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Abstract This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives.
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Lunar Infrastructure via Microscale Regolith Assembly
Multiscale Granular Stacking (MSGS) is a technology for assembling planetary-surface infrastructure from unprocessed regolith. The unprocessed grains serve directly as additive manufacturing feedstock in a process that exploits their natural variation in size and shape. With precise, single-grain scanning, computation, and packing, MSGS minimizes and potentially eliminates the need for adhesives, fluids, and other binders, saving the associated mass and energy. Preliminary calculations suggest that MSGS requires less mass transport and energy for construction than traditional terrestrial building methods, drastically reducing the reliance on earth resources for sustaining a deep-space human presence and long-term exploration goals. Constructing a desired structure may require stacking millions of grains which demands extensive computation. Packing solutions with many objects exist in the literature, e.g. some versions of the knapsack problem. However, micro- to macro-scale particle dry stacking itself has never been investigated, let alone in the context of space additive manufacturing. Modeling these fabrication process dynamics as a discrete-step linear system allows for tuning of parameters such as build speed, surface finish, and contour smoothing while providing the opportunity to leverage controls theory for determining system convergence, steady-state error, and overshoot of desired build height. This paper details attempts to bring multivariable control theory to bear on additive manufacturing by using feedback on overall build geometry, a technique proven to yield more accurate results than using feedback at the process level in traditional additive manufacturing.
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- Award ID(s):
- 1846340
- PAR ID:
- 10224845
- Date Published:
- Journal Name:
- AIAA 2021-0148 Session: Flight Systems and In-Space Infrastructure
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2514/6.2021-0148
