TY - GEN
T1 - 3D Printing of a Leaf Spring: A Demonstration of Closed-Loop Control in Additive Manufacturing
AU - Garanger, Kevin
AU - Khamvilai, Thanakorn
AU - Feron, Eric
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-18
PY - 2018/10/26
Y1 - 2018/10/26
N2 - 3D printing is rapidly becoming a commodity. However, the quality of the printed parts is not always even nor predictable. Feedback control is demonstrated during the printing of a plastic object using additive manufacturing as a means to improve macroscopic mechanical properties of the object. The printed object is a leaf spring made of several parts of different infill density values, which are the control variables in this problem. In order to achieve a desired objective stiffness, measurements are taken after each part is completed and the infill density is adjusted accordingly in a closed-loop framework. With feedback control, the absolute error of the measured part stiffness is reduced from 11.63% to 1.34% relative to the specified stiffness. This experiment is therefore a proof of concept to show the relevance of using feedback control in additive manufacturing. By considering the printing process and the measurements as stochastic processes, we show how stochastic optimal control and Kalman filtering can be used to improve the quality of objects manufactured with rudimentary printers.
AB - 3D printing is rapidly becoming a commodity. However, the quality of the printed parts is not always even nor predictable. Feedback control is demonstrated during the printing of a plastic object using additive manufacturing as a means to improve macroscopic mechanical properties of the object. The printed object is a leaf spring made of several parts of different infill density values, which are the control variables in this problem. In order to achieve a desired objective stiffness, measurements are taken after each part is completed and the infill density is adjusted accordingly in a closed-loop framework. With feedback control, the absolute error of the measured part stiffness is reduced from 11.63% to 1.34% relative to the specified stiffness. This experiment is therefore a proof of concept to show the relevance of using feedback control in additive manufacturing. By considering the printing process and the measurements as stochastic processes, we show how stochastic optimal control and Kalman filtering can be used to improve the quality of objects manufactured with rudimentary printers.
UR - https://ieeexplore.ieee.org/document/8511509/
UR - http://www.scopus.com/inward/record.url?scp=85056859252&partnerID=8YFLogxK
U2 - 10.1109/CCTA.2018.8511509
DO - 10.1109/CCTA.2018.8511509
M3 - Conference contribution
SN - 9781538676981
SP - 465
EP - 470
BT - 2018 IEEE Conference on Control Technology and Applications, CCTA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -