Projects

While information about production processes has been becoming increasingly transparent, detailed, and real-time, the utilization of this information for real-time manufacturing analysis and decision-making has been lagging behind largely due to the limitation of the traditional methodologies for production system analysis, and a lack of real-time manufacturing processes modelling approach and real-time performance identification method.

As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. This research establishes theory and develops automated real-time distributed control schemes to cope with the complex nature of manufacturing processes and systems to achieve higher energy efficiency and profit.

A production line in a factory can be made up of multiple production stations or work cells. Each station/work cell includes one or multiple machines or equipment performing a specific function, such as pretreatment, multilevel processing, testing and assembly. In each work cell, robots or humans move between machines/process steps to load and unload parts. In this project, we focus on the analysis of this kind of work cell, referred to as a gantry work cell.

The rapid development in battery technology and increasing needs in energy storages has introduced enormous challenges and complexities on battery production systems. Battery manufacturing must quickly ramp up with the newly developed technologies, new tools and equipment, and resources in order to meet battery production needs for a variety of stationary and motive applications.

Automation of composite laminates manufacturing is instrumental in meeting the growing demand for composites and promises to revolutionize composite-dependent industries. However, the few existing layup automation systems are prohibitively expensive and are not fully optimized for product quality.

In last decade, global competition has forced manufacturers to optimize logistics. An innovative containerization method (CM) provides a new perspective for optimizing logistics cost saving, where collapsible containers are used to reduce the frequency of return freight.

Driven by wind and solar photovoltaics technology, the power industry is shifting towards a distributed generation (DG) paradigm. We focus on DC micro-grid, and then extend to grid connected DG system. The current research on energy management will provide energy consumption optimization, reduce peak power and enable prioritized islanding, as well as create an opportunity for excess produced energy to be stored to battery or sold back to the grid. It will monitor and operate remotely and automatically, further reducing overall costs.

We used a system simulation and analysis methodology to identify the key factors in the after-discharging systems which may contribute to high readmission rate, and hence to improve system performance and reduce readmission rate with lower cost.

Manufacturers are seeking to operate the production in a more efficient and cost-effective way when facing the growing competition and globalization. Since capital expenditure incurred by maintenance accounts for a large portion of the overall cost in production activities, optimal maintenance decision making is one of the fundamental aspects in achieving this end.

We developed a new real-time event-based data-driven model to describe the production line with quality rework loop. Then we further analysed the properties of a production line with quality rework loops and developed a real-time system performance diagnostic method to evaluate the impact of the quality issues.

The introduction of Human-robot collaboration (HRC) can significantly improve the flexibility and automation of the intelligent manufacturing system. However, due to the stochastic and time-varying nature of human collaborators, it is quite challenging for the robot to efficiently and accurately identify such task plans and respond in a safe manner. In this project, we propose an integrated human-robot collaboration framework, in which both task planning and trajectory prediction modules are included for the efficiency and safety of the system.