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Integration of discriminant analysis with Artificial neural networks to decision analytic framework for enhancing automated visual IC inspection accuracy
(2026-05-15)
Kiatcharoenpol, Tossapol
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Klongboonjit, Sakon
This study aims to enhance the accuracy and reliability of automated visual inspection (AVI) in semiconductor manufacturing by integrating Linear Discriminant Analysis (LDA) and an Optimization Layer by Layer Neural Network (OLLNN). Unlike prior LDA-ANN hybrid approaches that primarily emphasize classification accuracy, this study formalizes a decision-analytic inspection loop that explicitly links discriminant centroids, feasible lighting regions, surrogate nonlinear modeling, and production level validation. A two-stage decision analytic framework is developed. In the first stage, LDA is applied to classify and identify discriminant boundaries and centroids between acceptable and defective image features under three lighting setups: coaxial ring, high ring, and low ring lights. In the second stage, OLLNN is trained using these features to capture nonlinear dependencies between greyscale intensity and lighting parameters, and then a surface response plot is used to ease the optimal parameter selection. The integrating model is validated using experimental IC marking inspection data to evaluate improvements in accuracy, especially false positive rates (Type I error). It was found that for the validation state, the false positive rates are reduced from 5.8% to below 4.6%, and classification accuracy improves significantly across variable illumination conditions. After implementation in mass production, the yield is increased to 99.6% with zero false positive found. This significant development of the integrating model enhances a foundation for adaptive, data-driven control of AVI parameters in smart factory environments that support real-time learning and improvement.
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Evaluating Critical Barriers to Industry 4.0 Adoption in the Thai Automotive Sector Using an Integrated Fuzzy BWM-PROMETHEE II-DEMATEL Framework
(2026-01-01)
Kiatcharoenpol, Tossapol
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Sirisawat, Pornwasin
This study investigates the barriers to the adoption of Industry 4.0 (I4.0) in the Thai automotive industry, which is a major economic growth and export competitiveness driver. It aims to offer evidence-based prioritization of barriers and causal relations to inform firms and policymakers in the transformation of smart manufacturing. The methodology follows three stages of multi-criteria decision-making model. Based on a literature survey and expert knowledge, the integration of Fuzzy BWM-PROMETHEE II was used for prioritization. Then Fuzzy DEMATEL is employed to illuminate the causal relationship among critical barriers. The Fuzzy BWM results highlight Customization, Flexible Production, Human-Machine Collaboration, and Cybersecurity as the most influential practices supporting I4.0 implementation. While analysis of Fuzzy PROMETHEE II and DEMATEL together identifies High Initial Investment, Supply Chain Integration as critical barriers and dominant causal drivers that influence other dependent barriers. Addressing these two factors initially helps accelerate digital readiness and enhance transformation performance. The study presents the advanced systematic ranking of I4.0 adoption barriers in the Thai automotive industry. The integration of Fuzzy BWM-PROMETHEE II-DEMATEL framework has a novel methodological contribution and also provides useful decision support to strategic planning and resource allocation.
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Enhancing Sustainable Herd Structure Management in Thai Dairy Cooperatives Through Dynamic Programming Optimization
(2025-05-01)
Sarttra, Thana
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Kiatcharoenpol, Tossapol
Herd management plays a vital role in boosting the productivity, profitability, and sustainability of dairy cooperatives, particularly in developing countries where smallholder farmers are prevalent and have limited access to modern farming technologies. This research presents a dynamic programming (DP) model aimed at helping dairy cooperatives optimize decisions regarding herd structure, specifically focusing on strategies for culling and replacement to match milk supply with varying market demands. The model considers essential traits of dairy cows, including age, milk production, and reproductive condition, to ascertain the best transitions within the herd over several periods. Findings indicate that implementing the proposed DP model can effectively align milk output with fluctuating demand, decrease the gap between supply and demand, and enhance overall herd productivity. While this study uses Thai dairy cooperatives as a case study, the developed model and its insights are relevant to similar smallholder dairy systems in other developing countries, thereby aiding improved decision-making and promoting sustainable herd management practices worldwide.
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PREDICTION OF STOCK PRICE USING HYBRID NEURAL NETWORK: A CASE OF COAL PRODUCTION COMPANY
(2025-01-15)
Kiatcharoenpol, Tossapol
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Klongboonjit, Sakon
Stock market prediction is a critical issue in the field of economics. As machine learning technologies advance, an increasing number of algorithms are being utilized to forecast stock price movements. Nonetheless, predicting stock market trends remains a challenging task due to the inherent noise and volatility in stock market data. This paper addresses this challenge by proposing a novel hybrid neural network model designed to predict stock market prices using parameters related to commodity prices and stock indices. A case study company is mainly in coal production business in Thailand, which produce coal, sale, distribute and operate coal-fired power plants as well. The Multiple Linear Regression (MLR) and Back propagation neural network (BPNN) as traditional prediction technique are employed to comparatively investigate the accuracy and performance of the proposed HNN. Experiment results show that the prediction accuracy of HNN is superior to MLR but similar to that of the BPNN model. However, HNN has a good performance both in accuracy, speed and practice. It can help investing analysts and investors make their wise decisions.
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Application of Two-Step Entropy–TOPSIS Method and Complete Linkage Clustering for Water-Pumping Windmill Investment on Thailand Peninsula
(2024-12-01)
Klongboonjit, Sakon
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Kiatcharoenpol, Tossapol
This study focuses on identifying suitable areas for the installation of water-pumping windmills in Thailand, which require wind speeds of at least 4 m/s to operate efficiently. A simple combined approach is introduced, integrating the Entropy–TOPSIS method complete linkage clustering to prioritize and categorize potential locations. Out of 271 initial areas, 28 have been selected based on their ability to meet the 4 m/s wind speed threshold. The Entropy–TOPSIS method first evaluates these areas based on monthly wind speed and agricultural area. The analysis reveals that regions with higher wind speeds generally score better for wind energy potential, while areas with larger agricultural spaces tend to score higher for farming suitability. The final integrated scores show that agricultural area is more significant, with a weight of 0.7788, compared to the wind speed weight of 0.2212. The areas are then ranked, and complete linkage clustering groups them into six categories, from the most to the least suitable for windmill installation. A sensitivity analysis confirms the robustness of the clustering method, as the group composition remains stable despite minor changes in weight adjustments. This approach simplifies decision-making for sustainable energy investments in Thailand agriculture sector.
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Improving Automotive Tire Defect in the Vulcanization Process by Leakage Bladders Issues
(2024-01-01)
Kiatcharoenpol, Tossapol
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Sawangnimitkul, Penpisut
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Junmewong, Rattanaporn
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Sirikasemsuk, Kittiwat
The primary objective of this project is to minimize defects in industrial tires resulting from issues with leaking bladders during the curing process. The project aims to reduce the current monthly defective tire production of 1,378.70 kilograms to a target of 1,240.83 kilograms per month, which represents a 10% reduction in defects. Utilizing the QC story methodology, QC 7-tools, and Quality Control techniques, a thorough examination of data and root cause analysis identified five significant factors contributing to defective tires caused by bladder leakage. These factors include 1) a lack of expertise in bladder inspection, 2) excessive usage of bladders, 3) contaminations on the bladder surface, 4) inadequate bladder lubrication and 5) oxidation occurring inside the bladder. To address these issues, a new operational procedure and training program were introduced, alongside modifications to the curing machine to mitigate the oxidation process. Over a span of two months, these changes resulted in a reduction in defective tires to 735.345 kilograms per month, equivalent to a 46.66% decrease in the problem, and a cost savings of 10,000 USD per year.
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DEFECT REDUCTION OF AUTOMATIC FARE COLLECTION SYSTEM FOR A NEW MRT MONORAIL LINE USING DMAIC
(2024-01-01)
Kiatcharoenpol, Tossapol
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Pratalab, Kewalin
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Klongboonjit, Sakon
The objective of this research is to examine and address issues related to the quality of the automatic fare collection system for the new Mass Rapid Transit (MRT) line and to decrease maintenance time to meet the key performance indicator (KPI) set by the Mass Rapid Transit Authority (MRTA). A total of 1,497 work orders were collected from a survey conducted between June and August 2023. The study utilizes the DMAIC methodology and identifies the primary issue as “Note Module Faulty” commands on the Ticket Vending Machine (TVM), particularly for cash transactions where banknotes become stuck in the banknote acceptor (BNA) section, resulting in transaction failures. After conducting a root cause analysis, it was determined that the problem stemmed from the inadequate design of the banknote acceptor for the new polymer banknotes, Biaxially Oriented Polypropylene (BOPP) film. Following the resolution of the problem, it was noted that the daily influx of work orders follows an exponential decay trend, represented by the equation Yt = 3.525x(0.97803t), and the maintenance time has decreased from 62 minutes per work order to 9.27 minutes per work order.
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A lean manufacturing approach to waste minimization: a case of industrial rack plant
(2024-01-01)
Kiatcharoenpol, Tossapol
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Chaosamthong, Kirati
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Rawirangsun, Phuwit
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Klongboonjit, Sakon
The concept of Lean Manufacturing is a methodology focused on eliminating waste in various activities through the application of five key principles: defining value, mapping the value stream, ensuring flow, establishing pull, and pursuing perfection. Starting with the identification of value based on product sales, it was determined that industrial rack product is the top-selling product. A Current Value Stream Map (Current VSM) was created to illustrate value-adding activities within the production process and highlight sources of waste. The analysis identified inefficiencies in the production processes of side frames, beams, and packaging, which hindered the ability to meet the target takt time of 60 seconds per unit. Once the processes requiring improvement were identified, strategies for optimization were developed, leading to the creation of an Improved Value Stream Map (Improve VSM). The implementation of waste reduction measures resulted in a significant decrease in production cycle time, from 104 seconds per unit to 60 seconds per unit—a reduction of 42.31%, enabling compliance with the target takt time. Ultimately, the complete elimination of waste led to the creation of an Ideal Value Stream Map (Ideal VSM). This was achieved by incorporating pull system and continuous u-shaped line principles and consolidating workstations to minimize non-value-adding activities. These improvements not only enhanced production efficiency but also maximized the ability to meet customer demands and contributed to building a competitive advantage.
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Assessment of Potential Area for Solar Energy Investment in Northeastern Thailand by Entropy-TOPSIS Method
(2024-01-01)
Phonphoon, Phichata
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Kiatcharoenpol, Tossapol
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Klongboonjit, Sakon
Agriculture is one of the important engines of Thailand's food industrial growth however the cost of fossil energy is quite high for Thai farmers. Since Thailand has great solar potential, especially northeastern region, renewable energy sources of sunlight in this region should be considered to be another energy source for Thai agriculture. To assess and classify the potential of the investment in agricultural solar power systems of 20 provinces in Thailand's northeastern region, this study applied the combining method of Entropy Weight Method and TOPSIS with secondary data of solar irradiance, farmer household density, and income of farmer households. With this combining method, the results showed that farmer household density and income of farmer household were more influence on assessing and classifying the potential of this investment than solar irradiance. Finally, all 20 provinces were classified into four groups of Group A (A<inf>12</inf>, A<inf>2</inf>, A<inf>4</inf>, A<inf>1</inf>, and A<inf>10</inf>), Group B (A<inf>8</inf>, A<inf>9</inf>, A<inf>19</inf>, A<inf>3</inf>, and A<inf>15</inf>)), Group C (A<inf>17</inf>, A<inf>13</inf>, A<inf>18</inf> A<inf>16</inf>, and A<inf>20</inf>) and Group D (A<inf>6</inf>, A<inf>11</inf>, A<inf>7</inf>, A<inf>14</inf>, and A<inf>5</inf>) from the most potential province group for investment in agriculture solar power system to the least potential province group for investment in agriculture solar power system.
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Priority of Wind Energy in West Coast of Southern Thailand for Installing the Water Pumping Windmill System with Combining of Entropy Weight Method and TOPSIS
(2023-10-01)
Klongboonjit, Sakon
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Kiatcharoenpol, Tossapol
Wind energy potential or quality serve as the primary determinants influencing the decisions of Thai farmers regarding the installation of water-pumping windmills with heights ranging from 9 to 15 m and a cut-in wind speed requirement of 4 m/s, aimed at reducing their fuel costs. To introduce a simplified calculation method as one of their decision-making tools, the combined approach of the entropy weight method with TOPSIS has been introduced to assist them in prioritizing and assessing the wind quality in their respective areas. This study focuses on the western region of Southern Thailand, known for its high agricultural productivity. Initially, only 18 out of the 227 sub-districts with a minimum monthly wind speed exceeding 4 m/s were selected for thorough investigation. Subsequently, the entropy weight method was applied to the monthly wind speed data of these 18 chosen sub-districts to calculate their monthly weight values. These monthly weight values provide a quantifiable characterization of the wind quality in these specific sub-districts, revealing variations in wind quality between seasons, with superior quality during the summer season compared to the rainy season. Following the calculation of monthly weight values, the TOPSIS technique was applied to the wind data in conjunction with these monthly weight values, resulting in the determination of performance scores (P<inf>i</inf>) for each of the 18 sub-districts. P<inf>i</inf> values were found to vary from 0.0641 to 0.9006. In the final step of the analysis, these 18 sub-districts were ranked based on their respective P<inf>i</inf> values, with the implication that sub-districts exhibiting higher P<inf>i</inf> values are more suitable for the installation of water-pumping windmills with heights ranging from 9 to 15 m compared to those with lower P<inf>i</inf> values.

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