The Journal of Industrial Technology

กองบรรณาธิการ (Editorial Board) / วัตถุประสงค์ (Objectives) / บทบรรณาธิการ (Editorial Note) / สารบัญ (Table of Contents)

2025-12-23T13:12:29+07:00

The journal of Industrial Technology, 21(3), 2025

2025-12-23T13:15:17+07:00

ปกวารสาร (cover) - JIT volume 21, issue 3, 2025

2025-12-23T13:01:23+07:00

Development of Hybrid Water Heaters

2025-12-09T16:18:26+07:00

Solar water heating systems differ from other renewable energy systems in that their monthly solar radiation values and hourly radiation values fluctuate. As a result, after 10:00 a.m., the solar water heating system's operating hours are restricted to roughly 4-6 hours per day. This research's primary goals are to create a hybrid water heater, investigate the temperature of the hot water used, and assess its financial worth. The flat-plate solar collector used in the developed hybrid water heater is made up of two primary parts: a hot water tank and a solar collector. Water is circulated between the hot water tank and the solar collector in the hybrid water heater. The 40-liter hot water tank and the 2x1-square-meter solar collector are angled 15 degrees. The experiment's solar radiation intensity is 900 W/m2. It is set up for senior citizens' leisure and recreational activities in the Bang Yo Subdistrict Elderly Club, Bang Yo Subdistrict Administrative Organization, Samut Prakan Province (Herbal foot soaking). On April 8, 2025, the experiment was carried out, and at noon, it began to accumulate heat. After one hour ("∆t"), the water temperature in the hot water tank was 59.77 °C in the absence of an additional heat source, and it could reach a maximum of 154.06 °C in the presence of an extra heat source. According to the economic research used to calculate the hybrid water heater's payback period, it would take about 6.6 years.

A Comparative Study Battery Modeling between Equivalent Circuit and Neural Network for UAVs Using Real Flight Data

2025-12-09T16:34:16+07:00

This research presents a comparative study of battery models for unmanned aerial vehicles (UAVs), focusing on the comparison between the equivalent circuit model (ECM) and the artificial neural network (ANN) model. The performance of each model is evaluated based on the root mean square error (RMSE) between the estimated terminal voltage and the actual measured value in two evaluations: first, with a fresh battery, and second, with an aged battery after being used in flight operations for agricultural area surveys in the 50^th cycle. The test results in the first cycle show that the ECM provides high accuracy, with an RMSE of 0.1544. In contrast, the ANN model, affected by the limitation of its training dataset, yields a higher error of 0.8742. When the battery degrades, the ECM’s accuracy decreases due to its inability to adapt to behavioral changes across increasing cycles. Meanwhile, the ANN model shows improved prediction performance, with an RMSE of 0.472. These findings indicate that while the ECM offers high accuracy for fresh batteries, it lacks adaptability and requires parameter updates. On the other hand, the ANN model can learn degraded battery behavior.

A Lesson Learned from the UK Rail Project: An Analysis of Critical Success Factors (CSFs) through the Project Life Cycle (PLC)

2025-12-09T16:55:24+07:00

This study examines the critical success factors (CSFs) of railway construction projects through the lens of the Project Life Cycle (PLC), addressing the defining, planning, implementing, and closing phases. Although CSFs have been widely studied, their systematic application to Thailand’s railway sector remains limited, particularly in linking lessons from international best practices. Employing a systematic literature review and content analysis, the research synthesizes evidence from major United Kingdom projects, including Crossrail and High Speed 2 (HS2), and compares them with experiences in Thailand. The analysis identifies governance, stakeholder collaboration, and risk management as decisive CSFs across all PLC phases. Key findings highlight that early participatory consultations enhance legitimacy, structured risk registers and stakeholder mapping strengthen planning, while real-time monitoring and independent audits improve implementation oversight. In the closing phase, structured handovers and training programs are vital for operational continuity. By integrating international lessons with a PLC perspective, this study provides policymakers and practitioners with a structured framework for improving the governance, efficiency, and sustainability of Thai railway projects. Beyond this sectoral focus, the PLC–CSFs framework demonstrates wider applicability to large-scale infrastructure initiatives, contributing to both theoretical advancement and practical guidance for sustainable transport development in Thailand.

Applying Quality Function Deployment with Anthropometric to Design A Working Bench Table for Persons with Disabilities

2025-12-09T17:19:20+07:00

This research aims to study and apply the Quality Function Deployment technique in conjunction with anthropometric measurements to design a workbench suitable for individuals with lower-limb disabilities. The House of Quality matrix was employed as the primary tool to link user requirements with technical specifications. In the first phase, user needs were collected through questionnaires and translated into technical requirements based on product standards. A relationship matrix was then constructed to evaluate the importance of each requirement. In the second phase, anthropometric data were collected from 15 individuals with disabilities at the Redemptorist Foundation for People with Disabilities. Nine key measurements were taken, including seat height, knee height clearance, leg width clearance, forward reach range, side reach range, elbow height, wheelchair width, eye level height and wheelchair turning radius. These data were used to define appropriate dimensions for the workbench design. The results from both phases were synthesized to develop a prototype that reflects user needs and accommodates physical limitations. The evaluation of the importance rating showed an average increase of 5.93% after prototype development, with the highest improvement observed in the safety and stability dimension (11.03%), indicating the effectiveness of the design in meeting the needs of wheelchair users.

Machine Design and Development of A Semi-automatic Machine for Roasting and Grinding Dry Chili

2025-12-11T14:30:12+07:00

This research aims to develop a semi-automatic roasting and grinding machine for dried chili peppers that can control temperature and humidity as desired, in order to enhance efficiency in the processing as well as improve the consistency of the output. The developed prototype consists a dry chili roasting system using electric heater heat controlled by a thermostat and microcontroller, with results displayed on an LCD screen, and a grinding system that allows adjustment of the fineness. The maximum temperature can be set at 160 ºC, but experiments show that the optimal roasting temperature is 80 ºC, with the reading on the LCD deviating from the reference thermometer by a maximum of about 2.05 ºC. In experiments with defined relative humidity levels of 70, 75, 80, 85, 90, 95, and 100% RH, it was found that at 85% RH, the chili produced a pleasant aroma, bright red color, and took about 9 minutes to roast. The dried chili used in the experiment initially weighed 500 grams and reduced to about 457 grams after roasting. In the grinding process, and it was found that the period of 3–5 minutes. The work of the blending machine was 9 minutes, with the heat radiated 5 minutes before the blending, and the blending time 4 minutes, altogether 18 minutes, for one working round.

Optimization of Condition Using Response Surface Methodology for Boring Process in S20C Carbon Steel

2025-12-11T09:00:30+07:00

This research designed a Box-Behnken experiment and analyzed it using surface response methodology to predict the boring process of carbon steel grade S20C. The experimental variables encompass speed from 800 to 1,700 rpm, feed rate from 0.04 to 0.08 mm/rev, depth of cut from 0.10 to 0.50 mm, and overhang length from 45 to 55 mm. The experiment found that all the main factors affecting the surface roughness values were speed, feed rate, depth of cut, and overhang length. The surface roughness values increase significantly as speed decreases. Moreover, the surface roughness tends to decrease when decreasing the feed rate, depth of cut, and overhang length. The optimal conditions for surface roughness (R_a) of 2.267 micrometers were speed of 1,685 rpm, feed rate of 0.04 mm/rev, depth of cut of 0.39 mm, and overhang length of 45 mm. The experimental results were confirmed by comparing the predicted values with the actual measured values from the experiment, with a maximum surface roughness prediction error of 5%. The comparison of the experimental results revealed that the mean absolute percentage error value of the surface roughness was 3.16 percent, which is less than the specified error value and remains within the acceptable range.

Influence of Flow-Obstructing Fins and Air Temperature on the Paddy Drying Process

2025-12-11T10:00:53+07:00

This research aims to investigate the influence of installing flow obstructing devices within the paddy drying duct and the drying air temperature on the paddy drying process. Experiments were conducted using 3 kilograms of Thai jasmine paddy rice 105 with initial moisture contents ranging from 21-25% wet basis. The drying air temperatures were set at 60°C and 80°C, with the target final moisture content reduced to 14% wet basis. A small-scale prototype dryer, 2.5 meters in overall height, was developed for this study. The heat and mass exchange zone was designed as a straight cylindrical vertical tube, 4.5 inches in diameter and 1.5 meters in length. Two configurations of the drying tube were examined: (1) a straight tube without an internal helical fin, and (2) a straight tube fitted with an internal helical fin. Each configuration was tested in triplicate, focusing on the increase in grain temperature, the reduction of moisture content over time, drying rate, and the specific energy consumption. The results showed that the use of the helical fin significantly enhanced heat transfer to the grains compared to the plain tube. At a drying air temperature of 60°C, the grain temperature increased by more than 4.47% under identical testing durations. This enhancement contributed to a 56.67% improvement in the drying rate, while the specific energy consumption decreased by 21.42%. A similar trend was observed at the higher air temperature of 80°C, confirming the beneficial effects of installing internal flow obstruction devices in enhancing heat transfer and improving overall drying performance.

Design and Development of A Hybrid Solar Drying Chamber with Dry Hot Air and Integrated Moisture Condensation for Thai Herb Dehydration

2025-12-11T14:47:36+07:00

This research aimed to develop and evaluate the performance of a prototype herbal drying system by integrating thermoelectric technology with a conventional solar dryer. The prototype system consists of three main components: a solar drying chamber, a thermoelectric drying chamber, and an air circulation system. The process utilizes the hot side of Peltier modules to generate hot air at 58 °C, while the cold side condenses moisture to reduce relative humidity. This enables continuous and accelerated drying compared to traditional dryers. Experimental results showed that within 4 hours, the system could reduce the weight of raw materials as follows: fingerroot by 61%, moringa leaves by 50%, and ginger by 58%. These results reflect the hybrid system's superior ability to control drying conditions compared to sun drying and conventional solar dryers, in terms of temperature regulation, drying speed, and reduction of relative humidity within the system. Since studies on thermoelectric drying systems that incorporate condensation and practical application remain limited, this research presents a novel approach for herbal drying in Thai community contexts. It helps preserve the quality of raw materials, allows for indoor use, supports clean energy utilization, and demonstrates potential for sustainable scaling.

Revolutionizing Lifespan Prediction and Cumulative Damage Assessment of XLPE Copper Main Cables Using Multiphysics Simulation and Intelligent AI: A Case Study of the Industrial Technician School Building, RMUTSV

2025-12-11T13:18:02+07:00

This study investigates the thermal fatigue behavior of XLPE copper main power cables within the electrical distribution system of the Industrial Technician School Building at RMUTSV. Real-time measurements of temperature, current, and voltage were collected over a one-month period, revealing significant thermal fluctuations in the main conductors. Multiphysics simulation results indicated that Phase B exhibited the highest mean temperature of 30.82°C-approximately 12% greater than the other phases-leading to a maximum voltage drop of 1.40% and a peak energy loss of 0.00485W under high-load conditions. The copper conductor in Phase B also experienced thermal stress reaching up to 85% of its critical limit. In addition, a Machine Learning model developed in this research achieved 92% accuracy in predicting thermal fatigue risk. The results contribute to proactive maintenance planning and optimized load management, effectively reducing energy losses and extending the service life of XLPE copper cables. Overall, this work represents a significant advancement toward intelligent, reliable, and energy-efficient electrical infrastructure in real-world operational environments.

Development of An Automatically Vegetables Irrigation Based on Partitioned Areas Using Solar-powered Pump

2025-12-11T15:01:14+07:00

This research presents a zone-automatic vegetable irrigation system designed to reduce timely
basis task. The system utilizes water from a 750-watt submersible solar-powered pump energized with
three 330-watt solar panels. This system consists of main board that measures water volume using a flow
rate sensor, providing data to control four sets of 1-inch solenoid valves for zone-by-zone irrigation. Each
valve is controlled via LoRa wireless communication in a direct point-to-multipoint topology, which
minimizes wiring and enhances installation flexibility. Water volume for each zone is defined in codes. The
system supports six sprinklers per zone, each spaced 4 meters apart-twice the typical spray radius used
by local farmers-covering 96 square meters per zone, totaling 384 square meters across four zones.
Experimental results show water flow rates ranging from 42 to 54 liters per minute under solar irradiance
levels of 600 to 1000 watts per square meter. The developed system increases labor productivity,
conserves energy, promotes clean energy usage, and is adaptable to drip irrigation systems. Future
development includes optimizing energy efficiency for the valve-controlled circuitry to extend battery life
and increasing pump capacity and valve size to expand irrigation coverage per zone.

Guidelines for Being Carbon Neutral for Educational Institutions: A Case Study of Rajamangala University of Technology Rattanakosin, Wang Klai Kangwon Campus

2025-12-11T13:53:10+07:00

This research aims to assess and analyze the amount of carbon dioxide equivalent emissions, explore and evaluate the potential for carbon sequestration, and propose a possible carbon neutrality operational approach at Rajamangala University of Technology Rattanakosin, Wang Klai Kangwon Campus. The study follows the guidelines of the GHG Protocol and ISO 14064-1:2006, covering the identification The study follows the guidelines of the GHG Protocol and ISO 14064-1:2006, covering the identification of emission scopes (Scope 1 2 and 3), activity data collection, and calculation of emission factors. The study found that in 2024, the campus emitted a total of 1,607.89 tCO_₂eq_/year. The main source of emissions was electricity consumption (Scope 2), accounting for 72.33 % of the total, followed by Waste management by landfill, Methane emissions from wastewater treatment systems and Fuel consumption for official travel. Meanwhile, green spaces within the campus were able to absorb up to 930.52 tCO_₂eq /year, resulting in net emissions of approximately 677.35 tCO_₂eq /year. The research also proposes mitigation strategies, including energy efficiency improvement, renewable energy adoption and carbon offset programs, to support strategic planning toward achieving carbon neutrality in higher education institutions.

Effect of Air Flow Rate Control on the Performance of a Direct Reduction Ironmaking Furnace with an Air Preheater

2025-12-11T14:03:02+07:00

This research investigated the effects of regulating airflow rates into the reactor chamber of a direct-fired iron ore blast furnace equipped with an air preheater. The primary objectives were to enhance smelting efficiency while concurrently reducing fuel consumption. The experiments were centered on two critical variables: (1) the frequency of electric current supplied to the air supply device, which varied between 10 and 50 Hz, (2) the air delivery pattern, which was adjusted through two settings of the butterfly valve. The analysis of variance conducted on the collected data indicated that both factors significantly impacted the air velocity at the air holes of the blast furnace reactor layer (p < 0.001). The findings derived from practical smelting applications demonstrated that optimal operating conditions varied across different phases. During the furnace preheating phase, a frequency of 40 Hz with the standard temperature air inlet throttle at level 3 and the air preheater throttle closed at level 0 yielded the best results. In the smelting phase at 1,200 °C, an optimal frequency of 20 Hz was achieved by setting the standard temperature air inlet throttle to level 1 and the air preheater throttle to level 3, ensuring proper air mixing with a higher oxygen concentration. By effectively managing these variables, the research demonstrated significant improvements in combustion efficiency, thereby enhancing the reduction process. This approach resulted in an average yield of 25.55% mild steel and a notable reduction in the fuel consumption ratio from 3:1 to 2:1.

An Optimization Approach for Solving a Mixed Model Assembly Line Balancing Problem with Collaborative Robots (Cobots) by Considering Time-weighted Average (TWA) Ergonomic Risk Score

2025-12-11T15:36:47+07:00

The objective of this research is to develop a mathematical model of the Mixed-Model Assembly Line Balancing Problem Type II (MMALBP-II) with collaborative robots (Cobots) considering time-weighted average (TWA) ergonomic risk score, and to test the performance of solving the model using the Branch and Cut method to reduce ergonomic risk for both working-age and elderly workers through human–Cobot collaboration. The performance evaluation using IBM ILOG CPLEX 22.1.2 showed that the model could solve benchmark balancing problems and find feasible solutions in 63 out of 75 experiments (84%). Optimal solutions were found in 53 out of 75 experiments (70.6%). On average, the total %gap of the total cycle time deviated from the lower bound of the average cycle time by 17.9%. The results further indicated that Branch and Cut could solve small- and medium-sized problems within the time limit (3600 seconds), with an average optimality %gap of 0% and 4.8%, respectively. However, for large-sized problems, in 12 out of 15 cases, Branch and Cut was unable to find a feasible solution within the given time.

Reducing Waiting Times for Outpatient Services: A Case Study of Hospital Operations

2025-12-12T08:32:46+07:00

Long waiting times are a major challenge in the outpatient departments of hospitals in Thailand. This study examines the outpatient service process in a hospital that serves an average of 120 patients per day. Patients typically spend a substantial amount of time waiting rather than receiving medical care, averaging approximately 1 hour and 49 minutes. Specifically, the average waiting time at the medical history-taking station was 29.48 minutes, exceeding the standard of 20 minutes. Moreover, patients waited an average of 35.54 minutes before physician consultation, which surpasses the standard of 30 minutes. To address this issue, eight appointment scheduling scenarios were designed to better distribute patient arrivals throughout the day. These scenarios were simulated using Arena Simulation to evaluate their effectiveness. The results indicated that the scheduling model assigning patient appointments every 30 minutes, with a higher concentration of appointments in the later hours, was the most effective. This scenario reduced the total time in the system by 29.03%, decreased the average waiting time at the medical history-taking station to 20.33 minutes (a 30.98% reduction), and lowered the average waiting time before physician consultation to 33.45 minutes (a 25.90% reduction), compared with the current process. These findings provide practical insights for improving outpatient scheduling and reducing patient waiting times in hospital settings.

A Comparative Analysis of Supervised Machine Learning Algorithms for Fault Prediction in Automotive Suspension Systems

2025-12-12T09:11:08+07:00

Intense automotive vibrations, while common, can seriously compromise driver health. This research utilizes machine learning (ML) to predict potential failures in car suspension systems, targeting an enhancement in vehicle reliability and safety. While numerous studies have simulated suspension faults, the increasing data complexity from uncertain parameters necessitates more efficient algorithms for precise fault identification. This study, therefore, conducts a comparative analysis of several supervised machine learning algorithms to determine the most accurate method for this predictive task. The algorithms were evaluated using four distinct feature set preparations: original data, standard deviation data, principal component analysis data, and a combined set of mean standard deviation and principal component analysis. The findings reveal that the Artificial Neural Network (ANN) and Support Vector Classifiers (SVC) algorithms yield the highest prediction accuracy. Notably, this peak accuracy was achieved when utilizing the combined feature set (mean standard deviation and Principal Component Analysis (PCA)). These results offer a valuable contribution toward designing more robust car suspension systems and advancing future preventive maintenance strategies.