzyme-Linked Immunosorbent Spot (ELISpot) technique is adopted for this purpose due to its high sensitivity and specificity to detect cytokine-releasing cells, which is precise and suitable for non-instantaneously allergic tests. A developed prototype for drug allergy testing has been considered in various aspects of the three main designs, comprising of the proposed testing procedure kit, the heat generating system to control temperature, and the prototype structure. All parts and prototype kits are modeled by using 3D printing technology. The electrical circuit system developed and employed in the prototype kit could generate the heat to control the required temperature between 36.5 °C and 37.5 °C, which is the optimum temperature for cull culture. Compared to the standard equipment, our portable prototype kit yielded good diagnostic values (80% sensitivity, 100% specificity, and 90% accuracy), which are satisfactory and acceptable as a practical tool to identify the culprit drugs. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.
2015
Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
In-process chatter detection in surface grinding Conference
vol. 28, EDP Sciences, 2015, ISSN: 2261236X, (Cited by: 3; All Open Access, Gold Open Access, Green Open Access).
@conference{Tangjitsitcharoen2015,
title = {In-process chatter detection in surface grinding},
author = {Somkiat Tangjitsitcharoen and Angsumalin Senjuntichai},
editor = {Wei-Hsin L. and Zhihua G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976497319&doi=10.1051%2fmatecconf%2f20152802001&partnerID=40&md5=b49a536729195784b773cff398a696da},
doi = {10.1051/matecconf/20152802001},
issn = {2261236X},
year = {2015},
date = {2015-01-01},
journal = {MATEC Web of Conferences},
volume = {28},
publisher = {EDP Sciences},
abstract = {The chatter causes the poor surface finish during the surface grinding. It is therefore necessary to monitor the chatter during the process. Hence, this research has proposed the in-process chatter detection in the surface grinding process by utilizing the dynamic cutting forces. The ratios of the average variances of three dynamic cutting forces have been adopted and applied to identify the chatter during the surface grinding process to eliminate the effects of the cutting conditions. The effects of the cutting conditions on the chatter are also studied and analyzed. The algorithm has been proposed to detect the chatter regardless of the cutting conditions. The verification of the proposed system has been proved through another experiment by using the new cutting conditions. The experimental results have run satisfaction. It is understood that the chatter can be avoided during the in-process surface grinding even though the cutting conditions are changed. © Owned by the authors, published by EDP Sciences, 2015.},
note = {Cited by: 3; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The chatter causes the poor surface finish during the surface grinding. It is therefore necessary to monitor the chatter during the process. Hence, this research has proposed the in-process chatter detection in the surface grinding process by utilizing the dynamic cutting forces. The ratios of the average variances of three dynamic cutting forces have been adopted and applied to identify the chatter during the surface grinding process to eliminate the effects of the cutting conditions. The effects of the cutting conditions on the chatter are also studied and analyzed. The algorithm has been proposed to detect the chatter regardless of the cutting conditions. The verification of the proposed system has been proved through another experiment by using the new cutting conditions. The experimental results have run satisfaction. It is understood that the chatter can be avoided during the in-process surface grinding even though the cutting conditions are changed. © Owned by the authors, published by EDP Sciences, 2015.
2014
Kulpiya Seri, Senjuntichai Angsumalin
Package chip defect reduction on integrated circuit Journal Article
In: Applied Mechanics and Materials, vol. 462-463, pp. 578 – 584, 2014, ISSN: 16627482, (Cited by: 2).
@article{Seri2014578,
title = {Package chip defect reduction on integrated circuit},
author = {Kulpiya Seri and Senjuntichai Angsumalin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891047743&doi=10.4028%2fwww.scientific.net%2fAMM.462-463.578&partnerID=40&md5=278e7d02001fc74656907ee61110b88a},
doi = {10.4028/www.scientific.net/AMM.462-463.578},
issn = {16627482},
year = {2014},
date = {2014-01-01},
journal = {Applied Mechanics and Materials},
volume = {462-463},
pages = {578 – 584},
abstract = {This research applies Six Sigma approach in order to reduce defect by increasing the assembly process capability index (Cpk) of Integrated Circuit (IC) production process. This study applies five phases (DMAIC) of Six Sigma approach beginning with define (D), measure (M), analyze (A), improve (I) and control (C) phases, respectively. The response of the research identified in the define phase is the chipped width with Cpk of 0.66 determined from the measure phase. The half-factorial experiments are implemented in the analyze phase to find the significant factors which are water temperature, water pressure and feed rate. In improve phase, the additioanl expriments are performed according to the Box-Behnken design in order to determine the non-linear relation between the chipped width and all mentioned factors. The optimal setting of each factors are determined by applied the response surface optimizer. Under the optimal setting, the control charts are used in the control phase to monitor the chipped width. The resulted Cpk of the response is increased to 1.39 which is greater than the one-sided accpetable process capability of 1.25. © (2014) Trans Tech Publicutions, Switzerland.},
note = {Cited by: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This research applies Six Sigma approach in order to reduce defect by increasing the assembly process capability index (Cpk) of Integrated Circuit (IC) production process. This study applies five phases (DMAIC) of Six Sigma approach beginning with define (D), measure (M), analyze (A), improve (I) and control (C) phases, respectively. The response of the research identified in the define phase is the chipped width with Cpk of 0.66 determined from the measure phase. The half-factorial experiments are implemented in the analyze phase to find the significant factors which are water temperature, water pressure and feed rate. In improve phase, the additioanl expriments are performed according to the Box-Behnken design in order to determine the non-linear relation between the chipped width and all mentioned factors. The optimal setting of each factors are determined by applied the response surface optimizer. Under the optimal setting, the control charts are used in the control phase to monitor the chipped width. The resulted Cpk of the response is increased to 1.39 which is greater than the one-sided accpetable process capability of 1.25. © (2014) Trans Tech Publicutions, Switzerland.