Abstract
This paper describes the development of an artificial neural networks-based in-process flash monitoring system (ANN-IPFM) in the injection molding process. This proposed system integrates two sub-systems. One is the vibration monitoring sub-system that utilizes an accelerometer sensor to collect and process vibration signals during the injection molding process. The other, a threshold prediction sub-system, predicts a control threshold based on the process parameter settings, thus allowing the system to adapt to changes in these settings. The integrated system compares the monitored vibration signals with the control threshold to predict whether or not flash will occur. The performance of the ANN-IPFM system was determined by using varying ratios of polystyrene (PS) and low-density polyethylene (LDPE) in the injection molding process, and comparing the number of actual occurrences of flash with the number of occurrences predicted by the system. After a 180 trials, results demonstrated that the ANN-IPFM system could predict flash with 92.7% accuracy.
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Chen, J., Savage, M., Zhu, J.(. (2007). Development of Artificial Neural Networks-Based In-Process Flash Monitoring (ANN-IPFM) System in Injection Molding. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_141
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DOI: https://doi.org/10.1007/978-3-540-72395-0_141
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