Showcasing his research about bioprinting, Shah Limon, a Slippery Rock University associate professor of engineering, recently had two articles published in the Journal of Manufacturing Science and Engineering.
Bioprinting is a cutting-edge technology that combines 3D printing with biology, using living cells and biomaterials to create complex, three-dimensional structures like tissues and organs.
Limon’s article titled “Data-Driven Optimization of Bioink Formulations for Extrusion-Based Bioprinting: A Predictive Modeling Approach” investigated the role of machine learning to streamline the historically slow and resource heavy processes that have been used to optimize material properties and bioprinting performance. This is accomplished through a decision tree model that significantly reduced the trial-and-error phase of experimentation through optimizing viscosity of bioinks.
Another article, titled “Integrating Decision Trees and Clustering for Efficient Optimization of Bioink Rheology and 3D Bioprinted Construct Microenvironments,” was covered the use of machine learning-based computational tools to optimize bioprinting processes.
Limon has additional work published in IEEE Xplore that describes a machine learning approach to predict network component degradation to support reliability in network health monitoring.
