The railway brake system usually uses compressed air brake system which uses high-pressure air to press the brake shoe on the surface of the wheel to reduce the speed of the train. Repeated friction generates heat at the contact surface, increasing thermal stress on the cast iron brake shoe. The purpose of this study is to investigate the thermal stress on a prototype of cast iron brake shoe using the finite element method compare the analytical results to the actual brake shoe and redesign a brake shoe prototype to reduce thermal stress. Based on the results of the thermal stress study using the finite element method, it has shown that the location of the thermal stress on the prototype brake shoe according to the location of the crack on the real brake shoe. The brake shoe's design which includes single notch in the center of brake shoe which is can help to reduce thermal stress. The results from this study should be validated with the results from the field test to evaluate both of thermal distribution and braking efficiency in term of braking distances as well.
ระบบเบรกของรถไฟนิยมใช้ระบบเบรกแบบลมอัด โดยใช้อากาศแรงดันสูงกดแท่งห้ามล้อไปสัมผัสกับผิวของล้อเพื่อลดความเร็วของรถไฟ เมื่อเกิดการเสียดสีกันซ้ำ ๆ จึงเกิดความร้อนขึ้นบริเวณผิวสัมผัส ทำให้เกิดความเค้นสะสมเนื่องจากความร้อนบนแท่งห้ามล้อวัสดุเหล็กหล่อ งานวิจัยนี้จึงมีวัตถุประสงค์เพื่อศึกษาความเค้นเนื่องจากความร้อน (Thermal stress) บนแท่งห้ามล้อวัสดุเหล็กหล่อรูปแบบต้นแบบด้วยระเบียบวิธีไฟไนต์เอลิเมนต์เพื่อเปรียบเทียบผลการวิเคราะห์ที่ได้กับแท่งห้ามล้อชิ้นงานจริง และออกแบบแท่งห้ามล้อวัสดุเหล็กหล่อในรูปแบบใหม่เพื่อลดความเค้นเนื่องจากความร้อนที่เกิดขึ้น
คณะแพทยศาสตร์
This study explores the application of deep convolutional neural networks (CNNs) for accurate pill identification, addressing the limitations of traditional human-based methods. Using a dataset of 1,250 images across 10 household remedy drugs, various CNN architectures, including YOLO models, were tested under different conditions. Results showed that natural lighting was optimal for imprinted pills, while a lightbox improved detection for plain pills. The YOLOv5-tiny model demonstrated the best detection accuracy, and efficientNet_b0 achieved the highest classification performance. While the model showed strong results, its generalization is limited by sample size and drug variability. Nonetheless, this approach holds promise for enhancing medication safety and reducing errors in outpatient care.
คณะเทคโนโลยีการเกษตร
Durian is a crucial economic crop of Thailand and one of the most exported agricultural products in the world. However, producing high-quality durian requires maintaining the health of durian trees, ensuring they remain strong and disease-free to optimize productivity and minimize potential damage to both the tree and its fruit. Among the various diseases affecting durian, foliar diseases are among the most common and rapidly spreading, directly impacting tree growth and fruit quality. Therefore, monitoring and controlling leaf diseases is essential for preserving durian quality. This study aims to apply image analysis technology combined with artificial intelligence (AI) to classify diseases in durian leaves, enabling farmers to diagnose diseases independently without relying on experts. The classification includes three categories: healthy leaves (H), leaves infected with anthracnose (A), and leaves affected by algal spot (S). To develop the classification model, convolutional neural network (CNN) algorithms—ResNet-50, GoogleNet, and AlexNet—were employed. Experimental results indicate that the classification accuracy of ResNet-50, GoogleNet, and AlexNet is 93.57%, 93.95%, and 68.69%, respectively.
คณะวิศวกรรมศาสตร์
SecurionSphere is the penetration testing learning platform that focuses on web application exploitation. This platform is intended to address concerns seen in existing penetration testing platforms, such as resource sharing that may affect other users and the constant environment configuration the permits the same response leading to copy the answer from others. Supervisors can use templates to address various forms of web application vulnerability threats. Users can generate the instance of supervisor's templates machine. The platform also randomly generates the environment configuration for each machine has the difference environment and the answer. This allows the users get more realistic learning experiences without affecting the resources of others.