Physics-based and data-driven modeling for biomanufacturing 4.0

Bioprinting involves the fabrication of functional tissue constructs using a combination of biomaterials and it has the potential to transform regenerative medicine. However, bioprinting faces several challenges which can be attributed to its high sensitivity to the slightest variation in process parameters, material constituents, and microenvironmental conditions. This research integrates a physics-based model with a memory-based data-driven model to provide predictive capabilities for bioprinting. The hybrid approach uses the long short-term memory (LSTM) network to provide real-time predictions of the bioprinting process parameters as demonstrated by an illustrated case study. (c) 2023 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.

Automated summary

Bioprinting is a promising technology in regenerative medicine for fabricating functional tissue constructs using biomaterials. However, it faces challenges due to its sensitivity to process parameters, material constituents, and microenvironmental conditions. This research integrates physics-based and data-driven models to predict bioprinting process parameters in real-time using a hybrid approach with an LSTM network.