Prof. Duc Truong Pham

University of Birmingham, UK

FREng, FLSW, FSME, BE, PhD, DEng, CEng, FIET, FIMechE

Title: Progress towards smart remanufacturing

Abstract: Remanufacturing is the process of restoring a product that has reached the end of its service life to at least its original condition, with a warranty equal to, or longer than, that of the original product.  Remanufacturing is integral to a circular economy, saving raw materials and other resources, including energy and water, and drastically cutting greenhouse gas emissions and the need for landfill.  Thus, remanufacturing is inherently ‘smart’ and sustainable, meeting several of the United Nations’ Sustainable Development Goals. 

In the same way that manufacturing can become smart by adopting technologies such as intelligent robots, the Internet of Things, machine learning, and big data analytics, remanufacturing can also be made even smarter by the deployment of such competitiveness-enhancing digital technologies.  For the past decade, research at the University of Birmingham has focused on making remanufacturing smarter by tackling perhaps the most critical link in the remanufacturing process chain: disassembly.

Disassembly of the product to be remanufactured is the first task in almost all remanufacturing operations.  Due to the condition of the used product, disassembly can be difficult and, consequently, has almost always been performed by human operators so far.  This presentation describes research conducted by our laboratory aimed at robotising disassembly as a first step towards achieving smart, autonomous remanufacturing.

Experience: Duc Truong Pham’s research covers the fields of mechanical, manufacturing, computer and systems engineering. His academic output includes more than 600 technical papers and 17 books. He has supervised over 100 PhD theses to completion. He has won in excess of £30M in external research grants and contracts. In addition to pursuing and leading research, he has acted as a consultant to several major companies and has been active with knowledge transfer to industry, applying the results of his work to help multinational companies and SMEs generate wealth and create and safeguard jobs. 

Prof. George Q. Huang

Hong Kong Polytechnic University, Hong Kong SAR, China

Chair Professor of Smart Manufacturing, Director of PolyU Research Institute of Advanced Manufacturing

Title: In Search of Breakthroughs for High-Performance Cyber-Physical Smart Manufacturing

Abstract: The talk is about our search for an Industry 4.0 intelligent factory following a formal computer architecture and operating system. By so doing, computer hardware and software techniques can be adapted for high-performance factory production management. The breakthrough is achieved through a trilogy of innovations: (1) digitizing a factory with smart IoT devices into a “factory computer” (iFactory); (2) innovating iFactory visibility and traceability (VT) to enable “look around” techniques just as used in the “Out of Order Execution (OoOE)” algorithm by CPUs (Central Processing Units); and (3) developing novel models for iFactory shopfloor operations management. The iFactory architecture provides new opportunities to explore and study factory uncertainties through cyber-physical visibility and spatial-temporal traceability, and to develop brand-new data-driven decision models for factory operations planning, scheduling and execution. iFactory demonstrates a new approach to implement Industry 4.0 smart manufacturing systems for high performance, responsiveness and resilience.

Experience: George Q. Huang joined Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University as Chair Professor of Smart Manufacturing and Director of PolyU Research Institute of Advanced Manufacturing (RIAM). George graduated from Southeast University (China) with BEng and Cardiff University (UK) with PhD degrees respectively. George has been working on smart manufacturing ever since his PhD study and continued and expanded into smart logistics and smart construction with substantial research grants from governments and industries. He published extensively in the related fields and his works have been widely cited with the research community. He served as senior / department / area / regional / associate editors and on editorial boards of more than a dozen of reputable journals. George is Chartered Engineer (CEng), Fellow of IEEE, ASME, IISE, IET, CILT and HKIE. 

Prof. Weidong Li

University of Shanghai for Science and Technology, China

FIMechE, FIET

Title: TBD

Abstract: TBD

Experience: He is the dean, professor, doctoral supervisor of the School of Mechanical Engineering, University of Shanghai for Science and Technology, Special Professor of Changjiang Scholars, Shanghai innovation leader, Fellow of the British Institution of Mechanical Engineers (IMechE), Fellow of the International Institute of Engineering and Technology (IET), and scholar of the European Union's Maligurie Program. He has presided over more than 30 scientific research projects of EU Framework Programme (EU FP7, EU Horizon), UK Engineering and Physics Foundation (EP SRC), UK Innovation Agency (IUK) and European industry (Airbus, Rolls-Royce, Jaguar Land Rover). He has been the director of the European Union International cooperation project for three times, won the European Union "Success Case Award" twice, and presided over the National Natural Science Foundation of China, the Ministry of Science and Technology, the Shanghai Science and Technology Commission, the Shanghai Municipal Education Commission, and the horizontal scientific research projects of enterprises.

Reader-Dr.MichaelPackianather

Cardiff University, UK

Title: Fault diagnosis in machines using AI and deep learning methods

Abstract: This talk will focus on the need to develop efficient techniques for fault diagnosis and fault classification in machines. In particular, mechanical and electrical faults in an induction motor will be considered. The techniques will cover Multimodal Preprocessing, Artificial Image Synthesis, Deep Learning and Load-Adaptive Graph-Based Methods. Emphasis will be given to Artificial Intelligence based methods for designing semi or fully automatic fault classification and fault prediction systems for extending the Remaining Useful Life (RUL) of machines. 

Experience: Dr. Michael Packianather is a Reader at Cardiff University and his expertise covers AI/ML, Robotics, Optimisation and Intelligent Systems. He is the Deputy Director of PGR Studies. He has worked successfully on several EPSRC, UKRI and EU funded collaborative research projects. He is an Associate Editor of the International Journal on Interactive Design and Manufacturing (IJIDeM). He was a key member of the MANUELA project funded by the EU H2020 programme and his research contribution focused on the development of Digital Twin for minimizing phores in Metal Additive Manufacturing process also known as Powder Bed Fusion through deep learning models and predictive data analysis.   

Prof. Jun Huang

Wuhan University of Technology,

China

Title: Robotic disassembly technology of end-of-life products towards a circular economy

Abstract: Disassembly is the first and critical step in the remanufacturing and recycling of end-of-life (EoL) products for a circular economy. Due to the complexity and uncertainties of the product at the end of its service life, manual disassembly is labour-intensive, inefficient, and costly. Robotic disassembly and human–robot collaborative disassembly have been developed and implemented to improve disassembly efficiency and reduce costs. The presentation will provide a brief overview of the research on robotic disassembly technology conducted in our lab.

Experience: He has published more than 20 high-level academic papers and 2 bibliographic chapters as the first or corresponding author, and obtained 7 national patents. From 2017 to 2021, he worked as a full-time researcher in the team of Duc Truong Pham, a fellow of the Royal Academy of Engineering and Professor at the University of Birmingham, and successively participated in a number of major scientific research projects in the EU and the UK. He was elected a Fellow of the Higher Education Institute (FHEA) in 2019, certified as a Registered Engineer (CEng) in 2020, and appointed as an Expert Member of the British National Standards Institute (BSI) from 2019 to 2021. In 2022, he was selected into the Hubei High-level Innovation Talent Plan.

Assoc. Prof. Mozafar Saadat

University of Birmingham, UK

Title: TBD

Abstract: TBD

Experience: Dr Mozafar Saadat holds an honours degree in mechanical engineering from University of Surrey, and a PhD in manufacturing automation from University of Durham. He has had a range of previous experience as manufacturing technology consultant to industry as well as holding a full-time academic position at University of Sunderland prior to joining University Birmingham in 1999.His general research and teaching interests are in the areas of automation, robotic, and manufacturing engineering. Dr Saadat has developed and directed a number of engineering undergraduate and MSc programmes, authored several open/distance learning teaching modules, and more recently, has instigated, developed and led international collaborative and partnership degree programmes.