Applied Predictive Machine Learning Algorithms in Bio-Composite Manufacturing and Development of Real-Time Digital Process Twin
Date of Award
Master of Science
Timothy M. Young
Dr. Bogdan Bichescu, Dr. Sheng-I Yang, and Dr. Micholas Smith
Much of the manufacturing industry is focused towards transitioning from Industry 3.0 towards Industry 4.0. Data science is the basis of Industry 4.0 for the bio-composites industry.
The bio-composites industry consists of enterprises which produce products from renewable raw materials. This research project was applied, and predictive models were developed of the strength properties of final products for a particleboard mill. Quality control measures in bio-composite manufacturing tend toward reactivity given the time from when samples are taken from the production line and destructive test data are presented to operations personal. The implementation of a feedback system for final board strength properties will allow for a more proactive approach to quality control and process improvement. A gap in the literature, identified by Liao et al (2017) found most Industry 4.0 research done in labs (95%), leaving little applied research (5%). This research aims for a better knowledge of how useful and feasible implementing machine learning algorithms in a bio-composite manufacturing setting are.
Quality control lab data was combined with process data to create fifteen predictive models. The merged data sets established a link between final board strength properties and process parameters from sensors along the process. The database starts March 3rd, 2021 and ends January 4th, 2022. The dataset was divided into seven product-grade categories by board strength property and grade. The algorithm ‘Multivariate Adaptive Regression Splines - four interaction terms’ (MARS4) performed best across all product categories in 10 validation studies. The R2 of MARS4 in ten validation studies ranged from 0.588 to 0.845 (𝑋¯ = 0.731) for all internal bond product-grade categories. The R2 of MARS4 in ten validation studies ranged from 0.745 to 0.923 (𝑋¯ = 0.826) of all modulus of rupture categories. Weighted predictions of the three top performing models (MARS4, MARS3, MARS2) in validation had promising results.
Automated decision making, which is powered by predictive analytics, is a foundation of Industry 4.0, which goes beyond Industry 3.0. The real-time feedback loop had to be simulated in this study due to an unforeseen occurrence and the subsequent disconnection of the modeling servers from the process servers.
Ericksen, Chloe M., "Applied Predictive Machine Learning Algorithms in Bio-Composite Manufacturing and Development of Real-Time Digital Process Twin. " Master's Thesis, University of Tennessee, 2022.
Available for download on Monday, May 15, 2028