Designing advanced printed circuit boards for industrial applications involves a variety of steps and methods depending on each company. From what I have learned, I have used Cadence Allegro to design printed circuit boards. This internship was designed on a variety of boards with varying levels of difficulty. Learning in this internship could not be learned in detail within the university. I had to work with many departments within Analog Devices (Thailand) Company. This design was assisted by a mentor who took care of and taught me the work, allowing me to complete the co-operative successfully.
โครงงานนี้ออกแบบมาเพื่อศึกษา หาประสบการณ์ในการทำงานและการใช้ชีวิตวัยทำงาน โดยเป็นการทำโครงการสหกิจศึกษาเพื่อเรียนรู้ถึงกระบวนการทำงานของแผนก Global Hardware Development ของ บริษัท Analog Devices (Thailand) และได้ทดลองปฏิบัติงานจริงในโรงงานอุตสาหกรรม กระบวนการทุกอย่างตั้งแต่เริ่มแรกจนกระทั่งออกมาเป็นผลิตภัณฑ์สำหรับการทดสอบ IC ใน Final Test และ ทดสอบ wafer ใน Wafer Sort โดยฝ่ายที่เข้าไปทำสหกิจในบริษัทคือ PCB Layout Design และได้ออกแบบผ่านโปรแกรม Cadence Allegro
คณะวิศวกรรมศาสตร์
This cooperative education project aims to enhance the efficiency of Hydrogen Manufacturing Unit 2 (HMU-2) and Pressure Swing Adsorption 3 (PSA-3) by using AVEVA Pro/II process modeling and a Machine Learning model for process simulation. The study found that the AVEVA Pro/II model predicted outcomes with deviations ranging from 0–35%, including a hydrogen flow rate deviation from the PSA unit of 12%, exceeding the company’s acceptable limit of 10%. To address this, a Machine Learning model based on the Random Forest algorithm was developed with hyperparameter tuning. The Machine Learning model demonstrated high accuracy, achieving Mean Squared Errors (MSE) of 8.48 and 0.18 for process and laboratory data, respectively, and R-squared values of 0.98 and 0.88 for the same datasets. It outperformed the AVEVA Pro/II model in predicting all variables and reduced the hydrogen flow rate deviation to 4.75% and 1.35% for production rates of 180 and 220 tons per day, respectively. Optimization using the model provided recommendations for process adjustments, increasing hydrogen production by 7.8 tons per day and generating an additional annual profit of 850,966.23 Baht.
คณะวิศวกรรมศาสตร์
Artificial intelligence for agriculture and environment is a collection of significant models for enviromental friendly Thailand development. The models create with machine learning and deep learning by Near infrared spectroscopy research center for agricultural and food products, including: Determining the nutrient needs (N P K) of durian trees by measuring durian leaves using a non-destructive technique using artificial intelligence, Identification of combustion properties of biomass from fast-growing trees and agricultural residues using non-destructive techniques combined with artificial intelligence, and Evaluation of global warming due to biomass combustion using non-destructive techniques using artificial intelligence. The basic technology used is Near infrared Fourier transform spectroscopy technology which measurement and output display can be done quickly without chemical, no requirement for special expert, and measurement price per sample is very low. But the instrument cannot be produced in Thailand.
คณะเทคโนโลยีการเกษตร
The project focuses on designing and creating Art Toy Mascots that reflect the identities of the 12 departments in the Faculty of Agricultural Technology. It combines the concepts of art and agricultural technology to promote better understanding and easy recognition of each department. The project utilizes creative design and artistic toy production techniques.