Journal of Industrial Technology Buriram Rajabhat University

Development of a Guiding Cane for the Safety of the Visually Impaired

2025-11-29T13:47:35+07:00

The research titled Guiding Cane for the Safety of the Visually Impaired aims to design and develop a cane capable of accurately detecting obstacles by integrating microcontroller technology with ultrasonic sensors, a vibration system, and a buzzer. The system is designed to enhance safety and confidence for visually impaired individuals during mobility. The Arduino Uno R3 board serves as the main controller for signal processing and operation of the alert components.

Experimental testing of the ultrasonic sensor was conducted 30 times. The results showed that the sensor successfully detected obstacles in every trial, achieving 100% accuracy, with both sound and vibration alerts responding effectively. These findings indicate that the developed cane performs efficiently, demonstrating high stability and reliability suitable for real-world applications in assisting the visually impaired.

In conclusion, the developed guiding cane successfully meets the research objectives in terms of design, functionality, and performance. Future improvements may include integrating additional sensors for omnidirectional detection, enhancing the alert system to distinguish obstacle distances more precisely, and incorporating technologies such as GPS or wireless connectivity to further increase usability and improve the quality of life for visually impaired individuals.

Development of a Cashew Nut Shelling and Size Grading Machine for Loss Reduction

2025-11-12T22:38:45+07:00

This study investigates the factors influencing the waste generation rate in the cashew nut peeling process using a newly developed peeling and size-grading machine aimed at loss reduction. The experimental samples consisted of raw cashew nuts classified into three quality grades, namely Grade AA, Grade A, and Grade B. For each grade, a total of 2,160 nuts were tested, derived from a designed experimental scheme comprising 18 experimental conditions with six replications per condition and 20 nuts per replication. The examined operational variables included blade clearance, slider speed, and lower herringbone blade angle. The experimental design was based on the design of experiments (DOE) approach, and the collected data were statistically analyzed using two-way analysis of variance (Two-Way ANOVA) to evaluate the main effects and interaction effects of the operating parameters on waste generation. When statistically significant differences were detected, simple main effect and interaction analyses were subsequently performed.

The results indicate the factors influencing the waste generation rate in the cashew nut peeling process by examining key operational variables, including the blade clearance, slider speed, and herringbone angle. The results indicate that blade clearance and slider speed consistently affect the amount of waste across all grades in the same direction, whereas the lower herringbone angle does not have a statistically significant effect on waste generation. Performance evaluation of the peeling machine shows that Grade AA yields 16.12 kg, comprising 12.76 kg of whole kernels and 3.36 kg of broken kernels. When extrapolated to an 8-hour operating period, the total output reaches 128.96 kg, of which 102.08 kg are whole kernels, corresponding to 79.15%, which is classified as good quality. For Grade A (three channels), the machine produces 24.18 kg, including 19.80 kg of whole kernels and 4.38 kg of broken kernels. Over 8 hours, the total production amounts to 193.44 kg, with 158.40 kg of whole kernels, representing 81.88% and indicating excellent performance. Similarly, Grade B (three channels) achieves a total output of 193.44 kg over 8 hours, yielding 153.12 kg of whole kernels, or 79.15%, which is considered good quality. Comparative analysis reveals that Grades A and B achieve higher total production than Grade AA due to a greater number of sliding channels. However, Grade A provides a higher proportion of whole kernels and a lower fraction of broken kernels compared with Grade B. These results demonstrate that Grade A offers the best overall production efficiency for the cashew nut peeling machine investigated in this study.

Design and Implementation of a Prototype Quadcopter UAV for Survey and Rescue

2025-12-16T19:45:23+07:00

This article aims to (1) design and develop a prototype quadrotor unmanned aerial vehicle (UAV) and (2) evaluate the performance of the developed UAV for surveillance and life-ring deployment in rescue operations. The research process begins with a study of the operating principles, structure, and components of quadrotor UAVs, which are then applied to the design process. Emphasis is placed on weight control, appropriate material and component selection, and the design of a radio-control flight system integrated with onboard software to enable efficient takeoff and landing, directional control, and autonomous flight along predefined routes. After the prototype was constructed, flight tests were conducted under various conditions, including hovering flight, translational flight, and life-ring deployment tests.

The results show that the developed quadrotor UAV performs well in open-area flight conditions and is capable of transmitting real-time visual data, as well as altitude and speed information, effectively. The UAV can hover for approximately 10 minutes and perform translational flight for about 6 minutes, with a flight range exceeding 500 meters, meeting the specified design requirements. In addition, the UAV is able to deploy the life ring accurately. These findings demonstrate that the proposed UAV prototype has strong potential for application in future surveillance and rescue missions.

Design and Development of a Workpiece Inspection System Using Artificial Intelligence on the CiRA CORE Platform

2025-12-25T17:57:53+07:00

The purposes of this study to develop an automated work piece detection system on a conveyor belt using digital image processing techniques with a camera, fulfilling specific operational conditions. The system design was implemented using the CiRA CORE software, and artificial intelligence (AI) was utilized for object detection. The camera captures image signals, and the system is tested for detecting three types of work pieces: Bolt, Nut, and Washer test results showed that when the work pieces were placed normally, the model achieved a 100% detection rate. However, in cases where work pieces were stacked or overlapped, the model was unable to detect them with 100% accuracy. The detection accuracy is directly correlated with the amount of training data; a larger dataset improves the model's precision.