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The Dean of the Faculty receives the Asyut Robotics team

The Dean of the Faculty received the "Assiut Robotics" team following their win of the "Best Mechanical Design" award at the *Minesweepers: Towards a Landmine-Free World* competition, held at Zewail City of Science and Technology.

Professor Khaled Salah, Dean of the Faculty, welcomed the Assiut Robotics team after their outstanding participation in the 2026 edition of the international *Minesweepers: Towards a Landmine-Free World* competition. The event took place on October 17, 2026, at Zewail City of Science and Technology, where the team secured the "Best Mechanical Design" award, outperforming all other participating teams in terms of engineering and design.

Also present to welcome the team were Professor Mazen Abdel-Salam, the team’s scientific supervisor; Dr. Mohamed Eid Abdel-Mawla, supervisor of scientific activities at the Faculty; and Mr. Mohamed Sharaqa, Director of the Student Welfare Department.

The competition is considered one of the most prominent engineering forums dedicated to the development of robots for landmine detection and removal.

The robot was designed to navigate rugged terrain, detect both surface and buried landmines, map their locations, and subsequently retrieve them—all through an integrated engineering system.

The Dean of the Faculty congratulated the team and their supervisors on this remarkable achievement, wishing them continued success in future endeavors and competitions.

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The graduation project "Smart Automated Plastic Recycle System" (SAPRS) has won the Sustainability and Localization Award.

The graduation project, "Smart Automated Plastic Recycle System (SAPRS)," has won the "Sustainability & Localization Award" at the "Egypt Makes 2026" competition. The project focuses on recycling plastic waste to produce sustainable interlocking paving blocks.

A team from the Faculty of Engineering at Assiut University—supported by the Academy of Scientific Research and Technology—successfully designed and manufactured a smart machine that converts plastic waste into high-durability interlocking tiles. The project, developed for the 2025–2026 academic year, integrates artificial intelligence, the Internet of Things (IoT), and Fourth Industrial Revolution technologies.

Named "Eco Lock," the project is an outcome of the "Egypt Makes" (Misr Tasna') alliance, backed by the "My Project, My Beginning" (Mashrou'i Bidayati) initiative. It aims to recycle plastic waste into sustainable construction materials, thereby helping to reduce pollution and maximize resource utilization.

Preliminary tests have demonstrated that the interlocking tiles—made from recycled plastic—can withstand compressive loads of up to 8 tons, confirming their efficiency and suitability for a wide range of applications.

The project was developed by the following graduates:

Eng. Pavly Magdy Zakher

Eng. Walid Khaled Mustafa

Eng. George Raef Sami

Eng. Mira Tharwat Girgis

Eng. Samuel Raafat Kamal

Eng. Mina Habib Bakhit

Eng. Yustina Naseef Samir

Eng. Anton Natai Tawfiq

Eng. Bishoy Bahaa Botros

Eng. Fadi Mamdouh El-Gamil

Eng. Mohamed Omar Badri Bishoy Rifaat Methenni

Eng. Mostafa Hammam Hassan

Eng. Bahaa Romani Ayoub

Eng. Nader Samir Mahawad

The project was supervised by Prof. Ahmed Saad Ali, Dr. Gamal Abdel Nasser, and Dr. Abdullah Farraj Abdel Rahim, with Dr. Ahmed Mohamed Attia serving as the team’s advisor.

For his part, Prof. Khaled Salah, Dean of the Faculty, congratulated the graduates who developed the project, as well as their supervisors, on receiving this award. He noted that the achievement reflects the high quality of the educational process at the Faculty, as well as the capabilities and skills of its students and graduates. He also extended his gratitude to the university administration—led by the University President, Prof. Ahmed Abdel Mawla—for his support of the educational process and for encouraging student participation in various local and international forums and competitions.

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Development of an Accelerated Thermal Cycling Test Setup and Machine-Learning-Based Lifetime Prediction of IGBT Modules in Inverter Operation

Research Abstract

The reliability of Insulated Gate Bipolar Transistor (IGBT) modules is critical for long-term durability and operational stability in power electronic systems. This paper presents a real-time degradation-monitoring and lifetime-prediction framework for IGBT modules using accelerated power-cycling tests under realistic inverter conditions. A two-level SVPWM inverter is used, with electrically driven current loading and developed induction heating, along with an active water-cooling system, to create precise junction-temperature swings to investigate thermal fatigue. Continuous acquisition of on-state voltage and temperature data is performed throughout the aging of six IGBT modules under two junction-swing conditions. The degradation behavior is examined through experimentally observed trends and used to develop a machine-learning model based on Random Forest (RF) to predict the remaining useful lifetime (RUL) utilizing the extracted degradation indicators. A comparative analysis with a Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models is performed to validate the RF model’s effectiveness, confirming that the RF model has greater accuracy, robustness, and computational efficiency with limited datasets. The proposed framework thus integrates experimental testing, and data-driven prediction, attaining lifetime-prediction errors under 4% while utilizing only 20–60% of the lifetime data, offering a precise and scalable method for assessing inverter-level IGBT reliability.

Research Authors
Ahmed H Okilly, Cheolgyu Kim, Seungdeog Choi, Jeihoon Baek
Research Date
Research Department
Electrical Engineering Department
Research Journal
IEEE Journal of Emerging and Selected Topics in Power Electronics
Research Member
Ahmed Hassan Hamed okilly
Research Publisher
IEEE
Research Rank
International Journal, Q1 WOS, Q1 Scopus, IF 5.7
Research Website
https://ieeexplore.ieee.org/abstract/document/11481045
Research Year
2026
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