Flexible adaptation of iterative learning control with applications to synthetic bone graft manufacturing

Tissue Engineering;Automatic Control;Manufacturing;Iterative Learning Control;Synthetic Bone Graft

Hoelzle, David J.

关键词: Tissue Engineering;    Automatic Control;    Manufacturing;    Iterative Learning Control;    Synthetic Bone Graft;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/29811/Hoelzle_David.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Additive manufacturing processes are powerful tools; they are capable of fabricating structures without expensive structure specific tooling -- therefore structure designs can efficiently change from run-to-run -- and they can integrate multiple distinct materials into a single structure.This work investigates one such additive manufacturing process, micro-Robotic Deposition ($\mu$RD), and its utility in fabricating advanced architecture synthetic bone grafts.These bone grafts, also known as synthetic bone scaffolds, are highly porous three-dimensional structures that provide a matrix to support the natural process of bone remodeling.Ideally, the synthetic scaffold will stimulate complete bone healing in a skeletal defect site and also resorb with time so that only natural tissue remains.The objective of this research is to develop methods to integrate different regions with different porous microstructures into a single scaffold; there is evidence that scaffolds with designed regions of specific microstructures can be used to elicit a strong and directed bone ingrowth response that improves bone ingrowth rate and quality.The key contribution of this work is the development of a control algorithm that precisely places different build materials in specified locations, thereby the fabrication of advanced architecture scaffolds is feasible.Under previous control methods, designs were relegated to be composed of a single material.The control algorithm developed in this work is an adaptation of Iterative Learning Control (ILC), a control method that is typically best suited for mass manufacturing applications.This adaptation reorients the ILC framework such that it is more amenable to additive manufacturing systems, such as $\mu$RD.Control efficacy is demonstrated by the fabrication of advanced architecture scaffolds.Scaffolds with contoured forms, multiple domains with distinct porous microstructures, and hollow cavities are feasible when the developed controller is used in conjunction with a novel manufacturing workflow in which scaffolds are filled within patterned molds that support overhanging features.An additional application demonstrates controller performance on the robot positioning problem; this work has implications for additive manufacturing in general.