Engineering Access

Enhancements to Human Resource and Data Resource Management Performance in the Development of Disc Brake Pad Products

Preeyanunt Morrakotsriwan — 2024-06-26

The brake friction material sector is impacted by severe competitiveness, continuously changing client needs, and very precise customer specifications. As a result, precise design and quick development are techniques to increase competitiveness. Cross-functional work approaches are complex and generate product development delays. In addition, resource management in cross-functional work, such as handling huge amounts of information and various participants, is critical for a corporation. This study aimed to enhance resource management performance by separating activity into two segments. First, the project management efficiency in a new model development process was improved by deploying a program for displaying work status, which was a project management program. Second, by establishing the product development program on a web application platform utilizing MySQL, the data collection procedure is being improved. Following execution, a project management program can improve team project management efficiency. It satisfied managers at a very satisfactory level with a mean of 4.33 out of 5 and staff at a good level with a standard of 4.09 out of 5. Moreover, the product development program can improve the data collection process by 5.72% reducing the working time of the disc brake pad product development process, 24.53% reducing the time required to input the sample test data, and 60% reducing the time required to search for the sample test data. Managers and employees were both extremely satisfied, scoring 4.25 and 4.24 out of 5 points, respectively.

Multi-Period Optimization of Energy Demand Control for Electric Vehicles in Unbalanced Electrical Power Systems Considering the Center Load Distance of Charging Station Areas

Noppanut Chitgreeyan — 2024-06-26

The rise of plug-in electric vehicles (EVs) impacts the energy demand of power systems. This study employed a multi-period power flow analysis on the IEEE 123 node test system, which was optimized for the installation of 6-position EV charging stations. Temporal load shifting was utilized to control the charging intervals of electric vehicles. Non-dominated Sorting Genetic Algorithm (NSGA-II) was applied to determine the optimal locations for installing EV charging stations, considering target functions, such as total energy loss, voltage unbalance factor (VUF), and center load distance. The results showed that the center load distance resulted in the optimal charging station location in the central area of the system, different from conventional considerations. The results showed that installing the charging station in the center of the load group (case 4) increased the total energy loss and VUF compared to installing it at the root of the load group (case 3) by about 2.1134 and 1.2287%, respectively. However, EVs reduced impacts during periods of system weakness. By controlling charging intervals during off-peak times (case 6), total energy loss and VUF were decreased by 4.7070 and 5.6896%, respectively, which effectively reduced energy demand during peak periods.

A Model for Determining the Effectiveness of Regulatory and Administrative Measures of Sustainability Policy in Thailand

Pruethsan Sutthichaimethee — 2024-06-26

The objectives of this research are to 1) create a causal relationship model for political management in order to form sustainable policies under Thai environmental law. and 2) propose a strategic framework for national management to achieve sustainability. The study is designed as a mixed-methods study, which is called Path-Analysis based on an autoregressive Conditional Heteroscedasticity with observed variables (Path-ARCH-x_i Model). The study confirms a causal relationship between all three latent variables: economic growth, social growth, and environmental growth, with direct and indirect effects. Notably, each variable's indicator shows a different impact size of the relationship to changes in the variables. Economic growth is significantly influenced by industrial structures, while employment can greatly influence the changes in social growth. In terms of environmental growth, it is greatly influenced by energy consumption. Furthermore, economic growth, followed by social and environmental growth, has the greatest capacity for error correction. Consequently, proactive measures, particularly economic measures, are critical to ensure that environmental law enforcement is used to its full potential, as seen in European countries.

Red Light Running Behavior of Motorcyclists on Urban Roads: Psychological Factors and Risk Perception

Jetsada Kumphong — 2024-06-26

Red light running (RLR) constitutes a significant road safety challenge encountered by numerous countries. Especially among motorcyclists, this behavior leads to severe accidents, serious injuries, and death. Hence, awareness of potential hazards and adherence to driving safety are significant. This research aims to study the risk perceptions and explain the psychological factors associated with rider's RLR behavior. The questionnaires (N=250), approved by the ethics committee for human research (No. HE613041), will be utilized to gather data on rider behaviors in Khon Kaen City. Psychological factors related to RLR behavior will be explained through the Theory of Reasoned Action (TRA) and Human Error (HE) by utilizing the Structural Equation Model (SEM). The results indicated that the overall model could explain about 36% of the variance of rider’s behavior at a 95% confidence level. The outcomes can serve as an initial guideline for defining necessary traffic safety strategies to reduce serious injuries of motorcyclists in Khon Kaen City.

Development of MS Excel and Power BI Integrated Production Scheduling System for an MSME

Pranav Shivraj Patil — 2024-06-26

Industry 4.0, or I4.0, uses digitalization, blockchain technology (BCT), artificial intelligence (AI), and machine learning (ML) to improve supply chain responsiveness and efficiency while cutting costs. Production planning (PP) is emphasized in manufacturing, a critical stage of supply chain management (SCM). In order to meet changing customer demands and optimize manufacturing processes, researchers concentrate on creating customized PP modules for use within enterprise resource planning (ERP) systems. ERP modules support predictive analytics for ideal inventory levels, resource needs, and supply chain risks in addition to managing operations. However, it is financially difficult for micro, small, and medium enterprises (MSMEs) to implement a comprehensive ERP system. Implementing ERP in MSMEs for production scheduling is challenging due to time, information technology (IT) expertise, and cost constraints, especially for make-to-order (MTO) MSMEs. Microsoft Excel (MS Excel) and Power BI offer a better alternative with easier learning, customization, quicker implementation, and lower cost. This solution integrates both for efficient production scheduling and resource planning. A concurrent, adaptable PP system that integrates MS Excel and Power BI is suggested as a solution to this problem. Machine schedules and important performance indicators are projected onto an operational dashboard by this system, which is intended for a parallel machine environment. The objective is to find the best combination of shifts (s = 1 to 3) and machines (m = 1 to 6) for a workload through 18 simulations, helping planners to meet delivery deadlines. The PP system's ideal combination changes over the course of six weeks of simulations, from 1s-1m to 3s-5m to 2s-3m, demonstrating its flexibility in response to shifting production demands. Despite fluctuating workload over six weeks, (i) 92% orders met the 45-day lead time, (ii) plant ran continuously for a month (100% achievement), and (iii) visibility for stakeholder was enhanced with efficient resource planning and providing scope for further detailed analysis towards improving important performance indicators.

Optimal Frequency Control in Interconnected Power System using Grey Wolf Optimization and Firefly Algorithms

Swati Smaranika Mishra — 2024-06-26

The inclusion of renewable energy sources (RES) into electricity grids raises numerous concerns. Designing interconnected power networks with minimal frequency variations and tie-line power fluctuations has become a top priority. Due to the intermittent nature of renewable energy sources, power generator fluctuations depend on environmental circumstances. This work presents a unique hybrid Fractional Order Controller (FOC) adapted for load frequency control in interconnected power networks. The novel controller combines the advantages of two commonly used fractional-order proportional-integral-derivative (FOPID) and tilt-integral-derivative (TID) controllers. Grey Wolf Optimization (GWO) and Firefly Algorithm (FFA) techniques are used to determine the optimal controller parameters. Optimization of the different controller parameters of a three-area interconnected power system incorporating different types of renewable energy sources and loads is considered. The simulation results obtained were compared by incorporating FOPID, FOPIDTID with GWO, and FOPID-TID with FFA. It is observed that FOPID-TID with FFA gives better performance in terms of high mitigation of frequency fluctuations, tie-line power deviation, increased robustness and enhanced system stability over a wide range of parameters, uncertainty, and fast transient response.

A Mixture-of-experts Approach to Production Capacity Planning for Diverse Demand Patterns via Deep Reinforcement Learning

Thachanon Danket — 2024-06-26

Ensemble Learning is gaining traction in Reinforcement Learning (RL) due to its ability to improve performance, robustness, and capabilities of RL models. This paper addresses the challenge of production planning with fluctuating demand by proposing a novel Mixture of Experts Deep Reinforcement Learning (MoE-DRL) model. We leverage a combination of Proximal Policy Optimization (PPO), a powerful reinforcement learning algorithm, and Ensemble learning, a technique that combines multiple models. We propose a mixture of expert ensemble learning model that combine multiple expert PPO-DRL agents through a gating model (MoE PPO-DRL). The gating model learns to select the best expert agent for predicting the most suitable production plan for each situation's different demand patterns. The proposed model was trained and tested against the results obtained from the Mixed Integer Linear Programming model and the individual expert PPO agents. The MoE PPO-DRL model achieved a total average profit that was 25.9% higher than an average of all expert single-agent models. It also achieved a 11.02% optimality gap, which is significantly lower than the 22.93% average gap of all expert single-agent models.

Behavior of Concrete Columns Using High-Performance Concrete Mixed with Steel Fibers in Prefabricated Steel Tubes as Reinforcement

Piyaphol Srihabutra — 2024-06-26

This study investigated the behavior of reinforced concrete columns using high-performance concrete (HPC) filled in prefabricated steel tubes to serve as reinforcement. The experimental setup involved HPC with a compressive strength of 1,515 kg/cm², filled into steel tubes with diameters of ¾, 1, and 1¼ inches, replacing the reinforcement in concrete columns sized 150x150x600 mm. The concrete used for casting the sample columns had a compressive strength of 257 kg/cm². The columns were tested for axial and eccentric loading at displacements of 20, 40, and 60 mm to determine the maximum compressive strength, bending moment, interaction diagrams, and failure patterns of the steel-reinforced concrete columns. The experimental results showed that the axial compressive strength of columns using HPC in steel tubes as reinforcement was higher than that of the control concrete columns. The compressive strength at various eccentricities showed a slight increase compared to the control columns. The comparison between experimental compressive strengths and bending moments with calculated values indicated a consistent trend. Furthermore, the failure patterns of the concrete columns revealed both compression and tension failures.

Effect of Micro-silica and Crumb Rubber on Mechanical Properties of Concrete

Dhiraj Agrawal — 2024-06-26

Several researches conducted on rubberized concrete in the past but very few researchers explained the bonding effect of the waste tire rubber particles with different cementitious materials. In this research the effect of the fineness of the micro silica and its bonding with the rubber particles is studied. To check the influence of fractional replacement of cement using micro silica and discarded tire rubber as limited substitution of sand with providing pre-treatment to rubber particles, this experimental investigation has been conducted on concrete with varying percentage switch of cement using micro silica (SF), especially micro-silica was used in this study to exchange with cement by its weight. SF exchanged cement from 0% to 12% with an incremental ratio of 3%. Along with this, the sand was fractionally replaced using pretreated crumb rubber (CR) particles derived from end-of-life tires. The replacement ratio changed by 5% by the volume of sand, ranging from 0% to 20%. The pre-treatment process for CR was done using Sodium Hydroxide (NaOH) of 1 Molarity, for achieving good bonding between cementitious material paste and rubber particles. Distinct fresh and hydrated concrete properties were assessed and compared with normal concrete (NC) having 0% SF and 0% CR. 17 concrete mixtures in all, together with the control mix, were assessed in this study with varying amounts of SF and CR. The bonding structure and impermeability were enhanced by using SF in concrete with promising enhancement in mechanical strengths. Based on the outcomes obtained from this research, the optimal proportion for partial substitution of cement and sand by SF and CR was fixed for the desired grade of concrete to provide an innovative form of concrete to the construction industry.

Prediction of Expansion due to Sulfate of Ground Bottom Ash Mortar by an Artificial Neural Network

Thanawat Choksawangnetr — 2024-06-26

This paper presents the application of the artificial neural network model (ANN) to predict the expansion of ground bottom ash mortar due to sodium sulfate. Portland cement type I was replaced by ground bottom ash at ratios of 0, 10, 20, 30, 40, 50 and 60 percent by weight of binder. The expansion of mortar which immersed in 5% sodium sulfate at various ages was measured. To show the efficiency of the proposed model, the prediction results of the ANN model are compared with the multiple linear regression (MLR) and the multiple second order polynomial regression (MSPR) models through statistical values. From the prediction results, it was found that the ANN model has a very high expansion prediction accuracy and more effective than the MLR and MSPR. The ANN model has a statistical value of absolute variance higher than 0.99. Therefore, it is concluded that the ANN model has a strong prediction capability of expansion due to sulfate of ground bottom ash mortar.

Design Requirements for a Haptic-Assisted Hand Motor Training Systems in Stroke Rehabilitation: Insights from a Multidisciplinary Cohort

Natnicha Boriboon — 2024-06-26

Stroke survivors with disabilities must actively participate in targeted rehabilitation processes to recover their skills and prevent secondary impairments. Haptic technology offers the potential to restore motor functions by integrating visual perception and tactile sensation. However, designing a haptic-assisted hand motor training system for stroke patients poses significant challenges concerning how the program should be developed to achieve the most favorable rehabilitation outcomes. This research aimed to identify the essential design requirements tailored to the unique needs of stroke patients and their care providers, then develop a prototype hand motor training system. A diverse and inclusive cohort was selected for this study. The participants were provided with comprehensive details, ensuring a clear understanding of the objectives. In-depth interviews were conducted to gather valuable insights, which were then summarized and used as the foundation for developing the proposed rehabilitation system. The results highlighted integrating training games with a variety of difficulty levels, and hand-motor functions. The findings provide valuable guidance that could enhance the rehabilitation experience and improve patient outcomes. Moreover, the prototype system developed from these human needs could also be used for real-time measurement, thus facilitating the uncomplicated and rapid evaluation of post-training patients.

IoT-Driven Soil Moisture Monitoring in Organic Rice Cultivation

Phairoj Samutrak — 2024-06-26

This research article explores the Internet of Things (IoT) in organic agriculture, focusing on data monitoring. The IoT devices and connected technologies have created new opportunities for agricultural providers to monitor and gather data across traditional farm settings. This study presents the development of a custom IoT device equipped with various sensors, air temperature, humidity and soil moisture sensors, to continuously monitor hydrology and soil data. Data collected from these devices are securely transmitted to Google sheet file and NETPIE2020 for real-time analysis. The article discusses the technical aspects of the IoT device, the data transmission and storage infrastructure and the implementation algorithms for predictive soil moisture data. Furthermore, the results of a pilot study in which the IoT device was tested on a group of organic agriculture. The study demonstrates the potential of IoT in improving outcomes, reducing time and enhancing the quality of data through continuous remote monitoring. The findings of this research suggest that IoT has the potential to revolutionize the agriculture industry by providing proactive and personalized solutions. The results efficiency evaluation results by 5 experts show that a total mean of 3.86 and a standard deviation of 0.30. Our study optimal soil moisture levels for rice cultivation in Chainat Province, providing guidelines for maintaining these levels to maximize yield. Furthermore, the research extends the data collection period to analyze the impact of temperature and humidity on rice growth, valuable insights into how these environmental factors interact with soil moisture to influence crop health (30-40%). The practical benefits and effectiveness of the IoT system, user-friendly guidelines and tools are developed to support farmers in making data-driven decisions, ensuring successful adoption and utilization of the technology.

The MIMO-OTFS Technique in the Next 6G Communications

Ali J. Ramadhan — 2024-06-26

The orthogonal time-frequency space (OTFS) technique is a promising method of waveform modulation that encodes data in the delay-doppler (DD) domain. OTFS distinguishes itself from conventional multiplexing methods by employing two-dimensional modulation to alternate between the time-frequency (TF) domain and the delay-Doppler domain. This feature enables the management of Doppler shifts induced by high-speed objects, which is not possible with conventional modulation schemes such as orthogonal frequency division multiplexing (OFDM). The main objective of this work is to provide a concise and comprehensive introduction of this emerging subject, emphasizing its system concept. In addition, we analyze crucial elements of OTFS modulation, including techniques for data detection, channel estimation, MIMO, and multiuser systems.

A Comprehensive Review of Utilization of Construction and Demolition Waste as Fine Aggregate in Concrete

Atul S. Kurzekar — 2024-06-26

In the dynamic landscape of the Asian region, particularly in India, a lot of waste is produced through construction and demolition operations, such as masonry dust and concrete debris. Growing interest in recycling and reusing these materials has arisen in response to environmental concerns around trash disposal. One possible way to repurpose this material is to utilize Construction and Demolition Waste (CDW) as a fine aggregate in concrete. Most researchers studying natural fine aggregate and its properties also study query dust as a fine aggregate but very little research work for fine aggregate produced from CDW. This review paper intends to offer a thorough analysis of the literature in this area, emphasizing the components and mechanical characteristics of concrete containing CDW. The review begins by discussing the sources and composition of C&D waste, highlighting the diverse range of materials that can be repurposed, including crushed concrete, brick, ceramic, and asphalt. Various techniques for processing and preparing C&D waste for incorporation into concrete are explored, emphasizing the importance of proper sorting, cleaning, and size reduction to ensure compatibility and quality. Furthermore, this research evaluates the mechanical, durability, and sustainability aspects of concrete containing C&D waste as fine aggregate. Studies indicate that while including C&D waste may slightly reduce compressive strength, it often enhances flexural strength and mitigates the adverse effects of shrinkage and cracking. Additionally, concrete with C&D waste exhibits comparable or even superior durability performance, attributed to the pozzolanic and filler impact of the recycled materials.