{"id":7366,"date":"2017-10-27T08:00:58","date_gmt":"2017-10-27T14:00:58","guid":{"rendered":"https:\/\/www.ulprospector.com\/knowledge\/?p=7366"},"modified":"2017-11-03T08:49:57","modified_gmt":"2017-11-03T14:49:57","slug":"using-cavity-sensors-optimise-injection-moulding","status":"publish","type":"post","link":"https:\/\/ulprospector.ul.com\/7366\/using-cavity-sensors-optimise-injection-moulding\/","title":{"rendered":"Using cavity sensors to optimise injection moulding"},"content":{"rendered":"

Advanced data processing is key to predictive maintenance and machine monitoring in smart factory environments. In injection moulding, a majority of reject parts produced are the result of temperature control errors. In the past few years, the energy consumption associated with all the various steps in injection moulding has also received a lot of attention.<\/p>\n

An exciting initiative under the EU Horizon 2020 programme is currently drawing to a close. It is aimed at optimising the performance of injection moulding machines on the fly.<\/p>\n

PREVIEW is a consortium of experienced European injection moulding manufacturers, leading technological industrial partners and academic research institutes, collaborating to explore merging state-of-the-art data acquisition systems with machine learning techniques to create a process for remotely monitoring and adjusting injection moulding processes. The primary project leaders were Eurecat (Spain), Smithers Rapra (United Kingdom) and Humboldt Universit\u00e4t (Germany).<\/p>\n

Technical planner Andrea S\u00e1nchez-Valencia is a Research Technologist at Smithers Rapra, responsible for the evaluation, development and deployment of the methodology to assess the impact of the PREVIEW technology in industrially-driven injection moulding applications.<\/p>\n

S\u00e1nchez-Valencia says that PREVIEW is capable of recognising process variations and timely executing alerts notifications, suggesting corrective actions to the operator to preserve optimum part quality. By doing so, the production process stays within part design tolerances, preventing unnecessary material waste, shifting injection moulding from traditional manufacturing to smart manufacturing.<\/p>\n

Smart manufacturing attempts to increase process adaptability via controlled computer systems. In injection moulding this can be translated to improved plant flexibility: running many products\/moulds in parallel; and low material waste.<\/p>\n

At present, the latest models of injection moulding machines incorporate quality control mechanisms that can shut down production if the machine\u2019s injection moulding parameters deviate from those defined as optimum. Unfortunately, they fail to specify the resulting part defect or to provide individual part traceability.<\/p>\n

Additionally, machine parameters control the cavity temperature and pressure as the polymer enters the mould. The use of cavity sensors makes it possible to monitor these parameters, and when combined with the injection moulding parameters of the machine (such as holding pressure, injection speed and cycle time), very high levels of part quality control can be achieved. Full control of multiple injection moulding machines running on a factory floor is the objective.<\/p>\n

\"The
PREVIEW Subsystems<\/figcaption><\/figure>\n

PREVIEW uses a data acquisition system (DAS) for the adaption, amplification and digitalisation of the electrical and mechanical signals coming from:<\/p>\n