Summary
Hamid is a Research Associate in the Advanced Manufacturing Group at the Dyson School of Design Engineering, Imperial College London. His passion lies in the field of data science and the applications of Machine Learning to solve complex engineering problems.
Currently, Hamid is focused on expanding his contributions in novel engineering applied machine learning and optimisation methods. His research revolves around developing strategies and support tools for the Hot Form Quench (HFQ®) hot metal stamping process, addressing today's industrial needs. In particular, he is developing machine learning methods to assist in component design and optimisation for hot stamping applications.
Hamid holds a Doctor of Philosophy (PhD) degree in Engineering applied Machine Learning from Imperial College London, UK, and a Master of Engineering (MEng) degree in Mechanical Engineering from the University of Surrey, UK, where he graduated with First Class Honours. His research interests include material forming, hot stamping, finite element analysis, machine learning, and design optimisation.
Throughout his career, Hamid has demonstrated exceptional problem-solving, interpretive, and numerical abilities. He takes pride in identifying opportunities for process and methodology improvements and follows through by devising, developing, and implementing effective solutions. He thrives when faced with challenges and performs well under pressure, maintaining a collaborative mindset as an excellent team player.
Hamid is the author of over 13 peer-reviewed journal and conference publications, showcasing his expertise and contributions in the field. If you have any inquiries related to machine learning consultation or need assistance in this field, please feel free to reach out. He is always open to connecting with fellow professionals and exploring exciting opportunities in the realm of applied machine learning and optimisation.
Publications
Journals
Attar HR, Foster A, Li N, 2023, Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints, Engineering Applications of Artificial Intelligence, Vol:123, ISSN:0952-1976, Pages:1-23
Attar HR, Foster A, Li N, 2023, Implicit neural representations of sheet stamping geometries with small-scale features, Engineering Applications of Artificial Intelligence, Vol:123, ISSN:0952-1976, Pages:1-21
Attar HR, Li N, 2022, Optimisation of deep drawn corners subject to hot stamping constraints using a novel deep-learning-based platform, Iop Conference Series: Materials Science and Engineering, Vol:1238, ISSN:1757-8981, Pages:012066-012066
Attar H, Li N, Foster A, 2021, A method for determining equivalent hardening responses to approximate sheet metal viscoplasticity, Methodsx, Vol:8, ISSN:2215-0161, Pages:1-20
Xu Q, Nie Z, Xu H, et al., 2021, SuperMeshing: a new deep learning architecture for increasing the mesh density of physical fields in metal forming numerical simulation, Journal of Applied Mechanics, Vol:89, ISSN:0021-8936, Pages:1-10