Download or read book Data-driven Modeling Implementation Within Materials Development and Manufacturing Systems written by Allen Jonathan Roman and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting polymeric material behavior during processing and predicting final part properties continues to be a strong research focus within the scientific community as it involves taking into consideration a wide range of time-dependent variables. By use of data-driven modeling, the materials development process can be accelerated, and the highly predictive modeling techniques can facilitate the development of smart manufacturing systems. This dissertation worked on solving polymer engineering problems by use of data-driven modeling techniques. The first strategy was using data-driven modeling to provide a predictive model with statistical insights of the injection molding process to ensure part quality is maximized for a highly viscoelastic material blend. By injection molding highly viscoelastic materials, the probability of part defects is increased, therefore, it was crucial to use advanced computational techniques to understand the nuances of this highly non-linear process and to predict the outcome before creating material waste from faulty trials. The second strategy was in the use of data-driven modeling for reverse engineering purposes, specifically within materials development. By combining experimental characterization and data-driven modeling, algorithms were developed and compared to prove how highly predictive models can be used as reverse engineering toolboxes. This ultimately informed users of the optimal formulation which would reach the specified target material properties. The final strategy explored using data-driven modeling to validate the high influence of viscous heating within the pressure melt removal process, therefore, work was done in implementing a viscous heating system within a fused filament fabrication (FFF) 3D printer to accelerate the 3D printing process. The instrumented FFF 3D printer proved capable of accelerating print speeds and improving mechanical performance of 3D printed parts, working towards solving two of the largest bottlenecks within additive manufacturing: lead times and part quality. Given the unique capabilities of the data-driven modeling, the novel 3D printer was tested and evaluated via data-driven modeling to provide statistical information regarding which processing parameters were the most influential for improving overall performance of the 3D printing system. The results of this work provide a basis for future research endeavors related to combining data-driven modeling and polymer science, such as in optimizing the newly developed viscous heating 3D printer.