Intelligent control of ISBM process for recycled PET bottles

Intelligent control of ISBM process for recycled PET bottles

HAN William, KERFRIDEN Pierre, VIORA Laurianne, COMBEAUD Christelle, BOUVARD Jean-Luc, CANTOURNET Sabine

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Abstract. To manufacture plastic bottles with an increased ratio of rPET (recycled Polyethylene terephthalate), the ISBM (Injection Stretch Blow Moulding) process must be controlled to account for the variable mechanical and thermal properties. Calibration and optimization of the process have been successfully realized in past works but cannot be used for real-time applications. To address this, a gaussian process regression model of the free blowing step is created. It can calibrate itself using the pressure curve from a previous blowing to obtain near instantaneous predictions of key properties of the bottle. To create the model, the process’ characteristics are studied. Finite element simulations of the blowing where the properties follow a multivariate gaussian distribution are used to train the artificial intelligence. Then, an example is shown using the artificial intelligence predictions to optimize the thickness distribution of a bottle after blowing.

PET, Free Injection Stretch Blow Process, Machine Learning, Gaussian Process Regression

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: HAN William, KERFRIDEN Pierre, VIORA Laurianne, COMBEAUD Christelle, BOUVARD Jean-Luc, CANTOURNET Sabine, Intelligent control of ISBM process for recycled PET bottles, Materials Research Proceedings, Vol. 41, pp 1817-1826, 2024


The article was published as article 201 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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