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 39

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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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Barriers to electric vehicle adoption in Thailand
(2021-11-01)
Kongklaew, Chanwit
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Phoungthong, Khamphe
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Prabpayak, Chanwit
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Chowdhury, Md Shahariar
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Khan, Imran
Electric vehicles (EVs) are considered to be a solution for sustainable transportation. EVs can reduce fossil fuel consumption, greenhouse gas emissions, and the negative impacts of climate change and global warming, as well as help improve air quality. However, EV adoption in Thailand is quite low. Against this backdrop, this study investigates barriers and motivators for EV adoption and their public perception in Thailand. A total of 454 responses were collected through an online questionnaire. The results indicate that the top three concerns of respondents about EVs are public infrastructure and vehicle performance in terms of charge range and battery life. Respondents with more than five years of driving experience in the age range of 26–35 years old could be key targets for early EV adoption.
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Impact of Climate Change on Livestock Production and Its Adaptation in Nepal
(2025-01-01)
Sedai, Dilli Ram
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Gyawali, Saroj
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Dangal, Megh Raj
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Yuangyai, Chumpol
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Lim, Noor Hashimah Hashim
Climate change directly affects livestock by increasing the temperature, which leads to increased infertility, loss of conception, poor expression of heat, repeat breeding, and sterility. This results in loss of weight gain, lower feed conversion ratio, increased transmission of vector-borne diseases, incidence and distribution of external parasites, and diseases susceptible to livestock systems. Livestock can be distressed by altering external factors. A study was conducted using a review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Environmental management is documented as a significant instrument for the review protocol. A systematic search process on the impact of climate change adaptation measures on livestock production includes enclosure and elimination criteria for suitability valuation. It is presented through quality assessment, data achievement, concepts, and investigations. Relevant literature was retrieved using Scopus, Google Scholar databases, and Web of Science. The effects of climate change on Nepalese livestock producers include reducing heat stress, risk management, adopting technology development by farmers, animal management, breeding management, feed and feeding management, and manure management for adaptation. These strategies should be applicable at the nationwide and local levels as development approaches. These conclusions and variations are relevant for improving farmers’ economies and ensuring food security in the least developed and developing countries with livestock producers.
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An auction bidding approach to balance performance bonuses in vehicle routing problems with time windows
(2021-08-02)
Cheng, Chen Yang
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Ying, Kuo Ching
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Lu, Chung Cheng
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Yuangyai, Chumpol
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Chiang, Wan Chen
In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW.
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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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Two-stage stochastic program for supply chain network design under facility disruptions
(2021-03-01)
Kungwalsong, Kanokporn
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Cheng, Chen Yang
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Yuangyai, Chumpol
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Janjarassuk, Udom
A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.
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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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Image analysis and high dimensional control chart for inspection of sausage color homogeneity and uniformity
(2016-09-01)
Kaewsuwan, Piraya
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Yuangyai, Chumpol
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Cheng, Chen Yang
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Janjarassuk, Udom
Sausage color usually influences consumers' selection due to the perceptions of quality. Extensive studies have applied image processing to capture the characteristics of food products according to the high-dimensional nature of the resultant images. However, the color homogeneity (i. e. "within pack" variation) and uniformity (i. e. "between-pack" variation) have rarely been studied. Therefore, this paper proposes a new framework to detect both variations using images. In addition, a new approach has been developed to deal with high-dimension data involving colorimetric characteristics, namely L∗, a∗, b∗, hue (h) and chroma (C∗). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T2 chart is used to detect color abnormalities. Our approach indicates that the out-of-control items can be identified with the control chart signals. Nonetheless, the out-of-control signals alone are inadequate for determination of the possible causes. Then, the proposed analysis framework was subsequently applied to identify possible causes that contributed to the process deviations. Furthermore, prior to the experiments with sausages, the image inspection device was tested for gauge repeatability and reproducibility.
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Automated Design of Affordable Modular Čustom Insoles by Multi-Classification Algorithms
(2021-06-01)
Khaing, Hnin Phyu
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Čhaiprapat, Supapan
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Rungsri, Tuanjit Na
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Yuangyai, Čhumpol
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Jirastitsin, Suriya
Čustom designed insoles are a niche product that is not always affordable to all who need them. When commercial insoles are fabricated using advanced technologies, the insoles in this study are assembled out of pre-cut modular components to keep the production cost down, hence their price. In this study, algorithms driven by a fuzzy inference were proposed in comparison with a decision tree in order to select the best component combination. One hundred and twelve subjects were recruited to collect foot data extracted from their foot images. Approximately 95% of 182 AI-designed insole pads were found in perfect agreement with the professional podiatrist's decision with acceptable 5% deviation. Differences in the algorithms' strength were also discussed. In addition to their superior performance, both algorithms allow the podiatrists to speed up the diagnosis and design phases. This approach, when integrated with applications of mobile devices for remotely retrieving foot data, will expand another simple yet effective customer-oriented product design service.
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Effect of Post Processing Heat Treatment on the Cyclic High Temperature Hot Corrosion Behavior of the Laser Powder Bed Fusion Processed Inconel 718
(2026-07-08)
Sahu, Saroj Kumar
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Dalai, Renu Prava
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Shaik, Nagoor Basha
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Yuangyai, Chumpol
;
Behera, Ajit
In the present investigation, the cyclic hot corrosion behavior of laser powder bed fusion (LPBF)-processed Inconel-718 superalloy subjected to single-aging (HT-1) and double-aging (HT-2) heat treatments was systematically evaluated. Tests conducted at 850°C for 96 h in a Na<inf>2</inf>SO<inf>4</inf>-NaCl-NaVO<inf>3</inf> molten salt environment demonstrated that HT-2 exhibited significantly enhanced corrosion resistance. The cumulative weight gain for HT-2 was 4.8 mg/cm2, compared to 5.7 mg/cm2 for HT-1 (an approximate 15.8% reduction), alongside a notably lower calculated corrosion rate (584.0 vs. 693.5 mg/cm2/year). Cross-sectional analysis indicated that HT-2 formed a thinner, denser, and more adherent oxide scale—primarily Cr<inf>2</inf>O<inf>3</inf> and NiCr<inf>2</inf>O<inf>4</inf> spinel—with reduced chromium depletion beneath the oxide layer. This improved corrosion resistance of HT-2 is attributed to its refined precipitate distribution and enhanced chemical homogeneity, which promote uniform chromium diffusion and the formation of a stable protective oxide barrier. This stable oxide scale effectively limits oxygen ingress, molten salt penetration, and scale spallation during cyclic thermal exposure. Overall, optimized double-aging significantly enhances the cyclic hot corrosion resistance of LPBF-processed Inconel-718. These findings provide important insights into tailoring post-processing heat treatments to improve the high-temperature durability of additively manufactured superalloys.

Yuangyai, Chumpol

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