Arun Nandagopal | Automation in Manufacturing | Best Researcher Award

Posted on by ScienceFather

Mr. Arun Nandagopal | Automation in Manufacturing | Best Researcher Award

Mr. Arun Nandagopal, Fives group, United States

Arun Nandagopal 🛠️🤖 is a highly accomplished mechanical engineer specializing in robotics and automation. He holds a Master’s degree in Mechanical Engineering from the University of Washington and a B.Tech from NIT Tiruchirappalli. Arun has excelled in roles ranging from Research Assistant to Controls Software Engineer, contributing to aerospace, manufacturing, and medical device development. His expertise includes machine learning, CAD design, and robotic motion control. Arun has authored award-winning publications on robotics and inspection systems and has led impactful projects in industry and academia. His skills span Python, ROS, SolidWorks, and advanced control techniques. 🌟📚

 

Publication Profile

Scopus

Educational Background 🎓📚

Arun Nandagopal is a dedicated academic achiever with a robust foundation in mechanical engineering. He is currently pursuing a Master of Science in Mechanical Engineering, specializing in Mechatronics, Robotics, and Automation, at the University of Washington, Seattle (2022–2024), where he has maintained an impressive GPA of 3.99/4. 🛠️🤖 Earlier, he earned his Bachelor of Technology in Mechanical Engineering from the prestigious National Institute of Technology, Tiruchirappalli (2016–2020), achieving a commendable GPA of 3.6/4. 📈 Arun’s education reflects his strong commitment to engineering excellence and innovation in cutting-edge technologies. 🌟

 

Professional Experience 💼🤖

Arun Nandagopal’s professional journey spans diverse roles in engineering and innovation. As a Controls Software Engineer at Fives DyAG (2024–present), he developed seamless PLC-SCADA integrations for operational optimization. At UW + GE Research, Arun enhanced aerospace part inspection efficiency by 50% using machine learning and robotics (2023–2024). 💻✈️ His work at Harborview Medical Hospital (2022–2023) included designing adaptable mechatronic devices, boosting energy efficiency by 30%. Previously, at TATA Advanced Systems + Boeing (2020–2022), he led manufacturing innovations, saving $2M and doubling CNC capacity. 🛠️ During his CAE internship, Arun improved crane load capacity through FEA analysis. 🚀

 

Research Focus 🛠️🤖

Arun Nandagopal specializes in robotics, machine learning, and advanced manufacturing processes. His research primarily focuses on developing frameworks for automated surface inspection using machine learning techniques in aerospace and precision manufacturing. 🛩️🔍 His work on robotic motion control and unsupervised learning has significantly improved inspection efficiency and segmentation accuracy. Additionally, Arun explores agile frameworks and innovative solutions for enhancing manufacturing processes, with applications in robotics, control systems, and data-driven optimization. 📊✨ His expertise bridges mechatronics and intelligent systems, contributing to advancements in smart manufacturing and automation technologies. 🏭💡

 

Publication Top Notes

  • 📝 A robotic surface inspection framework and machine-learning-based optimal segmentation for aerospace and precision manufacturing – Nandagopal, A., Beachy, J., Acton, C., Chen, X., Journal of Manufacturing Processes, 2025
  • 📝 Agile surface inspection framework for aerospace components using unsupervised machine learning – Nandagopal, A., Kulkarni, A., Acton, C., Manohar, K., Chen, X., ISFA Proceedings, 2024

 

 

 

 

Aqib Mashood Khan | Manufacturing Award | Young Scientist Award

Posted on by ScienceFather

Dr. Aqib Mashood Khan | Manufacturing Award | Young Scientist Award

Dr. Aqib Mashood Khan, Nanjing University of Aeronautics and Astronautics, China

Dr. Aqib Mashood Khan has an extensive educational background and professional experience in the field of mechanical engineering, particularly in manufacturing and automation. He holds a Ph.D. in Mechanical Manufacture and Automation from Nanjing University of Aeronautics and Astronautics, China, with a specialization in sustainable machining. His dissertation focused on investigating resource-based energy consumption in sustainable machining with lubricooling approaches.

Publication Profile

Orcid

Google Scholar

Education

Ph.D. in Mechanical Manufacture and Automation from Nanjing University of Aeronautics and Astronautics, China

M.Sc. in Industrial Engineering with a specialization in Manufacturing from the University of Engineering and Technology, Taxila, Pakistan

B.Sc. in Mechatronics and Control System Engineering from the University of Engineering and Technology, Taxila, Pakistan

Awards and Honors

Outstanding Scholar award from NUAA

Recognition in Stanford’s list of top 2% scientists worldwide

Best paper awards and nominations for various prestigious awards

Teaching Experience

Associate Professor, Assistant Professor, and Chairman in departments related to mechanical engineering and mechatronics. You’ve also been involved in research collaboration with international institutions.

Industrial Experience

Your industrial experience includes internships and training in instrumentation, mechanical engineering, foreign procurement, and involvement in university lab setups.

Research Focus

AM Khan’s research focuses on sustainable machining processes, particularly in the domain of minimum quantity lubrication (MQL) and cryogenic cooling, aiming to enhance machining efficiency while minimizing environmental impact. His work delves into the development and application of novel nano-cutting fluids and hybrid nanofluids, integrating them into machining operations for various materials like titanium alloys and steel. Khan’s investigations also include multi-objective optimization techniques to balance energy consumption, surface quality, and environmental sustainability. Through his research, Khan contributes to advancing eco-friendly machining practices, symbolizing a commitment to both technological innovation and environmental stewardship. 🌱🔧

Publication Top Notes 

  1. Effects of hybrid Al2O3-CNT nanofluids and cryogenic cooling on machining of Ti–6Al–4V 🛠️ Cited by: 216, Year: 2019
  2. A comprehensive review on minimum quantity lubrication (MQL) in machining processes using nano-cutting fluids 📝 Cited by: 197, Year: 2019
  3. Investigations of machining characteristics in the upgraded MQL-assisted turning of pure titanium alloys using evolutionary algorithms 🔍 Cited by: 138, Year: 2019
  4. Sustainable milling of Ti–6Al–4V: A trade-off between energy efficiency, carbon emissions and machining characteristics under MQL and cryogenic environment 🌱 Cited by: 115, Year: 2021
  5. Performance evaluation of vegetable oil-based nano-cutting fluids in environmentally friendly machining of inconel-800 alloy 🌿 Cited by: 109, Year: 2019
  6. Energy-based cost integrated modelling and sustainability assessment of Al-GnP hybrid nanofluid assisted turning of AISI52100 steel 💰 Cited by: 106, Year: 2020
  7. Multi-Objective Optimization for Grinding of AISI D2 Steel with Al2O3 Wheel under MQL🔧 Cited by: 96, Year: 2018
  8. Environment and economic burden of sustainable cooling/lubrication methods in machining of Inconel-800 🌎 Cited by: 93, Year: 2021
  9. Cutting performance of textured polycrystalline diamond tools with composite lyophilic/lyophobic wettabilities 💎 Cited by: 89, Year: 2018
  10. Tool wear, surface quality, and residual stresses analysis of micro-machined additive manufactured Ti–6Al–4V under dry and MQL conditions 🔬 Cited by: 85, Year: 2020