We have four major research tracks:
- Digital Design and Manufacturing
- Process Monitoring
- Process Control
- Industrial Systems
We have four major research tracks:
We aim to develop simulation tools with capability to predict the future behaviour of the assembly system before “physical” installation, commissioning and production. This implies the need to develop a multi-physics defect simulator with capability to model product-related defects and process-induced defects, with the ultimate goal of achieving “right-first-time” capability at (early) design stage.
It has been reported that a leading challenge in delivering cost effective and high quality products is the need to incorporate statistical variation model to tackle product and process variations, so to eliminate/reduce defects during manufacturing stages. Research has demonstrated that around 65–70% of all design changes are related to product variation. It is widely recognised that geometric and dimensional variation are among the most important quality and productivity factors in many assembly processes used not only in automotive (example of large production volume) and aerospace (example of low production volume) but also in appliance, ship-building and other industries. The leading challenges for effective digital design and manufacturing are as follows:
The key idea is the integration of product and process models along with stochastic mechanisms and first principles approaches (i.e. PDE, ODE) in order to predict the physical behaviour of the multi-stage assembly system in the presence of real manufacturing variations. The ultimate goal is to establish the functional relationship between input process parameters and defect indicators to narrow down defects (i) generated at single stage level (root cause analysis at single-stage level), (ii) and propagated at multi-stage level (root cause analysis at multi-stage level). Items (i) and (ii) are facilitated by (iii) stochastic robust optimisation which include process optimisation with integrated stochastic mechanisms.
We aim to develop closed-loop quality control mechanisms with the ultimate goal of achieving zero-defects manufacturing system. This entail the development of intelligent rapid monitoring and diagnostic methodologies to detect and isolate defects and abnormalities.
Classical approaches for defects diagnostic are mainly concerned with process monitoring to detect the occurrence of any defect, and pinpoint the type of defect and its location within the manufacturing system. Traditional Statistical Control Charts (SPC analysis) have focused on the monitoring of one quality parameters at a time and are not appropriate for analysing process data where variables exhibit collinear behaviour. These limitations are addressed through the application of multivariate statistical process control (M-SPC analysis). M-SPC methods offer the clear advantage to aggregate multiple quality parameters on one individual control chart using T2 and Q statistics. Unfortunately, M-SPC charts also have some disadvantages. The most important one is related to the lack of interpretation of data represented on the control chart. In fact, even though defects can be detected the subsequent isolation is not trivial because of the multiple feasible combinations generating a single fault. The main challenges for rapid diagnostic of defects are enumerated as follows:
Each stage of the manufacturing process is understood as a stochastic event with complex interactions between input parts/sub-assemblies, process parameters and variations associated with parts and process parameters. The functional relationship between inputs and outputs is represented as a stochastic multi-variate function, which facilitates defect identification in combination with failure and defects patterns extrapolated on-the-fly with in-process adaptive measurement strategy and rapid diagnostic.
We aim to develop closed-loop quality control mechanisms with the ultimate goal of achieving zero-defects manufacturing system. This entail the development of architecture and models for in-process rapid adjustment and control.
Currently, corrective or preventive actions are mainly data-driven, meaning that they can only work out locally acquired data. Limitations are:
We propose to go from data-driven to model-based approach and to introduce self-optimising systems into industrial applications that have the ability to correctively and preventively act on the process based on model-based reasoning information. This underlies the following challenges:
Our vision is to correct and prevent only those defects which trigger unwanted deterioration of product quality. This is facilitated by novel algorithms with the capability to model: (i) multi-physics defect simulation; (ii) intelligent root cause analysis evaluation; and (iii) intelligent knowledge repository based on advanced machine learning models, such as neural network, rough set, fuzzy logic etc.
To develop systematic methodologies and simulation tools through the integration of care processes, resources and complex service system information to reduce and prevent unnecessary variation in care along the patient journey for quality improvement.
Healthcare organisations represent fast-paced environments that are often characterised by high levels of uncertainties. Depending upon patient conditions, multiple roles, therapies, resources and quality indicators interact with the care journey to ensure the delivery of appropriate care. Often the roles and resources are not clearly defined and may be subject to frequent changes. Also, the progression of the care journey generates data that are heterogeneous in nature and reside in multiple locales that are not always connected. Frequently these data are ill-structured, uncertain, and/or missing. Taken together, these factors trigger unnecessary variation in care that must be reduced and where possible prevented. Some of the key challenges towards reducing such variation may be seen as:
Patient care is essentially process driven. Plus, the healthcare system is both resource intensive and measured through numerous quality indicators. Research activities aim to develop systematic methodologies by integrating process related measurements with key performance indicator data involving care quality requirements and service performance to understand, reduce and prevent unnecessary variations in care in the patient journey through the service system.