School of Engineering
Digital Manufacturing and Management Research Group (DMM)
The Digital Manufacturing and Management research group (DMM) was created to promote and implement cross-disciplinary research in materials and process modelling to advance manufacturing through digitisation and data driven technologies. The group collaborates with manufacturing companies to derive value from data sources by improving the accuracy of decisions, reducing uncertainty, and gaining insights into their operational environments.
Our agenda
- Digital twinning via process modelling integrated with process monitoring and control of manufacturing.
- Data-analytics and data-driven manufacturing in collaboration with the School of Computing and Mathematics Science. Using deep reinforcement learning and computer vision, real-time time-series data analysis, manufacturing data pipelines, and the development of autonomous decision support systems.
- Intelligent manufacturing via implementing state-of-the-art automation and robotics, integrating real-time optimisation, artificial intelligence, and 5G communications, into existing and future manufacturing processes. It includes human-robot collaboration, requiring provable safe automation by design, and real-time optimisation of economic performance and complements the existing "Industry 4.0" approach by specifically putting research and innovation at the service of the transition to a sustainable, human-centric and resilient manufacturing industry.
- Product development and rapid prototyping in collaboration with Space Park Leicester. Using our unique expertise in the disruptive technology of additive manufacturing (AM) we will develop digital design platforms for exploring new methods of manufacturing, linking key staff with industrial partners.
- Digital-enabled net-zero manufacturing to support circular approaches for the next generation of manufactured products using digital tools, enabling companies to adopt technologies that provide greater value retention across the full product lifecycle. We also aim to create models/testbeds to prove the fundamental science around both recycling and the reintroduction of waste materials and energy through the full manufacturing chain and the full life cycle of the products in aerospace, metals, ceramics, and pharmaceutical sectors for a greener planet.
Research projects
- Horizon Europe: Boosting sustainability, reliability and efficiency of perovskite PV through novel materials and process engineering (SUNREY). Grant agreement ID: 101084422
- Horizon Europe: Innovative integrated tools and technologies to protect and treat drinking water from disinfection by-products (H2OforAll). Grant agreement ID: 101081963
- H2020: Development of novel and cost-effective coatings for high-energy processing applications (FORGE). Grant agreement ID: 958457
- H2020: Innovative digital watermarks and green solvents for the recovery and recycling of multi-layer materials (Sol-Rec2). Grant agreement ID: 101003532
- BEIS/ACT3. Conversion of captured CO2 to industrial chemicals (CoCaCO2la). Grant agreement: 691712
- Industrial sponsored project: Machine learning of metallurgical processes and materials design.
- Innovate UK: Novel approach for development of targeted high entropy powders for additive manufacturing aerospace applications (NATEP-AM).
- Industrial sponsored project: Search and characterisation of novel Phase Change Materials (PCM) using a combined modelling and calorimetrical approach.
- Innovate UK: Knowledge Transfer Partnership with I Holland Ltd to develop a predictive model for manufacturability of pharmaceutical tablet formulations. 2023-2025.
- IFPRI (International Fine Particle Research Institute): Adhesion of powders to metal surfaces during compaction 2018-2023.
People
- Professor Csaba Sinka, Head of the Group, ics4@leicester.ac.uk
- Dr Shiladitya Paul, Deputy Head of the Group, sp660@leicester.ac.uk
- Professor Hongbiao Dong FREng, hd38@leicester.ac.uk
- Dr Victor Cedeno, vmcc1@leicester.ac.uk
- Dr Zeynel Abidin Cil, zac7@leicester.ac.uk
- Dr Reza Baserinia, rb662@leicester.ac.uk
Research associates and fellows
- Dr Maryam Khaksar Ghalati, Research Associate
- Dr Vikas Kumar, Research Associate
- Dr Tianmao Li, Research Associate
- Dr Adamantini Loukodimou, Research Associate
- Dr Gauri Mahalle, Research Associate
- Dr Kranthi Maniam, Research Fellow
- Ms Madhuri Maniam, Research Associate
- Dr Corentin Penot, Research Associate
- Dr Deepak Sharma, Research Associate
- Dr Amandeep Amandeep, Research Associate
Honorary visiting fellows
- Dr Keith Cater, Honorary Visiting Fellow
- Dr Zihui Dong, Honorary Visiting Fellow
- Dr Hasan Elmsahli, Honorary Visiting Fellow
- Dr Jean-Christophe Gebelin, Honorary Visiting Fellow
- Dr Tung Lik Lee, Honorary Visiting Fellow
- Dr Qing Tao, Honorary Visiting Fellow
- Dr Shuwen Wen, Honorary Visiting Fellow
Current PhD students
- Jun Fu
- Numerical modelling and experimental investigation of thermal and microstructural evolution of E36 marine steel during high heat input welding
- Arunima Bhuvanendran Nair Jayakumari
- Design of materials and joints for hydrogen service
- Peter Polak
- Understanding densification and crack propagation in pharmaceutical tablet manufacturing
- Ahmad Ramahi
- Experimental characterisation of adhesion of powders to metal surfaces
- Vishal Shinde
- Modelling adhesion of powders to metal surfaces
- Shaun Smart
- Boundary conditions for occurrence of weld metal hydrogen-assisted cold cracking in multipass weld
- Amadi Udu
- A machine learning-based approach for the assessment of fabrication and porosity effects on the mechanical properties of additive manufactured composites
- Adriana Castro Vargas
- Protection of offshore wind turbines using low-cost, damage tolerant, sacrificial coatings
- Kenta Yamada
- Effect of multiple thermal cycles on microstructure evolution and properties in 25Cr-5Ni-1Mo-2.5Cu-1Mn-0.18N duplex stainless steel
- Xiaoan Yang
- Numerical modelling of inclusions control in clean steel production
- Junguo Zhao
- Novel powder materials for additive manufacturing
- Jianbo Zhang
- Machine learning of metallurgical processes
PhD vacancies and applications
We welcome applications from prospective PhD students. Please contact us directly for an informal discussion. Find out about the PhD programme and application process.