Engineering and Technology Horizons

Streamlining Production: ECRS Approach to Enhancing Efficiency in Case Tank Sub Weld of Hydraulic Excavator

2025-06-24T07:27:34+07:00

This research aimed to reduce the production time in the manufacturing process of fuel tanks, with a focus on enhancing the efficiency of the process to better meet customer demands. A study of the production process revealed that the primary issue was in the welding stage at the top of the tank using a robot, which took 2 hours and 45 minutes. Almost half (1 hour 22 minutes) of the time were spent on setting the position of the workpiece—a step that must be repeated each time a new workpiece was introduced. This significantly contributed to reducing throughput and increasing labor costs. To address this issue, the research team applied the ECRS concept to analyze and streamline the production process by eliminating unnecessary steps. Consequently, the team designed and developed a workpiece holding fixture to assist in the welding process. This fixture ensured the workpiece was positioned accurately without the need for repeated setup. Two conceptual designs of the fixture were initially developed and evaluated using FEA. After evaluation by engineers and welding staff, using AHP, the best option was determined. As a result of this improvement, cycle time in robotic welding was reduced to 1 hour and 23 minutes a 49.26% reduction.

Development of an Automated Workflow for Reinforced Concrete Structural Quantity Takeoff and Cost Estimation Using Visual Programming in a BIM Environment

2025-06-19T08:06:01+07:00

In the construction industry, traditional quantity takeoff (QTO) methods still rely on manual measurement from 2D drawings, which are time-consuming, prone to human error, and difficult to verify. As Building Information Modeling (BIM) adoption grows, especially following the release of national BIM standards in 2020, there is increasing interest in developing automated and data-driven workflows to improve the accuracy and efficiency of material quantity estimation. This study proposes an integrated workflow for the automated quantity takeoff and cost estimation of reinforced concrete structures using BIM and visual programming. The workflow leverages Dynamo in Autodesk Revit to extract structured rebar and concrete data from BIM models, which is then exported to Excel using VBA macros for sorting and cleaning data and visualized in Power BI dashboards. A case study of a 7-story residential building demonstrates the application of the proposed workflow. Results show a significant reduction in processing time from several hours using manual Revit QTO to just a few minutes while maintaining high accuracy, standardization, and traceability. The dashboard allows multi-dimensional analysis by material type, structural component, and building floor, supporting effective decision-making in construction cost management. The findings point out the prospects for scalable deployment of this workflow in digital construction environments.

Performance Comparison of Nonlinear Pre–Calibrate Low–Cost PM2.5 Sensors Using an SPS30 Reference

2025-08-06T08:15:35+07:00

This research presents a performance comparison of low–cost particulate matter (PM2.5) sensors, widely used in Internet of Things (IoT) applications for air quality monitoring. Since sensor calibration is often costly, this study proposes a cost–reduction strategy by applying pre–calibration before full calibration. The SPS30 was selected as the primary reference device due to its combination of low cost and near–regulatory–grade performance. Unlike other low–cost sensors, the SPS30 benefits from factory calibration against reference instruments (e.g., TSI DustTrak DRX 8533, OPS 3330), and it has demonstrated very low intra–model variability (<1.5% for PM2.5) and strong correlations across all concentrations with Federal Equivalent Method (FEM) instruments. It is also MCERTS–certified (UK Environment Agency), confirming its compliance with PM2.5 monitoring standards. To validate the methodology, the SPS30’s accuracy was additionally examined using an air purifier in the test setup. A nonlinear mathematical model was then applied to calibrate commonly used sensors, including the Plantower PMS series (PMS7003, PMS5003, PMS3003) and SDS011. Experiments were conducted in an indoor environment at 33 ± 1°C and 69 ± 4% relative humidity. The results showed coefficient of determination values of 0.98, 0.98, 0.96, and 0.88, with root mean square error values of 1.2, 1.47, 1.84, and 3.26 for the PMS7003, PMS5003, PMS3003, and SDS011, respectively. The findings indicate that low–cost sensors, particularly the PMS7003 and PMS5003, can achieve high measurement accuracy when combined with appropriate pre–calibration and a suitable reference device. The SDS011 also demonstrated consistent performance. In addition, applying a nonlinear model reduces costs and enhances sensor reliability. For initial deployment, pre–calibration lowers expenses by approximately one–third compared to full calibration, while pairwise pre–calibration for recalibration can substantially reduce or even eliminate recurring calibration costs during long–term operation and maintenance. These results highlight the practicality of deploying low–cost sensors in air quality monitoring applications.

An Investigation of Ice Formation Behavior in Vertical Annular Flow

2025-08-26T08:36:51+07:00

This study investigates the ice formation behavior in an annular flow under initial flow velocities ranging from 0.20 to 0.45 m/s. The experiment was performed to validate the Computational Fluid Dynamics (CFD). The results indicate that lower flow velocities, 0.20-0.35 m/s, promote continuous ice growth leading to full the annular passage, whereas higher velocities, 0.40-0.45 m/s, suppress ice accumulation due to enhanced convective heat transfer and disruption of the mushy zone. Importantly, it was found that the ice growth rate decreases with increasing initial flow velocities. Furthermore, the correlations for predicting the ice thickness and ice growth rate with time as power functions were developed. The correlations agreed well with the simulation results. This information is very useful for design, and operating the tubular ice machines which has never found in literatures.

The Development of a Node-RED-Based Dashboard for Real-Time Monitoring and Control of Air Data Test Set (ADTS) Based on IoT

2025-07-22T13:37:50+07:00

Aviation accidents are often caused by incorrect airspeed readings due to inaccurate pitot-static sensors. This study develops an IoT-based Air Data Test Set (ADTS) for real-time airspeed calibration, utilizing an ESP32, MS5803 sensor, MQTT protocol, and Node-RED interface. Unlike previous studies limited to local monitoring, Node-RED in this study functions as an interactive control hub as well as an integrated visualization platform with automatic logging to MySQL, thereby enhancing the system’s reliability and accessibility. Experiments were conducted over a range of 20–140 knots with five repetitions per data point, resulting in communication latencies of 200–500 ms and high accuracy, with pressure-to-speed conversion errors ranging from 0.09% to 14%. The largest deviation occurred at low speeds (40 knots, −14%), whereas at speeds above 70 knots, errors remained below ±1%. With features such as remote control, real-time monitoring, and automatic logging, this system provides a practical calibration tool for laboratories and educational purposes, while also laying the groundwork for further development under more realistic flight conditions.

Stock Clustering Framework using Financial Ratios: A Case Study in the Stock Exchange of Thailand

2025-09-16T07:49:13+07:00

Value investors typically seek undervalued stocks that align with specific financial criteria to maximize their margin of safety. However, manually analyzing the financial data of all listed stocks is a time-intensive process. Furthermore, the market price of a target stock may exceed its intrinsic value, introducing potential investment risks. To address these challenges, this study proposes a stock clustering framework that groups equities based on financial ratio similarity. The proposed framework is designed to streamline the investment decision-making process by recommending stocks with comparable financial profiles as alternatives to those currently attracting investor interest but that may already be overvalued. Multiple clustering algorithms are evaluated to determine the most effective grouping strategy. Empirical back testing using four years of data from the Stock Exchange of Thailand reveals that the Gaussian Mixture Model (GMM) achieves the highest composite performance metric among the tested methods. Additionally, the HDBSCAN algorithm is employed to detect and exclude outlier stocks, thereby enhancing the reliability of the clustering results.