18 research outputs found
Design of Experiment for Predicting Residual Stresses in Gas Tungsten Arc Welding Process
No full textEffect of forming conditions of poly-lactic acid/hydroxyapatite to tensile strength of canine bone fixation plate using full factorial experimental design
No full textProblems of using metallic bone fixation plates for canine lead to the idea of the replacement with biocomposite materials. In this research, polylactic acid (PLA) blends with hydroxyapatite (HA) powder 5-15% was used as biocomposite material. The specimens were formed by hot compression molding and the experiment in investigating suitable forming condition was based on the 23 full factorials with the center point design of experiment. There are many types of research attempting to develop a prototype of bone fixation plates from a substitute material including forming conditions, in order to obtain the effective bone fixation plate to use. This paper presents the tensile strength for the proposed biocomposite bone fixation plates. After the mechanical testing on various conditions, the tensile strength results ranged from 40 to 60 MPa and it showed that the higher HA ratio had significant effect to the decrease of the tensile strength. This preliminary experiment reveals the tensile strength of forming conditions of PLA/HA composite, which may indicate a direction for improving the better mechanical properties of the bone fixation plates
IoT-Integrated Machine Learning for Precision Watering in Bamboo Mushroom Farming
No full textThis study offers a method for enhancing bamboo mushroom farming. We aim to increase productivity by combining machine learning methods for device-level computing with the Internet of Things (IoT). The first step is to record the ideal environmental conditions in the bamboo mushroom greenhouse. The IoT devices collect data on temperature, humidity, soil moisture, and water usage, storing it in the cloud. The regression model is then formulated for irrigation control and predicting water consumption in the bamboo mushroom farm. Later, the microcontroller is programmed to control the water pump in a systematic manner to release water. The study found that temperature, soil moisture, and relative humidity are the primary factors affecting water content. The proposed method increased mushroom volume by 46.67% and saved 22% of water usage, demonstrating the successful integration of machine learning into smart farming at the device level
Estimation of microbial load in Ganoderma lucidum using a solar-electric hybrid dryer enhanced by machine learning and IoT
No full textThis study focuses on developing a hybrid-powered dryer that uses both solar and electric energy to dry Ganoderma lucidum mushrooms. Integrated with an Internet of Things (IoT) platform, the system enables real-time monitoring of temperature, time, and humidity. The analysis evaluated reductions in weight, moisture content, water activity, and microbial counts (bacteria, fungus, and yeast) across temperatures ranging from 40 °C to 80 °C over 480 min. The results indicated that higher temperatures, particularly 80 °C, were most effective in reducing microbial counts, achieving near-zero levels after 240 to 480 min. Machine learning (ML) models random forest regression (RFR), decision tree regression (DTR), and multiple linear regression (MLR) were trained to estimate microbial levels based on input variables such as time, temperature, and weight. RFR had the highest accuracy for estimating bacteria, while DTR excelled for fungus and yeast. However, MLR proved most suitable for IoT applications due to its simplicity in real-time implementation on devices. Therefore, the ML models were selected based on accuracy performance (RFR and DTR) and ease of integration into IoT systems (MLR). This study demonstrates the hybrid dryer's efficiency and the potential of ML models to optimize the drying process, contributing to energy efficiency and product quality control. Initially designed for small-scale on-farm use, the system also has the potential for future scaling to industrial processing facilities
Effect of Back Plate Preheating Assistance System and Deep Rolling Process on Microstructure Defects and Axial Force Reduction of Friction Stir Welded AA6061 Joint
No full textThis study investigates the effects of a back plate preheating assistance system and deep rolling (DR) on axial force and tunnel defects during friction stir welding (FSW). Different preheating configurations—advancing side (AS), retreating side (RS), and both sides—were examined to evaluate their impact on axial force reduction, temperature distribution, and defect minimization. Axial force measurements were taken using a dynamometer, and temperature histories were recorded with a thermal camera. The results demonstrate that a preheating temperature of 200 °C is optimal, reducing axial force by 30.24% and enhancing material flow. This temperature also facilitated deeper tool penetration, especially when preheating was applied to both sides. Preheating on the AS resulted in the smallest tunnel defects, reducing defect size by 80.15% on the RS and 96.91% on the AS compared to the non-preheated condition. While DR further reduced tunnel defects, its effectiveness was limited by the proximity of defects to the surface. These findings offer significant insights for improving the FSW process
Finite Element Analysis of Traditional and New Fixation Techniques of the 3D-Printed Composite Interlocking Nail in Canine Femoral Shaft Fractures
No full textSince the removal of a metallic interlocking nail system leaves a blank cavity inside a healed bone, bioactive and biodegradation materials have been used instead to induce bone formation and eliminate complications of the material removal procedure. The previous study presented the possibility of an interlocking nail fabrication from polylactic acid (PLA), polycaprolactone (PCL), and hydroxyapatite (HA) using 3D printing, namely fused filament fabrication (FFF), for canine diaphyseal fractures. Therefore, a finite element analysis (FEA) was used to predict the maximum principal stress of this 3D-printed composite interlocking nail to stabilize a canine femoral fracture, and the biomechanical performance was evaluated for the treatment of canine femoral shaft fractures using both traditional and new fixation techniques. Three-dimensional FEA models were created, and the composite interlocking nail was tested for implant strength and stability. Three types of canine femoral shaft fracture (proximal shaft fracture, middle shaft fracture, and distal shaft fracture) fixed by traditional and new fixation techniques, consisting of two, four, and six locking screws, were analyzed with a multilevel factorial design technique. The maximum principal stresses of the composite interlocking nail were compared with each fixation technique. According to the multilevel factorial design, gap type, fracture gap, and fixation techniques are factors that affect the maximum principal stress of the composite interlocking nail for two and four locking screws. For six locking screws, all factors, including gap type, fracture gap, nail length, and fixation techniques, significantly affect the maximum principal stress. The use of a 3D-printed composite interlocking nail system with new fixation techniques demonstrated lower maximum principal stresses than the interlocking nail system that used a traditional fixation technique. The results of this study could help orthopedic veterinary surgeons to understand the biomechanical performances of traditional and new fixation techniques. Furthermore, surgeons may use the numerical results of this analysis to choose a fixation technique based on a patient’s condition
The Educational Process of the Work-integrated Learning (WIL) program: Case Study of Engineering Faculty, Rajamangala University of Technology Lanna, Thailand
Get PDFThis research studies the educational process of the Work-integrated Learning (WIL) program in a case study of engineering faculty, at Rajamangala University of Technology Lanna (RMUTL), Thailand. The opinions of students and teachers who participated in the WIL program are investigated. In this regard, during the academic year 2020-2022, we applied for the "The Questionnaire about the Educational Process in WIL program". The questionnaire was sent to a sample of 120 students and 50 teachers. The subjects answered to questions regarding the roles of students, teachers, factory trainers, the quality of curricula, the effectiveness of teaching methods, the establishment, and university and executive support, in the WIL program. The conducted study shows the necessity of developing WIL curricula and strengthening a modern WIL learning process. WIL had an impact on students\u27 increased responsibility and organizational communication. Teachers and factory trainers were obliged to have integrated performance of their teaching skills. New courses should be created expressly for WIL to reduce study time and interact with the workplace. The WIL model should also be improved student learning effectiveness. To effectively handle and administer the project with the backing of the executive\u27s policy, the administrative framework and structure should be established
Optimized Travel Itineraries: Combining Mandatory Visits and Personalized Activities
No full textTourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model
Optimizing Tourist Destination Selection Using AHP and Fuzzy AHP Based on Individual Preferences for Personalized Tourism
No full textTourism is a dynamic industry that significantly contributes to the global economy, driven by the increasingly diverse preferences of tourists. Addressing these preferences requires sophisticated decision-making models capable of handling the uncertainty and subjectivity of human judgments. This study proposes sustainable models for effectively capturing and evaluating individual tourist preferences using the Analytic Hierarchy Process (AHP) and the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). These models leverage the strengths of the AHP to construct a flexible decision-making framework that adapts to diverse tourist preferences, offering personalized recommendations. In this study, three main criteria are considered: types of tourism, tourism facilities, and tourism areas. Tourists are encouraged to provide their preferences for these criteria and sub-criteria, enabling the AHP and Fuzzy AHP to recommend suitable destinations. An analysis was conducted with 30 respondents providing pairwise comparisons of the tourism criteria, which were then used to generate tourist attraction recommendations using both the AHP and Fuzzy AHP. The study assessed respondents’ satisfaction with the recommendations, finding that both methods were effective, with a slight preference for the Fuzzy AHP due to its ability to better capture individual preferences. The results underscore the potential of these models in sustainably enhancing decision support systems in the tourism industry, offering tailored recommendations that align more closely with tourist expectations
Deep Rolling Process Modeling Using Finite Element Analysis in Residual Stress Measurement on Rail Head UIC860 Surface
No full textThis study investigates the effects of deep rolling parameters, pressure, speed, and offset, on the residual stress distribution and material deformation in UIC 860 Grade 900A railway rails. We will model deep rolling to simulate the process and predict the residual stress profile in railway rails. Subsequently, we will rigorously compare and analyze the FEM simulation results with experimental data to optimize deep rolling parameters for improved residual stress distribution. Using both experimental methods and finite element analysis via ANSYS 2023 R1, the study varied deep rolling parameters. Experimental deep rolling pressure was set at 150 bar, speed at 1800 mm/min, and offset at 0.1 mm, while FEA simulations predicted corresponding pressures of 157 bar and speed of 1796.52 mm/min. These parameter settings were chosen to induce significant surface compressive stresses that could enhance the material’s mechanical performance. The experimental results showed an average compressive residual stress of 498.9 MPa, closely aligning with the FEA-predicted value of 502.5 MPa. A paired t-test revealed no statistically significant difference between the two results, with a T-value of −0.22 and a p-value of 0.833, validating the reliability of the FEA model. The consistent deformation observed in both experimental and FEA simulations, especially with a 0.1 mm offset, confirmed that the rolling parameters were effective in producing uniform stress distribution, albeit with a slightly extended processing time due to the small offset. Overall, the findings confirm that optimizing the deep rolling parameters of pressure, speed, and offset leads to favorable residual stress distributions and improved material properties. The results indicate that FEA is a reliable tool for predicting the outcomes of deep rolling, and this study provides a strong foundation for further refinement of the process to enhance performance in practical applications, such as railway rail treatments
