Dr. Hassan Alqahtani | AI in manufacturing | Best Researcher Award

Dr. Hassan Alqahtani, Taibah Uinversity, Saudi Arabia

👨‍🎓 Hassan Hussain Alqahtani is an Assistant Professor specializing in Mechanical Engineering at Taibah University. He holds a Ph.D. in Mechanical Engineering and MSc degrees in both Mechanical and Electrical Engineering from Penn State University, earned in 2021. Prior to his academic career, Hassan gained valuable industry experience at ARAMCO and SABIC, where he worked on utility projects, inspection engineering, maintenance, and project construction evaluation. His research interests focus on fatigue damage detection and risk assessment in mechanical structures, with a particular emphasis on employing neural networks and ultrasonic testing techniques. Hassan has authored several notable publications in respected journals such as Engineering Failure Analysis and Machines. He has also received recognition for his academic achievements, including graduation honors from King Abdulaziz University for his BSc.

🌐 Professional Profiles :

Google Scholar

Scopus

🎓 Education:

📚Ph.D. in Mechanical Engineering, Penn State University, 2021 📚MSc in Electrical Engineering, Penn State University, 2021 📚MSc in Mechanical Engineering, Penn State University, 2016 📚BSc in Mechanical Engineering, King Abdulaziz University, 2010

📅 Academic Experience:

• Assistant Professor, Taibah University, 2021 – present
• Lecturer, Taibah University, 2018 – 2021
• Teaching Assistant, Taibah University, 2013 – 2018

🏢 Non-Academic Experience:

• Trainee, ARAMCO, 2008
• Inspection Engineer, SABIC, 2010-2011
• Maintenance Representative, SABIC, 2011-2012
• RFTR Leader, SABIC, 2012-2013

🏅 Honors and Awards:

• Graduation Honor of B.S from King Abdulaziz University

📝 Principal Publications and Presentations of the Last Five Years:

1. Classification of fatigue crack damage in polycrystalline alloy structures using convolutional neural networks. Engineering Failure Analysis, 2021.
2. Neural Network-Based Automated Assessment of Fatigue Damage in Mechanical Structures. Machines, 2020.
3. Forecasting and Detection of Fatigue Cracks in Polycrystalline Alloys with Ultrasonic Testing via Discrete Wavelet Transform. ASME J Nondestructive Evaluation, 2021.
4. Analysis of Fatigue Crack Evolution using In-Situ Testing.
5. Fatigue Damage Detection and Risk Assessment via Wavelet Transform and Neural Network Analysis of Ultrasonic Signals. Fatigue & Fracture of Engineering Materials & Structures, 2021.
6. Detection and Classification of Fatigue Damage in a Neural-Network Setting. Neural Computing and Applications, 2021.
7. Convolutional Neural Network for Risk Assessment in Polycrystalline Alloy Structures via Ultrasonic Testing. Structural Health Monitoring, 2021.

📚 Publication Impact and Citations :

Scopus Metrics:

• 📝 Publications: 11 documents indexed in Scopus.
• 📊 Citations: A total of 49 citations for his publications, reflecting the widespread impact and recognition of Dr. Hassan Alqahtani’s research within the academic community.

Google Scholar Metrics:

• All Time:
  • Citations: 63 📖
  • h-index: 4 📊
  • i10-index: 3 🔍
• Since 2018:
  • Citations: 63 📖
  • h-index: 4 📊
  • i10-index: 3 🔍

👨‍🏫 A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. 🌐🔬

Publications Top Notes :

1. Publication: “Classification of fatigue crack damage in polycrystalline alloy structures using convolutional neural networks”
   • Authors: H. Alqahtani, S. Bharadwaj, A. Ray
   • Journal: Engineering Failure Analysis
   • Published Year: 2021
   • Cited by: 32 Citations
2. Publication: “Appealing perspectives of the structural, electronic, elastic and optical properties of LiRCl 3 (R= Be and Mg) halide perovskites: a DFT study”
   • Authors: N. Rahman, M. Husain, V. Tirth, A. Algahtani, H. Alqahtani, T. Al-Mughanam, …
   • Journal: RSC advances
   • Published Year: 2023
   • Cited by: 11 Citations
3. Publication: “Neural network-based automated assessment of fatigue damage in mechanical structures”
   • Authors: H. Alqahtani, A. Ray
   • Journal: Machines
   • Published Year: 2020
   • Cited by: 10 Citations
4. Publication: “Forecasting and detection of fatigue cracks in polycrystalline alloys with ultrasonic testing via discrete wavelet transform”
   • Authors: H. Alqahtani, A. Ray
   • Journal: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of …
   • Published Year: 2021
   • Cited by: 4 Citations
5. Publication: “Feature extraction and neural network-based fatigue damage detection and classification”
   • Authors: H. Alqahtani, A. Ray
   • Journal: Neural Computing and Applications
   • Published Year: 2022
   • Cited by: 2 Citations