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- สมเกียรติ ตั้งจิตสิตเจริญ
ศ. ดร.สมเกียรติ ตั้งจิตสิตเจริญ
- 8th Floor of Engineering 4 Bldg., Room 802
- +66-2218-6853
- somkiat.ta@eng.chula.ac.th
Overview
Professor Dr. Somkiat Tangjitsitcharoen is currently Head of Advanced Manufacturing and Precision Engineering Research Center at the Industrial Engineering, Chulalongkorn University, Thailand. His research interests include in-process monitoring and optimization of manufacturing processes, micro-machining and micro-assembly, high precision cutting, and intelligent manufacturing system and machine tool.
Education
D.Eng. Mechanical Engineering
Kobe University, Japan, 2004
M.Eng. Industrial Engineering
Chulalongkorn University, Thailand, 1998
B.Eng. Production Engineering
King Mongkut’s Universtiy of Technology Thonburi, Thailand, 1995
Expertise
Manufacturing & Service Systems
Publications
2025
Ravee Bunduwongse, Somkiat Tangjitsitcharoen
Virtual Reality Application of Lathe Machine Training Journal Article
In: Engineering Journal, vol. 29, no. 3, pp. 59 – 77, 2025, ISSN: 01258281, (Cited by: 0).
@article{Bunduwongse202559,
title = {Virtual Reality Application of Lathe Machine Training},
author = {Ravee Bunduwongse and Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002127229&doi=10.4186%2fej.2025.29.3.59&partnerID=40&md5=99ba0d3a45a5c12a7860fa072cc2736e},
doi = {10.4186/ej.2025.29.3.59},
issn = {01258281},
year = {2025},
date = {2025-01-01},
journal = {Engineering Journal},
volume = {29},
number = {3},
pages = {59 – 77},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This study aims to utilize Virtual Reality (VR) technology to develop an industrial training application with content focusing on Machining operation using lathe machine. The focus of the study is to present the content being taught in manufacturing laboratory classes about lathe machining operations in the virtual world. Machining operations demonstrated within this application includes turning, facing, chamfering, cutoff, internal and external threading. The user journey is designed to guide users through the content tailored aimed to familiarize them with the order of operation and safety guidelines. This educational tool further provides the freedom of the user to repeat the process without restriction to their location, time, or resource, as opposed to conventional hands-on laboratory. The study involves the research of the laboratory content, development planning, assets creation, functional design and implementation, and integration with Salesforce platform as learning assessment database tool. This study emphasizes future content expansion and is therefore developed with modularity in mind to best promote future content enrichment. The application resulted in the ability to represent a total of 5 machining operations. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study aims to utilize Virtual Reality (VR) technology to develop an industrial training application with content focusing on Machining operation using lathe machine. The focus of the study is to present the content being taught in manufacturing laboratory classes about lathe machining operations in the virtual world. Machining operations demonstrated within this application includes turning, facing, chamfering, cutoff, internal and external threading. The user journey is designed to guide users through the content tailored aimed to familiarize them with the order of operation and safety guidelines. This educational tool further provides the freedom of the user to repeat the process without restriction to their location, time, or resource, as opposed to conventional hands-on laboratory. The study involves the research of the laboratory content, development planning, assets creation, functional design and implementation, and integration with Salesforce platform as learning assessment database tool. This study emphasizes future content expansion and is therefore developed with modularity in mind to best promote future content enrichment. The application resulted in the ability to represent a total of 5 machining operations. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Somkiat Tangjitsitcharoen, Jettanong Klaewsongkram
DEVELOPMENT OF ENUMERATOR ON ANDROID PLATFORM FOR COUNTING ELISPOT Conference
Association for Computing Machinery, 2025, ISBN: 979-840071792-5, (Cited by: 0).
@conference{Tangjitsitcharoen2025180,
title = {DEVELOPMENT OF ENUMERATOR ON ANDROID PLATFORM FOR COUNTING ELISPOT},
author = {Somkiat Tangjitsitcharoen and Jettanong Klaewsongkram},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011694640&doi=10.1145%2f3719384.3719410&partnerID=40&md5=6c0dc845ce603c1321b61c29f6dec4c2},
doi = {10.1145/3719384.3719410},
isbn = {979-840071792-5},
year = {2025},
date = {2025-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {180 – 188},
publisher = {Association for Computing Machinery},
abstract = {The objective of this research is to create an object-detecting Android application for ELISpot immunoassay reading. The ability to accurately, quickly, and cheaply conduct immunoassay tests will guarantee that public healthcare industries are operating and helping patients in rural areas effectively. The past reliance on purchased laboratory equipment, and the rampant cost of health services has led to the privatization of healthcare industries and the negligence of rural area healthcare facilities. Therefore, this research is intended to help spread ELISpot test capability to smaller less-funded programs by creating an accessible ELISpot reading system in the form of an application and the hardware required. Object detection solutions present 2 issues; computational intensity and image quality. An Android application containing the enumeration algorithm was also designed. In order to implement object detection capability on mobile devices, the parameters used were extracted from a Faster R-CNN neural network model trained from a set of immunoassay images provided by the laboratory. The annotation of the images was done using LabelImage, whose output of .xml was converted to .csv and tfrecord files respectively. The computational accuracy tested over the samples averages out at 98% accuracy. While other design parameters such as image processing speed, system weight, and process throughput time are all satisfactory. With the project completed successfully, it is believed that with the upcoming technologies of deep learning super sampling (DLSS) With this kept in mind, true deep learning capable object detection applications operated on a mobile device will soon become more widespread and accessible. © 2024 Copyright held by the owner/author(s).},
note = {Cited by: 0},
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pubstate = {published},
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The objective of this research is to create an object-detecting Android application for ELISpot immunoassay reading. The ability to accurately, quickly, and cheaply conduct immunoassay tests will guarantee that public healthcare industries are operating and helping patients in rural areas effectively. The past reliance on purchased laboratory equipment, and the rampant cost of health services has led to the privatization of healthcare industries and the negligence of rural area healthcare facilities. Therefore, this research is intended to help spread ELISpot test capability to smaller less-funded programs by creating an accessible ELISpot reading system in the form of an application and the hardware required. Object detection solutions present 2 issues; computational intensity and image quality. An Android application containing the enumeration algorithm was also designed. In order to implement object detection capability on mobile devices, the parameters used were extracted from a Faster R-CNN neural network model trained from a set of immunoassay images provided by the laboratory. The annotation of the images was done using LabelImage, whose output of .xml was converted to .csv and tfrecord files respectively. The computational accuracy tested over the samples averages out at 98% accuracy. While other design parameters such as image processing speed, system weight, and process throughput time are all satisfactory. With the project completed successfully, it is believed that with the upcoming technologies of deep learning super sampling (DLSS) With this kept in mind, true deep learning capable object detection applications operated on a mobile device will soon become more widespread and accessible. © 2024 Copyright held by the owner/author(s).
Somkiat Tangjitsitcharoen, Nattawut Suksomcheewin, Alessio Faccia
Leveraging Intelligent Machines for Sustainable and Intelligent Manufacturing Systems Journal Article
In: Journal of Manufacturing and Materials Processing, vol. 9, no. 5, 2025, ISSN: 25044494, (Cited by: 0).
@article{Tangjitsitcharoen2025,
title = {Leveraging Intelligent Machines for Sustainable and Intelligent Manufacturing Systems},
author = {Somkiat Tangjitsitcharoen and Nattawut Suksomcheewin and Alessio Faccia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006619808&doi=10.3390%2fjmmp9050153&partnerID=40&md5=a336e5d0ce219d6adf44fa05c12921de},
doi = {10.3390/jmmp9050153},
issn = {25044494},
year = {2025},
date = {2025-01-01},
journal = {Journal of Manufacturing and Materials Processing},
volume = {9},
number = {5},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {This study presents an intelligent machine developed for real-time quality monitoring during CNC turning, aimed at improving cutting efficiency and reducing production energy. A dynamometer integrated into the CNC machine captures decomposed cutting forces using the Daubechies wavelet transform. These force ratios are correlated with key workpiece dimensions: surface roughness, average roughness, straightness, and roundness. Two predictive models—nonlinear regression and a feed-forward neural network with Levenberg–Marquardt backpropagation—are employed to estimate these parameters under varying cutting conditions. Experimental results indicate that nonlinear regression models outperform neural networks in predictive accuracy. The proposed system offers effective in-process control of machining quality, contributing to shorter cycle times, lower defect rates, and more sustainable manufacturing practices. © 2025 by the authors.},
note = {Cited by: 0},
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pubstate = {published},
tppubtype = {article}
}
This study presents an intelligent machine developed for real-time quality monitoring during CNC turning, aimed at improving cutting efficiency and reducing production energy. A dynamometer integrated into the CNC machine captures decomposed cutting forces using the Daubechies wavelet transform. These force ratios are correlated with key workpiece dimensions: surface roughness, average roughness, straightness, and roundness. Two predictive models—nonlinear regression and a feed-forward neural network with Levenberg–Marquardt backpropagation—are employed to estimate these parameters under varying cutting conditions. Experimental results indicate that nonlinear regression models outperform neural networks in predictive accuracy. The proposed system offers effective in-process control of machining quality, contributing to shorter cycle times, lower defect rates, and more sustainable manufacturing practices. © 2025 by the authors.
2024
Somkiat Tangjitsitcharoen
Intelligent monitoring of tool wear and quality control of roughness with roundness in CNC turning Journal Article
In: International Journal of Advanced Manufacturing Technology, vol. 135, no. 5-6, pp. 2337 – 2354, 2024, ISSN: 02683768, (Cited by: 3).
@article{Tangjitsitcharoen20242337,
title = {Intelligent monitoring of tool wear and quality control of roughness with roundness in CNC turning},
author = {Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207317983&doi=10.1007%2fs00170-024-14650-0&partnerID=40&md5=7a7c829610fd4e580fc9f093ece27009},
doi = {10.1007/s00170-024-14650-0},
issn = {02683768},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Advanced Manufacturing Technology},
volume = {135},
number = {5-6},
pages = {2337 – 2354},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This paper integrates an in-process monitoring of tool wear with quality control of roughness and roundness of machined parts in CNC turning, which are the critical parameters. The Daubechies wavelet transform has been applied to decompose the dynamic cutting forces in order to classify the signals of the tool wear, the average surface roughness, the surface roughness, and the roundness. The decomposed cutting forces are normalized to predict those parameters regardless of cutting conditions by taking the ratio of decomposed cutting forces. Firstly, the ratio of average variance of decomposed feed force to that of decomposed main force is proposed to estimate the tool wear. Secondly, the areas of those decomposed feed force and decomposed main force have been proposed and taken into the ratio to forecast the average surface roughness and the surface roughness concurrently. Lastly, the roundness error is calculated simultaneously by utilizing the ratio of average variance of decomposed radial force to that of decomposed feed force. The exponential function is adopted to represent the relations of the tool wear, the average surface roughness, the surface roughness, the roundness, and all proposed ratios of decomposed cutting forces, respectively. The new cutting tests are conducted to verify all obtained models. The experimentally obtained results showed that the tool wear declines as the ratio of average variance of decomposed feed force to that of decomposed main force increases. The average surface roughness and the surface roughness tend to decrease while the ratio of decomposed feed force to decomposed main force progresses. On the other hand, the roundness error becomes larger when the ratio of average variance of decomposed radial force to that of decomposed feed force escalates. It is concluded that the tool wear, the average surface roughness, the surface roughness, and the roundness can be monitored and controlled at the same time during the in-process CNC turning, which has never been done and developed before. The highest benefit of the proposed system can enhance the quality of machined parts and lead to higher productivity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper integrates an in-process monitoring of tool wear with quality control of roughness and roundness of machined parts in CNC turning, which are the critical parameters. The Daubechies wavelet transform has been applied to decompose the dynamic cutting forces in order to classify the signals of the tool wear, the average surface roughness, the surface roughness, and the roundness. The decomposed cutting forces are normalized to predict those parameters regardless of cutting conditions by taking the ratio of decomposed cutting forces. Firstly, the ratio of average variance of decomposed feed force to that of decomposed main force is proposed to estimate the tool wear. Secondly, the areas of those decomposed feed force and decomposed main force have been proposed and taken into the ratio to forecast the average surface roughness and the surface roughness concurrently. Lastly, the roundness error is calculated simultaneously by utilizing the ratio of average variance of decomposed radial force to that of decomposed feed force. The exponential function is adopted to represent the relations of the tool wear, the average surface roughness, the surface roughness, the roundness, and all proposed ratios of decomposed cutting forces, respectively. The new cutting tests are conducted to verify all obtained models. The experimentally obtained results showed that the tool wear declines as the ratio of average variance of decomposed feed force to that of decomposed main force increases. The average surface roughness and the surface roughness tend to decrease while the ratio of decomposed feed force to decomposed main force progresses. On the other hand, the roundness error becomes larger when the ratio of average variance of decomposed radial force to that of decomposed feed force escalates. It is concluded that the tool wear, the average surface roughness, the surface roughness, and the roundness can be monitored and controlled at the same time during the in-process CNC turning, which has never been done and developed before. The highest benefit of the proposed system can enhance the quality of machined parts and lead to higher productivity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Somkiat Tangjitsitcharoen, Nattawut Suksomcheewin
Association for Computing Machinery, 2024, ISBN: 979-840071691-1, (Cited by: 0).
@conference{Tangjitsitcharoen20249,
title = {Intelligent Monitoring of Surface Roughness and Straightness with Roundness on CNC Turning Utilizing Wavelet Transform via Neural Networks},
author = {Somkiat Tangjitsitcharoen and Nattawut Suksomcheewin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212394425&doi=10.1145%2f3690063.3690066&partnerID=40&md5=f91cf6f0e6e9fb4654f3f3f02b39720e},
doi = {10.1145/3690063.3690066},
isbn = {979-840071691-1},
year = {2024},
date = {2024-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {9 – 17},
publisher = {Association for Computing Machinery},
abstract = {This paper presents an advanced work of in-process monitoring and quality control of machining parts of author. The in-process monitoring system has been proposed by utilizing the Daubechies wavelet transform to decompose the dynamic cutting forces and control the machining quality, which are the critical parameters such as the surface roughness, the average roughness, the straightness, and the roundness. The dynamic cutting forces have been monitored by employing the dynamometer. The decomposed cutting forces are analyzed and identified which correspond to those parameters in frequency domain. The ratios of decomposed cutting forces are hence proposed to predict the surface roughness, the average roughness, the straightness, and the roundness. The feed-forward neural network with backpropagation algorithm is utilized to calculate those parameters simultaneously. Hence, the machining quality can be controlled within the required specification. Finally, the new cutting conditions are applied to check the proposed method and the prediction accuracy of those parameters. The experimental results showed that the developed system can be used to estimate and control those parameters during the in-process turning. © 2024 Copyright held by the owner/author(s).},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper presents an advanced work of in-process monitoring and quality control of machining parts of author. The in-process monitoring system has been proposed by utilizing the Daubechies wavelet transform to decompose the dynamic cutting forces and control the machining quality, which are the critical parameters such as the surface roughness, the average roughness, the straightness, and the roundness. The dynamic cutting forces have been monitored by employing the dynamometer. The decomposed cutting forces are analyzed and identified which correspond to those parameters in frequency domain. The ratios of decomposed cutting forces are hence proposed to predict the surface roughness, the average roughness, the straightness, and the roundness. The feed-forward neural network with backpropagation algorithm is utilized to calculate those parameters simultaneously. Hence, the machining quality can be controlled within the required specification. Finally, the new cutting conditions are applied to check the proposed method and the prediction accuracy of those parameters. The experimental results showed that the developed system can be used to estimate and control those parameters during the in-process turning. © 2024 Copyright held by the owner/author(s).
Pakorn Sangiamrat, Somkiat Tangjitsitcharoen
Design of a Chatbot with Artificial Intelligence System-Assisted Drug Allergy Diagnosis Conference
Association for Computing Machinery, 2024, ISBN: 979-840071820-5, (Cited by: 0).
@conference{Sangiamrat202445,
title = {Design of a Chatbot with Artificial Intelligence System-Assisted Drug Allergy Diagnosis},
author = {Pakorn Sangiamrat and Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207061485&doi=10.1145%2f3695220.3695225&partnerID=40&md5=7ca3e20a00b8e4b3d0e9764b7d93a2ec},
doi = {10.1145/3695220.3695225},
isbn = {979-840071820-5},
year = {2024},
date = {2024-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {45 – 51},
publisher = {Association for Computing Machinery},
abstract = {This study addresses the impact of the COVID-19 pandemic on health care, including hospital congestion and an overload of medical staff. This study proposes a system that uses a chatbot with artificial intelligence to assist in the initial classification of drug allergy in patients. The chatbot would allow general users to input images to chat and wait to categorize drug allergy. Along with testing and gathering information to make sure of a correct diagnosis. Following that, we will collect additional details for future diagnostics. Patients with mild allergic reactions do not need to be hospitalized. Chatbot systems can help with the initial drug allergy screen and can follow instructions. This study investigates the design of an efficient chatbot and uses convolutional neural networks for image classification. The results show that the investigators' new set-up procedure for initial drug allergy screening and CNN model validation works, which means that this work could be used as a model for future research and development. Artificial intelligence is becoming increasingly significant in medical technology. However, there are recommendations to further enhance data set preparation and bot responses. This research leads to opportunities for future work in drug allergy diagnostics utilizing breakthrough technologies that support the entrance of the AI era. © 2024 Owner/Author.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This study addresses the impact of the COVID-19 pandemic on health care, including hospital congestion and an overload of medical staff. This study proposes a system that uses a chatbot with artificial intelligence to assist in the initial classification of drug allergy in patients. The chatbot would allow general users to input images to chat and wait to categorize drug allergy. Along with testing and gathering information to make sure of a correct diagnosis. Following that, we will collect additional details for future diagnostics. Patients with mild allergic reactions do not need to be hospitalized. Chatbot systems can help with the initial drug allergy screen and can follow instructions. This study investigates the design of an efficient chatbot and uses convolutional neural networks for image classification. The results show that the investigators’ new set-up procedure for initial drug allergy screening and CNN model validation works, which means that this work could be used as a model for future research and development. Artificial intelligence is becoming increasingly significant in medical technology. However, there are recommendations to further enhance data set preparation and bot responses. This research leads to opportunities for future work in drug allergy diagnostics utilizing breakthrough technologies that support the entrance of the AI era. © 2024 Owner/Author.
Somkiat Tangjitsitcharoen, Naris Lawankowit
Virtual Reality of Labless Foundry and Heat Treatment for Next Industrial Training Journal Article
In: Engineering Journal, vol. 28, no. 6, pp. 1 – 23, 2024, ISSN: 01258281, (Cited by: 2; All Open Access, Gold Open Access).
@article{Tangjitsitcharoen20241,
title = {Virtual Reality of Labless Foundry and Heat Treatment for Next Industrial Training},
author = {Somkiat Tangjitsitcharoen and Naris Lawankowit},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199634018&doi=10.4186%2fej.2024.28.6.1&partnerID=40&md5=e61239f5c3ad34aa9b14632faf2d6f8f},
doi = {10.4186/ej.2024.28.6.1},
issn = {01258281},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {6},
pages = {1 – 23},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This study presents an innovative Virtual Reality (VR) application tailored for educating users on Foundry and Heat Treatment operations. Its primary objectives encompass the creation of a secure and lifelike learning environment while ensuring accessibility. The development process was a meticulous fusion of technology, incorporating the Unity engine, 3D modeling, and seamless Salesforce integration, all grounded in extensive research. The VR application's structure strategically subdivides these industrial processes into four stations: Molding, Furnace, Workbench, and Heat Treatment, with each station's execution steps comprehensively outlined in corresponding tables. This detailed approach empowers users to engage directly in these operations, making it a valuable educational tool. The study's significance extends to the realm of virtual education, particularly within fields necessitating hands-on experience. The VR application offers a standardized, accessible, and immersive means of learning, transcending geographical constraints. Its adaptability for future enhancements, such as task execution accuracy improvements and scoring system configuration, positions it as a dynamic and enduring solution within the domain of virtual education. The application's 90% accuracy in simulating these operations further underscores its efficacy and reliability. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 2; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study presents an innovative Virtual Reality (VR) application tailored for educating users on Foundry and Heat Treatment operations. Its primary objectives encompass the creation of a secure and lifelike learning environment while ensuring accessibility. The development process was a meticulous fusion of technology, incorporating the Unity engine, 3D modeling, and seamless Salesforce integration, all grounded in extensive research. The VR application’s structure strategically subdivides these industrial processes into four stations: Molding, Furnace, Workbench, and Heat Treatment, with each station’s execution steps comprehensively outlined in corresponding tables. This detailed approach empowers users to engage directly in these operations, making it a valuable educational tool. The study’s significance extends to the realm of virtual education, particularly within fields necessitating hands-on experience. The VR application offers a standardized, accessible, and immersive means of learning, transcending geographical constraints. Its adaptability for future enhancements, such as task execution accuracy improvements and scoring system configuration, positions it as a dynamic and enduring solution within the domain of virtual education. The application’s 90% accuracy in simulating these operations further underscores its efficacy and reliability. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Somkiat Tangjitsitcharoen
Development of Virtual CNC Turning Application Journal Article
In: Smart Innovation, Systems and Technologies, vol. 382, pp. 151 – 162, 2024, ISSN: 21903018, (Cited by: 0).
@article{Tangjitsitcharoen2024151,
title = {Development of Virtual CNC Turning Application},
author = {Somkiat Tangjitsitcharoen},
editor = {Nakamatsu K. and Patnaik S. and Kountchev R.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190677459&doi=10.1007%2f978-981-99-9018-4_11&partnerID=40&md5=8b0b639ac58fb60142efcdcbb96dc2ab},
doi = {10.1007/978-981-99-9018-4_11},
issn = {21903018},
year = {2024},
date = {2024-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {382},
pages = {151 – 162},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This paper presents a development of virtual Computer Numerical Control (CNC) turning for training and replacing the laboratory classes involving the machining processes in order to enhance the user experience in the field of CNC turning. This research utilizes the advantages of the virtual world for the user to interact with CNC turning machine and to practice the CNC turning processes. A development of virtual reality application contains the key academic concepts taught during CNC machining classes. By wearing a virtual reality headset and using handheld controllers, the content available will allow the user to interact with and inspect the workshop environment, which is modeled after the real world, where a series of turning processes and demonstrations will appear to guide the user. The user can interact with objects in the scene freely with the tracking capability enabled by the two handheld controllers. With these valuable assets available, the application of virtual CNC turning is considered a new generation of the education system. The application of virtual CNC turning can be used in the educational field, training scenario, or manufacturing practice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents a development of virtual Computer Numerical Control (CNC) turning for training and replacing the laboratory classes involving the machining processes in order to enhance the user experience in the field of CNC turning. This research utilizes the advantages of the virtual world for the user to interact with CNC turning machine and to practice the CNC turning processes. A development of virtual reality application contains the key academic concepts taught during CNC machining classes. By wearing a virtual reality headset and using handheld controllers, the content available will allow the user to interact with and inspect the workshop environment, which is modeled after the real world, where a series of turning processes and demonstrations will appear to guide the user. The user can interact with objects in the scene freely with the tracking capability enabled by the two handheld controllers. With these valuable assets available, the application of virtual CNC turning is considered a new generation of the education system. The application of virtual CNC turning can be used in the educational field, training scenario, or manufacturing practice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
2020
Somkiat Tangjitsitcharoen
vol. 51, Elsevier B.V., 2020, ISSN: 23519789, (Cited by: 4; All Open Access, Gold Open Access).
@conference{Tangjitsitcharoen2020222,
title = {Comparison of neural networks and regression analysis to predict in-process straightness in CNC turning},
author = {Somkiat Tangjitsitcharoen},
editor = {Vosniakos G.-C. and Pellicciari M. and Benardos P. and Markopoulos A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099828471&doi=10.1016%2fj.promfg.2020.10.032&partnerID=40&md5=a0500670568d2a7df01a12d78e05219b},
doi = {10.1016/j.promfg.2020.10.032},
issn = {23519789},
year = {2020},
date = {2020-01-01},
journal = {Procedia Manufacturing},
volume = {51},
pages = {222 – 227},
publisher = {Elsevier B.V.},
abstract = {The objective of this research is to predict the in-process straightness of aluminum (Al 6063) and carbon steel (S45C) by monitoring the in-process cutting forces during CNC turning. The Fast Fourier Transform (FFT) is adopted to prove the relation between cutting force and straightness in frequency domain, which appear the same frequency. The cutting force ratio is proposed and normalized to predict the in-process straightness regardless of the cutting conditions. Firstly, the straightness is calculated by employing the two-layer feed-forward neural networks. The Levenberg-Marquardt backpropagation algorithm is utilized to train the system. Secondly, the multiple regression analysis has been applied to model the in-process prediction of straightness and the cutting force ratio under various cutting conditions with the use of least square method at 95% confidence level. Finally, the experimentally obtained results from the neural networks are compared with the ones obtained from the developed straightness model. It had been proved that the in-process straightness can be well predicted under various cutting conditions by using the trained neural networks. © 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: 4; All Open Access, Gold Open Access},
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pubstate = {published},
tppubtype = {conference}
}
The objective of this research is to predict the in-process straightness of aluminum (Al 6063) and carbon steel (S45C) by monitoring the in-process cutting forces during CNC turning. The Fast Fourier Transform (FFT) is adopted to prove the relation between cutting force and straightness in frequency domain, which appear the same frequency. The cutting force ratio is proposed and normalized to predict the in-process straightness regardless of the cutting conditions. Firstly, the straightness is calculated by employing the two-layer feed-forward neural networks. The Levenberg-Marquardt backpropagation algorithm is utilized to train the system. Secondly, the multiple regression analysis has been applied to model the in-process prediction of straightness and the cutting force ratio under various cutting conditions with the use of least square method at 95% confidence level. Finally, the experimentally obtained results from the neural networks are compared with the ones obtained from the developed straightness model. It had been proved that the in-process straightness can be well predicted under various cutting conditions by using the trained neural networks. © 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.
Phonlawut Jaturawichanan, Somkiat Tangjitsitcharoen
Head gimbal assembly automated visual inspection by using digital image processing Conference
vol. 784, no. 1, Institute of Physics Publishing, 2020, ISSN: 17578981, (Cited by: 0; All Open Access, Gold Open Access).
@conference{Jaturawichanan2020,
title = {Head gimbal assembly automated visual inspection by using digital image processing},
author = {Phonlawut Jaturawichanan and Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083211184&doi=10.1088%2f1757-899X%2f784%2f1%2f012033&partnerID=40&md5=ab81f9826d560da904f640febfbf5a15},
doi = {10.1088/1757-899X/784/1/012033},
issn = {17578981},
year = {2020},
date = {2020-01-01},
journal = {IOP Conference Series: Materials Science and Engineering},
volume = {784},
number = {1},
publisher = {Institute of Physics Publishing},
abstract = {This paper presented an approach of Digital Image Processing (DIP) and measurement properties of image region for inspecting the part in Head Gimbal Assembly (HGA) process. Current, the inspection process is performed by the skilled employee, which depends on employee experiences. The inspection method proposed by using digital image processing to do an image enhancement in order to detect the abnormalities and defects on part. The measurement properties of image region is proposed to identify the abnormality occurred on HGA part after image enhancement, in production process. This research aimed to develop the algorithm to inspected HGA part by considered the number of pads, convex area value, and eccentricity value. Then used measurement properties of image region to enhance the algorithm for classifying part in production process. This approach has been developed and proved with the real part in HGA production process. It's proved that the algorithm and method proposed can be classified the part in HGA production process. © Published under licence by IOP Publishing Ltd.},
note = {Cited by: 0; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper presented an approach of Digital Image Processing (DIP) and measurement properties of image region for inspecting the part in Head Gimbal Assembly (HGA) process. Current, the inspection process is performed by the skilled employee, which depends on employee experiences. The inspection method proposed by using digital image processing to do an image enhancement in order to detect the abnormalities and defects on part. The measurement properties of image region is proposed to identify the abnormality occurred on HGA part after image enhancement, in production process. This research aimed to develop the algorithm to inspected HGA part by considered the number of pads, convex area value, and eccentricity value. Then used measurement properties of image region to enhance the algorithm for classifying part in production process. This approach has been developed and proved with the real part in HGA production process. It’s proved that the algorithm and method proposed can be classified the part in HGA production process. © Published under licence by IOP Publishing Ltd.