(149k) Considerations of Space Manufacturing: Utilizing Earth-Based Resources for Modeling and Control Applications While Considering Communication Delay

Habitation at distant locations in space will introduce new challenges in ensuring safety and sustainability. This is compounded by the long transportation times between Earth and any colony in space, meaning that colonists may have difficulty adapting to unpredictable concerns. Because of this, it is likely that colonists will need to be mostly self-sufficient. This includes, for example, the manufacture of replacement parts and tools or agriculture to produce food. Given that many optimization and model-based control schemes are computationally intensive, being able to utilize cloud-resources on Earth may be desired. An issue with the use of cloud computation in this case is the long communication time between Earth and other bodies in space. Other issues to consider are the ability to model complex processes for use in model-based control. In space, it is likely that available energy is limited, which could be addressed in part by using model-based controller to minimize energy usage.

For this reason, we first consider how the control of processes in space would work given long communication times, specifically by studying the speed of the process dynamics. In general, ‘fast’ processes will become unstable when controlled this way, while ‘slow’ processes may benefit from this strategy. In this analysis, we consider a continuous stirred-tank reactor with adjustable residence time, controlled by Lyapunov-based economic model predictive control (LEMPC) [1, 2]. Previously, our group demonstrated difficulties in determine best case estimates of the LEMPC parameters [3]. In this work, we study ways of improving this method, for the purpose of finding parameters for different residence times. With this, we are able to predict how ‘fast’ a process can be and still maintain stability.

Next, we discuss the modeling of methods for production in space. For the production of parts, we study an additive manufacturing method called powder bed fusion (PBF), which produces objects in a bed of powder. In PBF, which is a ‘fast’ process, each successive layer is added by melting a cross-section into the bed of powder fusing it to the previous layer, sweeping over a fresh layer of powder between each layer. Previously we adapted several sources [4-6] to create a finite element analysis (FEA) simulation in ANSYS to represent the thermal evolution of the process [7]. In this work, we first focus on the development of a coupled ANSYS thermal and structural simulation for predicting the temperatures, deformations, and stresses in the completed object. We discuss challenges in the modeling strategy, such as accounting for unconnected floating elements and large element deformations, as well as solutions to these problems. Some explored solutions include comparing the application of linear and quadratic elements, temporarily removing floating elements until they become fused to the larger part body, and appropriately setting reference temperatures. Also discussed is the method used to link the thermal profile results to the structural simulation. Since PBF has fast dynamics, we study methods of control that slow down the manufacturing process so that Earth-based resources might be usable.

[1]. Nieman, Kip, et al. "Challenges and Opportunities for Next-Generation Manufacturing in Space." IFAC-PapersOnLine 55.7 (2022): 963-968.

[2]. Hussein, Ahmed, et al. "Finite element simulation of the temperature and stress fields in single layers built without-support in selective laser melting." Materials & Design (1980-2015) 52 (2013): 638-647.

[3]. Zeng, K., et al. "Comparison of 3DSIM thermal modelling of selective laser melting using new dynamic meshing method to ANSYS." Materials Science and Technology 31.8 (2015): 945-956.

[4]. Goldak, John, Aditya Chakravarti, and Malcolm Bibby. "A new finite element model for welding heat sources." Metallurgical transactions B 15 (1984): 299-305.

[6]. Oyama, Henrique, et al. "Lyapunov-based economic model predictive control for detecting and handling actuator and simultaneous sensor/actuator cyberattacks on process control systems." Frontiers in Chemical Engineering 4 (2022): 810129.

[7]. Heidarinejad, Mohsen, Jinfeng Liu, and Panagiotis D. Christofides. "Economic model predictive control of nonlinear process systems using Lyapunov techniques." AIChE Journal 58.3 (2012): 855-870.

[8]. Ellis, Matthew, Helen Durand, and Panagiotis D. Christofides. "A tutorial review of economic model predictive control methods." Journal of Process Control 24.8 (2014): 1156-1178.