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Reservations about Qualitiative Models
Very little of the knwoledge that an operator of a complex physical system brings to the job is purely quantitative in form. Virtually all of an operator's knowledge can be represented as qualitative relations or quasi-quantitative relations such as rough proportionalities. The realization that computer-based Instruction syste,s need to provide instructions and explanations In terms that students can use, that is, often in qualitative terms, has led to recent efforts in cognitive science and artificial intelligence to develop qualitative simulation models of complex dynamic systems. In this paper we discuss theoretical and pragmatic problems involved in using qualitative models to support automated explanation facilities
Novel analysis and modelling methodologies applied to pultrusion and other processes
Often a manufacturing process may be a bottleneck or critical to a business. This thesis
focuses on the analysis and modelling of such processest, to both better understand them,
and to support the enhancement of quality or output capability of the process.
The main thrusts of this thesis therefore are:
To model inter-process physics, inter-relationships, and complex processes in a
manner that enables re-exploitation, re-interpretation and reuse of this knowledge and
generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM)
techniques. This involves the development of superior process models to capture
process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural
Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number
of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited
in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and
logistics) using analysis and modelling.
To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA)
methodology, and a novel Artificial Neural Network Process Modelling (ANNPM)
methodology were developed and applied to a number of complex manufacturing case
studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics
supply chain. It has been shown that these methodologies and the models developed support
capture of complex process inter-relationships, enable reuse of generic elements,
support effective variable selection for ANN models, and perform well as a predictor of
process properties. In particular the ANN pultrusion models, using laboratory data from
IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well