Yuangyai, Chumpol

Preferred name

Yuangyai, Chumpol

Alternative Name

Yuangyai, Čhumpol

Yuangyai, C.

Main Affiliation

School of Engineering

chumpol.yu@kmitl.ac.th

Now showing 1 - 10 of 16

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Effect of Si on Microstructure and Corrosion Behavior of CoCrMo Alloys
(2018-06-04)
Peaubuapuan, Chonlawit
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Tunthawiroon, Phacharaphon
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Kanlayasiri, Kannachai
;
Yuangyai, Chumpol
The aim of this research was to investigate the effect of silicon (Si) on the microstructure and corrosion behavior of CoCrMo alloys. The concentration of Si added in CoCrMo alloys was 0.1, 0.5 and 1 wt %. The corrosion behavior of the present alloys was investigated using potentiodynamic polarization measurements. The normal saline solution with 0.9 wt % was used as an electrolyte. Polarization curves obtained from the polarization test were used to evaluate in terms of corrosion current density (i<inf>corr</inf> ), corrosion potential (E<inf>corr</inf> ) and corrosion parameters that be used to compute corrosion resistant property of CoCrMo alloys. The microstructure of a sample during the polarization test was compared using X-ray diffraction (XRD) and optical microscope (OM). The results indicated that the increase in Si slightly changed the microstructure of CoCrMo alloys and could enhance the resistance to corrosion of CoCrMo alloys in NaCl solution. In addition, the reduction of Cr and Mo concentration in CoCrMo alloys was found to be a significant influence on the decrease in corrosion resistance.
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Statistical process control with autocorrelated data using neural networks
(2011-10-24)
Yuangyai, Chumpol
;
Abrahams, Rachel
Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build a system as such, artificial neural networks (ANN) are widely used as tools where complex patterns can be difficult to recognize. Many research projects involve using random data patterns for training and recognition of patterns for ANN/SPC applications. However, many manufacturing processes involve autocorrelated data, to determine the effect of autocorrelated data, green sand data was analyzed and a neural network was built and trained to analyze a number of out of control patterns. Overall, the network performed best for detecting larger mean shifts. © 2011 IEEE.
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Multiple-performance optimization for human-robot collaboration: A translation motion in a tele-sandblasting maintenance system
(2016-01-01)
Jientrakul, Ranon
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Yuangyai, Chumpol
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Chotiprayanakul, Pholchai
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Limnararat, Sunpasit
Human-robot collaboration is a vital part of teleoperation systems. Determining the design parameters satisfying the optimal human performance is very important in the design of a teleoperation system for a specific task. Specifically, to thoroughly represent human performance for a task, multiple performance measures in a teleoperation system are evaluated. The experiment was also conducted on a mixed-reality tele-sandblasting system which is setup to study 2D-motion translation in tele-sandblasting task. Then, identifying the design setting to satisfy the optimal measures are difficult due to conflicts among them. Therefore, this paper presents a multipleperformance optimization using a desirability function. The results provide the range of setting condition promoting the optimal human performance measures. © IEOM Society International.
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Variation minimization in tele-sandblasting system: The effect of human-arm movement error
(2018-08-14)
Jientrakul, Ranon
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Attavanish, Pornsak
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Chotiprayanakul, Pholchai
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Limnararat, Sunpasit
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Yuangyai, Chumpol
In tele-sandblasting task, human arm movement is a critical source of producing variation in position of sandblasting nozzle resulting in high operating cost and low productivity. Each operator behaves differently leading to unpredictable movements. Skilled operators are able to reduce the variation; however, developing skills requires a training period. In this paper, we proposed a new approach which is the use of a novel operator's arm movement pattern incorporated with a Kalman filter to reduce the effect of human-arm movement error. A virtual tele-sandblasting system is used to validate our approach. The experimental results verify that our proposed approach is able to significantly reduce the effect of human arm movement error. The approach helps operators to perform the task more comfortably and takes short training time.
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Decentralized Network Building Change in Large Manufacturing Companies towards Industry 4.0
(2017-01-01)
Poonpakdee, P.
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Koiwanit, J.
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Yuangyai, C.
In complex industrial ecosystems together with an increasing global competition, success depends on a complete value chain transformation. The use of Industry 4.0 standards is therefore gradually emerging in many industries to ensure significantly higher factory productivity, flexibility, and efficiency. However, selected methodology and results are required to be studied to fully understand the digital transformation as well as its characteristics. This research presents a system conversion study, from centralized to decentralized systems, using epidemic membership protocols on a large manufacturing company towards Industry 4.0. The system conversion shows that the epidemic membership protocols provide an ability to rewrite the structure of the overlay topology. The experimental results are presented in two categories: (1) convergence speed and (2) accuracy of the epidemic applications. These provide the information for the performance guarantee of the global aggregate computation. The expectation of this paper is to present a preliminary study focusing on the system conversion methodology in the context of Industry 4.0. There are several recent publications based on Industry 4.0; however, nothing has been done to address any methodologies applied in Industry 4.0 or their simulation results.
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Epidemic Algorithms on Distributed Systems Towards Industry 4.0
(2018-08-21)
Poonpakdee, Pasu
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Koiwanit, Jarotwan
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Yuangyai, Chumpol
In today changing world, with the advent of technology and a challenging competition, reliable information methodologies and tools are required to collaborate big data and various components in both vertical and horizontal integrations over the value chain. Cyber-Physical System or CPS is one of available technologies serving industries' needs and desires. However, its important properties are required to be investigated to fully understand its characteristics and performance. This study present an epidemic-based communication and the results show that the base station could obtain the higher accuracy of the information while the method was adopted.
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Intelligent robot of inclined assembly sequence planning in Industrial 4.0
(2018-08-14)
Chiang, Yu Cheng
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Yuangyai, Chumpol
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Cheng, Chen Yang
In the industry 4.0, the Cyber-physical system (CPS) is one of the most important core which makes the manufacturing process more intelligent. Intelligent assembly operation is an important key in intelligent manufacturing of CPS. To complete the intelligent assembly operation, the cooperation between assembly robotic arm and assembly sequence planning (ASP) is necessary. However, the ASP and writing robotic codes manually is time consuming and requires professional knowledge and experience. Because the Local Coordinate System (LCS) is often ignored when checking for interference. If product have inclined interference and without considering LCS and causing and infeasible ASP. Therefore, this paper proposes a LCCPIAS (Local Coordinate Cyber-Physical Intelligent Assembly System) system to achieve three objective functions. First, this paper presents a dual-projected-based interference analysis approach (DPIAA) that analyzes the relations between components. Second, this paper generates optimal assembly sequence automatically to let the assembly sequence more suitable for the robotic arm to perform the assembly operation. The last one is LCS can recognize inclined interference between components and generate feasible ASP. Furthermore, this paper uses CAD model to verify that the DPIAA is faster and consider LCS interference can solve inclined interference problem. In the future assembly factory, the proposed method can help to realize intelligent manufacturing.
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T5-Based Named Entity Recognition for Social Media: A Case Study for Location Extraction
(2024-01-01)
Dahlan, Ahmad Faisal
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Yuangyai, Chumpol
Social media platforms have emerged as invaluable sources of real-time information, particularly during emergencies and events. Efficient and accurate extraction of location names from this unstructured text data can significantly enhance response efforts. This study investigates the performance of two models, T5 and SpaCy, for extracting location names from 5554 Indonesian-language tweets related to traffic conditions. The T5 model, leveraging its Transformer architecture and extensive pre-training, achieved a significantly higher accuracy of 95% in training and 93% in testing compared to SpaCy's 45% and 41% respectively. This disparity highlights T5's superior ability to handle complex language patterns and indirect location references often found in social media text. Conversely, SpaCy's reliance on Convolutional Neural Networks (CNNs) poses limitations in effectively processing diverse location representations and non-local text patterns. The results demonstrate the potential of T5 as a powerful tool for location extraction in social media analysis, with significant implications for improving disaster response and public safety efforts.
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Epidemic System Conversion on Industry 4.0's Perspective under Dynamic Network Condition
(2019-09-25)
Poonpakdee, Pasu
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Koiwanit, Jarotwan
;
Yuangyai, Chumpol
In large-scale manufacturing operations with increasing global competition, success depends on a reliable network system to complete a value chain transformation. The use of the Industry 4.0 paradigm is increasingly evolving in many areas of different industries to ensure significant increases in factory productivity, flexibility, and efficiency. Consequently, the system transformation requires a shift from single automated node to a fully integrated system. However, selected methodology and results require study to fully understand the digital transformation as well as its characteristics. This investigation presents a system conversion study, between centralized and decentralized systems, using the concept of epidemic membership protocols in the context of Industry 4.0. This paper proposes the method based on membership protocols focusing on the system conversion methodology under dynamic network condition. The experimental results show that the proposed method provides an ability to rewrite the structure of the network topology with optimal accuracy of epidemic application.
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A Last-mile Facility Allocation Problem with Traffic Congestion and Natural Disaster
(2023-01-01)
Dahlan, Ahmad Faisal
;
Yuangyai, Chumpol
Managing distribution in urban areas can be much more challenging in the twenty-first century. The population density carries accessibility issues to education, healthcare, infrastructure, and services through traffic congestion and pollution. It is worth noting that some areas simultaneously deal with traffic congestion and natural disasters. This issue may interfere with last-mile logistics, mainly when extreme conditions occur. One way to overcome this issue is by placing distribution facilities in unaffected areas to maximise last-mile logistics despite extreme conditions. However, the facility location problem regarding traffic congestion and natural disasters remains limited. This paper proposes Expected coverage problems with traffic congestion and natural disaster (ECP-CN), the extension model of facility location problems involving traffic congestion and natural disasters. ECP-CN consists of flexibility where the facilities will be in non-disaster areas rather than congestion-free areas whenever the ideal condition is not met. The algorithm used to solve the problem is a Genetic Algorithm (GA). The algorithm has converged to an optimal solution, showing a high initial demand coverage, subsequent improvements, and significant fluctuations. Further exploration of parameter settings or diversity-enhancing mechanisms may be optional. This result indicated that GA could locate the facilities in non-disaster areas while maintaining maximum demand coverage in all sample sizes.

Yuangyai, Chumpol

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