John P. T. Mo is Professor of Manufacturing Engineering and former Head of Manufacturing and Materials Engineering at RMIT University, Australia, since 2007. He has been an active researcher in manufacturing and complex systems for over 35 years and worked for educational and scientific institutions in Hong Kong and Australia. From 1996, John was a Project Manager and Research Team Leader with Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) for 11 years leading a team of 15 research scientists. John has a broad research interest and has received numerous industrial research grants. A few highlights of the projects include: signal diagnostics for plasma cutting machines, ANZAC ship alliance engineering analysis, optimisation of titanium machining for aerospace industry, critical infrastructure protection modelling and analysis, polycrystalline diamond cutting tools on multi-axes CNC machine, system analysis for support of complex engineering systems John obtained his doctorate from Loughborough University, UK and is a Fellow of Institution of Mechanical Engineers (UK) and Institution of Engineers Australia.
Abstract: Modern engineering systems have increasing complexity and sophistication. Researches into methods to ensure reliable operation of the system have been continuing for decades. With modern microcontroller technologies, continuous online health monitoring systems are now commonly installed on new engineering systems. The availability of system performance data is not an issue. However, analysis of data and making good sense out of the information is still difficult. Predictive control schemes require continuous assessment of the conditions of the manufacturing equipment to determine if it will operate properly in the next minute or hour. Statistically based monitoring methods are simple in concept but applicability of the algorithm is limited by what has transpired in history. There are always surprises when the performance deviates beyond pre-determined and sometimes broad limits. Hence, this type of analysis could only provide only a rudimentary assessment of the system’s condition based on some ad hoc experience which may not relate to the current situation. Worse still is that the computational process can take a long time. As a consequence, the action taken is often not appropriate and unable to cure the cause. An alternative branch of research investigates system performance on the basis of recognising normal behaviour, thereby providing a means of synthesising their abnormal behaviour, i.e. irregularities. This technique has the advantage that, instead of comparing simple limits, it assesses the system’s condition based on a whole range of performance signal patterns. The outcome can be implemented online in real time operations so that appropriate remedial actions can be taken in time to correct errors. However, recognising the “normal” states is a challenging problem. Large number of simulated experiments is required to ensure there is no missing normal behaviour. This paper discusses the above issues with examples and stimulates further research in system prognostics and health monitoring.
Professor Xu received his undergraduate education from Shenyang Jianzhu University and masters degree from Dalian University of Technology, P. R. China, respectively. In 1996, he received a PhD from the University of Manchester (then, UMIST), UK.
Since then, he has been working at the University of Auckland as a Lecturer, Senior Lecturer, Associate Professor and now Professor. His teaching at the Department of Mechanical Engineering cuts across a number of fields, e.g, mechanical engineering design, manufacturing systems, advanced manufacturing technology, manufacturing information systems and advanced CAD/CAM/CNC.
Professor Xu's main research focuses are on computer-aided design, process planning and manufacturing, in particular STEP-compliant CNC (STEP-NC), cloud manufacturing and Industry 4.0.
The IIMS research group Professor Xu leads is one of top research teams in the world working in the field of STEP-compliant design and manufacturing. Professor Xu is the founder of Manuclouds, an online Cloud Manufacturing Forum. In 2016, he set up the first Industry 4.0 laboratory in New Zealand, Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS).
Abstract: Industry 4.0 is the German vision for the future of manufacturing, one that smart factories use information and communications technologies to digitise their systems and processes, and reap benefits in the form of improved quality, lower costs, and increased efficiency. The technological fundamentals of Industry 4.0 are unpinned by that of Internet of Things and exist in various forms of Cyber-Physical Systems, where “systems” can be products and machines. A key element in a Cyber-physical System is Cyber Twin or otherwise commonly known as Digital Twin. There are some subtle technological differences between the two terminologies. Nevertheless, they share some common features that enable a Cyber-physical System to function effectively in the Industry 4.0 surroundings. This talk gives an overview of the background, concept and major technologies, in particular digital twin and twinning technologies, for Industry 4.0. Some of the cases demonstrated are based on the projects being carried out at the Laboratory for Industry 4.0 Smart Manufacturing Systems (LISMS), The University of Auckland.
Professor He obtained his Bachelor and Master of Engineering degrees
in Extractive Metallurgy from Central South University in China, and
a PhD in Chemical Engineering from The University of Queensland in
Australia. He subsequently worked at The University of Queensland
and The University of Adelaide before joining James Cook University
in 2004. Since then, he was appointed to various leadership roles
including the Head of School of Engineering and Physical Sciences
from 2009 to 2014. Professor He is a Fellow of The Institution of
Chemical Engineers (FIChemE) and Fellow of The Institution of
Engineers Australia (FIEAust) and a Chartered Engineer (CEng).
Abstract: The consumption of rechargeable batteries has been increasing rapidly. At present, the
majority of rechargeable batteries are disposed of to landfills at the end of their useful lives.
This represents a significant loss of valuable, non-renewable resources, in addition to the
potential to cause serious environmental damages from leakage of chemicals in the
electrolyte and leaching of heavy metals. To develop a truly sustainable battery industry, it is
essential to recycle and retrieve materials from the spent batteries.
Research efforts to recover valuable materials from spent batteries have surged in recent
years. Many process flow charts have been reported in the literature, which are largely based
on the pyrometallurgical or hydrometallurgical extraction methods, or their combinations, of
the constituent metals. To date, however, there are only very limited number of commercial
scale operations to recover or regenerate materials from the spent batteries due largely to
economic viability and unclear environmental benefits of most of the lab scale technologies.
In this presentation, I will first provide an overview of the spent batteries in the world with
some statistics on Australia, China, Europe and the United States. I will then discuss the
challenges faced in the recycling of and materials recovery from the spent batteries, with a
focus on the lithium ion batteries. I will conclude my presentation by offering my personal
views on the role of policies, battery design and circular economy, and the responsibilities of
governments, manufacturers and consumers in the development of a sustainable battery