Mandana Kariminejad

PhD Student / PEM Student Researcher

Research topic: Wireless sensing & sensorization of injection moulding for prediction and control of part quality

 

Mandana received her bachelor's degree in Mechanical Engineering at Shiraz University and then completed a Masters in mechanical engineering (Control & Dynamic system) at K.N Toosi University of Technology, Iran.

Afterwards, she worked as a design engineer in the steel complex for a year and a half.  She started her Ph.D. with IT Sligo in July 2020 under Dr. David Tormey and Dr. Marion McAfee's supervision. She is investigating wireless sensing for the prediction and control of part quality in injection molding with industry partner Abbvie Ballytivnan. She is also a member of I-Form, the SFI Research Centre for Advanced Manufacturing. Her specific research interests are optimization and control, injection molding, additive manufacturing, mathematical modeling, simulation, and Machine learning.

Research Project:

The injection moulding process is prone to many defects such as residual stress, leading to warpage and shrinkage, weld lines, etc. So, improving the part quality is essential to manufacture a part more efficiently. One of the causes of these defects is inefficient cooling, leading to a long cycle time and uneven heat removal. To improve and optimize the process parameters and the resulting defects, real-time and inline monitoring of process parameters must extract precise data from the process. This data can be used for modeling and optimization of the process.

The residual stress and weld line defects on the injection moulded part.

A significant goal of injection moulding research relates to reducing cycle time 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, optimizing the process. This data can also be employed to develop a model for process control.

The heat efficiency of conformal cooling channels vs. conventional cooling channels

Hence, Mandana is attempting to optimize the part quality and enhance the efficiency of the process while preserving the quality criteria.

The project aims can be summarized as the following:

  • Investigate a range of wireless in-mould sensing options for monitoring part quality

  • Predict the residual stress (warpage, shrinkage) and weld lines based on this data

  • Enhance the part quality by achieving the required dimensional tolerances and stability

  • Exploit the model for predicting residual stress, warpage, shrinkage, etc.

  • Reducing cycle time by the current cooling method and conformal cooling channels

Initially, suitable sensors should be selected for real-time monitoring. There is a wide range of in-mould sensors such as ultrasonic sensors, pressure sensors, acoustic sensors, etc. The particular type of ultrasonic sensor has been selected for further investigation in in-mould sensing. This sensor will be able to monitor different properties of the polymers and tooling.

 

Mandana is also working on a review paper examining ultrasonic sensors' applications in the injection moulding process, which will be submitted in early 2021.

The fabricated ultrasonic sensor on the barrel of the injection molding