Comparison of intelligent approaches for cycle time prediction in injection moulding of a medical device product

Kariminejad, Mandana, Tormey, David, Huq, Saif, Morrison, Jim and McAfee, Marion (2021) Comparison of intelligent approaches for cycle time prediction in injection moulding of a medical device product. In: IEEE RTSI 2021: 6th online Forum on Research and Technologies for Society and Industry Innovation for a smart world, 6 - 9 September 2021, Italy, Online.

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Abstract / Description

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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: injection moulding; cycle time; ANN; ANFIS; MSE
Subjects: 600 Technology > 620 Engineering & allied operations
600 Technology > 670 Manufacturing
Department: School of Computing and Digital Media
Depositing User: Saif Huq
Date Deposited: 04 Oct 2021 10:42
Last Modified: 04 Oct 2021 10:42
URI: http://repository.londonmet.ac.uk/id/eprint/7015

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