An application of spatial Iterative Learning Control to micro-additive manufacturing

2016 ◽  
Author(s):  
Zhi Wang ◽  
Christopher Pannier ◽  
Lauro Ojeda ◽  
Kira Barton ◽  
David J. Hoelzle
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning

Spatial Iterative Learning Control for Multi-material Three-Dimensional Structures

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Zahra Afkhami ◽  
Christopher Pannier ◽  
Leontine Aarnoudse ◽  
David Hoelzle ◽  
Kira Barton
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Single Layer ◽  
Learning Control ◽  
Iterative Learning ◽  
Layer By Layer ◽  
Additive Process ◽  
Jet Printing

Abstract Iterative learning control (ILC) is a powerful technique to regulate repetitive systems. Additive manufacturing falls into this category by nature of its repetitive action in building three-dimensional structures in a layer-by-layer manner. In literature, spatial ILC (SILC) has been used in conjunction with additive processes to regulate single-layer structures with only one class of material. However, SILC has the unexplored potential to regulate additive manufacturing structures with multiple build materials in a three-dimensional fashion. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying initial conditions, system parameters, and surface interaction dynamics in different layers of multi-material structures. In this paper, SILC is used as a recursive control strategy to iteratively construct the feedforward signal to improve part quality of 3D structures that consist of at least two materials in a layer-by-layer manner. The system dynamics are approximated by discrete 2D spatial convolution using kernels that incorporate in-layer and layer-to-layer variations. We leverage the existing SILC models in literature and extend them to account for the iteration varying uncertainties in the plant model to capture a more reliable representation of the multi-material additive process. The feasibility of the proposed diagonal framework was demonstrated using simulation results of an electrohydrodynamic jet printing (e-jet) printing process.

scholarly journals Multi-Layer Spatial Iterative Learning Control for Micro-Additive Manufacturing

2019 ◽  
Vol 52 (15) ◽  
pp. 97-102 ◽  
Author(s):  
Leontine Aarnoudse ◽  
Christopher Pannier ◽  
Zahra Afkhami ◽  
Tom Oomen ◽  
Kira Barton
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning

Resistance based iterative learning control of additive manufacturing with wire

Mechatronics ◽  
2015 ◽  
Vol 31 ◽  
pp. 116-123 ◽  
Author(s):  
Petter Hagqvist ◽  
Almir Heralić ◽  
Anna-Karin Christiansson ◽  
Bengt Lennartson
Keyword(s):  
Additive Manufacturing ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning
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