Multi-response parameters optimisation for energy-efficient injection moulding process via dynamic shainin DOE method

Process parameters optimisation has been identified as a potential approach to realise a greener injection moulding process. However, reduction in the process energy consumption does not necessarily imply a good part quality. An effective multi-response optimisation process can be demanding and often relies on extensive operational experience from human operators. Therefore, this research focuses on an attempt to develop a more user-friendly approach which could simultaneously deal with the requirements of energy efficiency and part quality. This research proposes a novel approach using a dynamic Shainin Design of Experiment (DOE) methodology to determine an optimal combination of process parameters used in the injection moulding process. The Shainin DOE method is adopted to pinpoint the most important factors on energy consumption and the targeted part quality whereas the 'dynamic' term refers to the signal-response system. The effectiveness of the proposed approach was illustrated by investigating the influence of various dominant parameters on the specific energy consumption (SEC) and the Charpy impact strength (CIS) of polypropylene (PP) material after being injection-moulded into impact test specimens. From the experimental results, barrel temperature was identified as the signal factor while mould temperature and cooling time were used as control factors in the full factorial experiments. Then, response function modelling (RFM) was built to characterise the signal-response relationship as a function of the control factors. Finally, RFM led to a trade-off solution where reducing part-to-part variation for CIS resulted in an increase of SEC. Therefore, the research outcomes have demonstrated that the proposed methodology can be practically applied at the factory shop floor to achieve different performance output targets specified by the customer or the manufacturer's intent.