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Wireless sensing for prediction and control of part quality in injection moulding

Project Description

Across the West of Ireland injection moulding is used for the manufacture of products which improve quality of life and environmental sustainability, including: autoinjector devices for diabetes treatment; lightweight components for Jaguar and Land Rover cars; delivery systems for cardiovascular stents, as well as leading the way in use of recycled materials in the automotive industry. Adoption of Industry 4.0 technologies - increasing sensorisation, predictive analytics, modelling and smart control technologies – are essential to enable manufacturing of ever more complex, customised parts with lower lead times, and lower material and energy consumption.
Injection moulding is a complex process and is prone to defects due to suboptimal distribution of temperature and pressure inside the mould. However, due to limited availability of such data during the process, relatively little is known about how to optimize the process settings to achieve correct pressure and temperature distribution in the mould. In-mould data is generally limited due to technical challenges around embedded sensor insertion. This project will investigate the integration of novel wireless sensor technologies, within Additive Manufacturing (AM) produced moulds for the inline monitoring of product quality. Inline mould sensors will capture data enabling insight into the state of the material inside the mould. This data will be used to develop predictive models of the quality of the part in terms of dimensional and mechanical properties, with the aim of more rapid product quality assessment. Ideally, with sufficient validation of the approach, post-processing metrology steps can be reduced, hence reducing product lead-times and reducing scrap rates. Data captured from the inline mould sensors will also be explored for use in developing adaptive closed loop control strategies for the adjustment and optimization of injection moulding process settings to reduce set-up times and associated material and energy wastage.
• To investigate a range of wireless in-mould sensing options for monitoring of part quality (temperature/ pressure/acoustic/fibre-optic/ultrasound)
• To develop models for the prediction of part quality from in-mould sensor data
• To explore the role of these predictive models in validation of part quality
• Model the relationships between settable process parameters and part quality
• Explore the use of the models for process optimisation and control.

Funding Notes

This bursary has three elements:
a.Maintenance grant €13,000 p.a. (Paid by IT Sligo President's Bursary)
b.Tuition fees (currently €819 p.a.) (Paid by the Institute for all holders of student bursaries)
c. The Principal Investigator will part-fund the student registration fee. For 2019-2020 this fee is €3,000 p.a. and half of this will be funded. The remaining €1,500 will be deducted from maintenance grant, with the balance, €11,500 p.a., paid to the awardee monthly.
Total Annual Award: €15,319
A further allocation for consumables may be made up to €4,000 p.a. from the IT Sligo President's Bursary.

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