Ms Mandana Kariminejad
PhD Researcher

Mandana Kariminejad is a PhD researcher in I-Form and is based in IT Sligo working in the area of injection moulding. She received her bachelor’s degree in Mechanical Engineering at Shiraz university and then went on to complete a Masters in Mechanical Engineering (field of Control & Dynamic system) at K.N Toosi University of Technology, Iran. Her specific research interests are in the areas of optimisation and control, injection moulding, additive manufacturing, Mathematical Modelling, simulation and Machine learning.

Research Interests (Lay Summary)

Mandana started her PhD with I-Form in June 2020 and is investigating Wireless sensing for prediction and control of part quality in injection moulding. A major goal of injection moulding research relates to reduction of time cycle, while maintaining quality and efficiency. Replacing conventional cooling circuits with conformal cooling channels is the best approach to achieve this goal, since the cooling cycle is two thirds of the processing time. Another method for enhancing efficiency is using wireless sensing to monitor the crystallization kinetics and morphology of polymers with real time data which can be used to optimize the process. This data can be also employed to develop a model for process control. Hence, Mandana is attempting to optimise the part quality and enhance the efficiency of process, while preserving the quality criteria.


Technical Summary

Injection moulding is a complex process and is prone to defects due to suboptimal distribution of temperature and pressure inside the mould. To overcome this concern, two methods can be followed. Firstly, proposing a model or an approach to predict the process parameters for optimization such as pressure, temperature, shrinkage, warpage, residual stress and etc. This can be solved by using CAE methods such as Moldflow simulation, or employing artificial intelligence and empirical models like artificial neural networks, DOE, fuzzy logic and so on, or embedding different types of sensors for real time monitoring. Secondly, using conformal cooling channels is another method to eliminate the defects such as thermal induced residual stress, shrinkage and warpage. These channels not only smooth deviations in cooling rate, but also reduce the time cycle. However, manufacturing these cooling channels without compromise to dimensional stability is a challenge.

The main focus of this research project is to enhance the efficiency of the injection moulding process.  To do this, the main defects such as warpage, shrinkage and residual stress s=will be optimized with one of the above approaches. The goal is a reduction in cycle time by replacing conventional cooling circuits with conformal cooling channels. This project also investigates the methods of embedding in-mould sensors to monitor the process parameters with higher accuracy and resolution.


Additive Manufacturing (3D Printing), Artificial Intelligence, Control Engineering, Injection Moulding, Process Modeling, Process Monitoring and Control, Process Optimisation, Real-time Data Analytics