TY - GEN
T1 - Prediction of Hotspots in Injection Moulding by Using Simulation, In-Mould Sensors, and Machine Learning
AU - Kariminejad, Mandana
AU - Tormey, David
AU - O'Hara, Christopher
AU - McAfee, Marion
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Injection moulding is an industrial process for the mass production of plastic components, with many parameters affecting the quality of this process. Hotspot regions in the component occur due to non-optimised process variables or limitations in the cooling system and can lead to warpage or shrinkage. Hotspots should be minimised to avoid part defects and achieve the required dimensional tolerances for precision components. This work outlines a machine-learning-based approach for predicting the maximum hotspot temperature in an injection moulded component using process simulation and in-mould sensor data. The hotspots were identified through software simulation, and then their locations and temperatures were confirmed through an actual experiment using in-mould thermocouples. Two different machine learning approaches, artificial neural network (ANN) and support vector regression (SVR), were developed using the extracted data from the sensors and a design of experiment (DOE) method. The performance of linear and Gaussian kernels was compared for the SVR method. The Gaussian SVR resulted in superior performance compared to the linear kernel. The Gaussian SVR was then compared to the ANN prediction method, where ANN showed a slightly better prediction performance. This study has two primary outcomes. First, we show the simulation results can be used to identify critical areas of the part for real-time monitoring. Secondly, embedding sensors in these locations and applying the collected data for a machine learning analysis, provides a good indication of potential quality issues such as warpage and shrinkage post-production. The use of ANN indicates an accurate prediction performance, facilitating rapid optimisation of the process for the minimisation of hotspots.
AB - Injection moulding is an industrial process for the mass production of plastic components, with many parameters affecting the quality of this process. Hotspot regions in the component occur due to non-optimised process variables or limitations in the cooling system and can lead to warpage or shrinkage. Hotspots should be minimised to avoid part defects and achieve the required dimensional tolerances for precision components. This work outlines a machine-learning-based approach for predicting the maximum hotspot temperature in an injection moulded component using process simulation and in-mould sensor data. The hotspots were identified through software simulation, and then their locations and temperatures were confirmed through an actual experiment using in-mould thermocouples. Two different machine learning approaches, artificial neural network (ANN) and support vector regression (SVR), were developed using the extracted data from the sensors and a design of experiment (DOE) method. The performance of linear and Gaussian kernels was compared for the SVR method. The Gaussian SVR resulted in superior performance compared to the linear kernel. The Gaussian SVR was then compared to the ANN prediction method, where ANN showed a slightly better prediction performance. This study has two primary outcomes. First, we show the simulation results can be used to identify critical areas of the part for real-time monitoring. Secondly, embedding sensors in these locations and applying the collected data for a machine learning analysis, provides a good indication of potential quality issues such as warpage and shrinkage post-production. The use of ANN indicates an accurate prediction performance, facilitating rapid optimisation of the process for the minimisation of hotspots.
UR - http://www.scopus.com/inward/record.url?scp=85177443165&partnerID=8YFLogxK
U2 - 10.1109/CoDIT58514.2023.10284132
DO - 10.1109/CoDIT58514.2023.10284132
M3 - Conference contribution
AN - SCOPUS:85177443165
T3 - 9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 2023
SP - 309
EP - 314
BT - 9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Control, Decision and Information Technologies, CoDIT 2023
Y2 - 3 July 2023 through 6 July 2023
ER -