  Injection moulding is an increasingly automated industrial process,
particularly when used for the production of high-value precision components
such as polymeric medical devices. In such applications, achieving stringent
product quality demands whilst also ensuring a highly efficient process can be
challenging. Cycle time is one of the most critical factors which directly
affects the throughput rate of the process and hence is a key indicator of
process efficiency. In this work, we examine a production data set from a real
industrial injection moulding process for manufacture of a high precision
medical device. The relationship between the process input variables and the
resulting cycle time is mapped with an artificial neural network (ANN) and an
adaptive neuro-fuzzy system (ANFIS). The predictive performance of different
training methods and neuron numbers in ANN and the impact of model type and the
numbers of membership functions in ANFIS has been investigated. The strengths
and limitations of the approaches are presented and the further research and
development needed to ensure practical on-line use of these methods for dynamic
process optimisation in the industrial process are discussed.
