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Leveraging Advanced Computation to better employ Additive Manufacturing

Description:

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software, Advanced Materials, Human-Machine Interfaces

 

OBJECTIVE: This topic seeks to advance the science of Additive Manufacturing by developing advanced in-process monitoring and parameter optimization to the 3D printing capability of local users throughout the Army.

 

DESCRIPTION: The Army could greatly enhance its utilization of Additive Manufacturing (AM) by better leveraging advanced computational tools and instrumentation.  A more robust implementation of AM could make invaluable contributions to fielded capabilities and Soldier Lethality, as well as future systems such as Next Generation Combat Vehicle (NGCV), Future Vertical Lift (FVL), and Long Range Precision Fires (LRPF).  One of the greatest handicaps faced by AM thus far has not been the printer hardware itself, but rather the ability to trust that prints have been optimally executed [5].  Subsequently, the quality of printed parts and their respective material properties is typically interrogated by destructive testing and/or the contemporaneous printing of test coupons.  This post-hoc analysis is an inefficient and impractical exercise for many printer operators, especially those in remote locations.  Monitoring prints in real-time, however, could alleviate the need for this after-the-fact verification and constitute a significant advancement in the Science of Additive Manufacturing.  Using sensors and printer outputs to collect data, and then statistically correlating that data with resultant material properties, can yield instantaneous confirmation that the print is of baseline quality.  Such a method could be significantly enhanced with advanced computational methods such as artificial intelligence and machine learning.  Furthermore, the correlations between input and output can be employed not only to verify the printer output, but also to enhance the printer inputs [4].  A feedback loop using these same computational tools can be leveraged to optimize the parameters and settings of the printer to factor in material selection, part requirements, and environmental conditions.  This approach could even identify and control non-intuitive contributing factors to print quality.  Thus, it would be desirable for the Army to develop and field a printer kit featuring both the real-time monitoring and verification of the printing process, in addition to the fine-tuning of setup parameters.  The desire for these AM-augmenting functions is not here newly articulated, having rather been under investigation for some time by various institutions.  However, such investigations have typically been in a sanitary, high-resource environment using particular printer platforms [1], [2], [3].  The novelty of the proposed kit, then, is in the implementation of these features in a way that is accessible to non-experts and modular for interface with a variety of printer systems. 

 

The proliferation of both AM hardware and widely-accessible advanced computational tools make the time ripe to develop this next advancement in the Science of Additive Manufacturing.  Thus, this topic seeks to develop a modular kit, consisting of sensor, software, and computational tools, to augment the AM process.  This product would afford users the ability to verify the quality of each printed part, but also ensure that the material properties of the part are optimized.  A higher-quality and higher-confidence AM capability would immensely assist forward assets and Soldier Lethality, as well as affording FVL, NGCV, and LRPF far greater design space.  A successful execution and implementation of this topic would thus assist both the direct users and operators of AM as well as the Army in general.

 

PHASE I: Identify the COTS hardware, software, and computational products relevant to this application, and begin combining them into a benchtop prototype.  Initially orient the prototype toward optimizing a polymer FDM system.  All prototypes must cohere with Army IT security protocols.  This prototype should be demonstrated to generate recommended parameters for print an Army-relevant polymer, as well as pass/fail determination in real-time.  The prototype will be evaluated by comparing test parts/coupons printed using optimized parameters against those printed by stock/automatic machine parameters.  Additionally, a methodology for modularizing the prototype (necessary for commercial viability) should be outlined.

 

PHASE II: Transition the benchtop prototype from Phase 1 into a modular kit capable of interfacing with different 3D printers and different materials.  Develop a robust user-interface that makes the data accessible to AM technicians and machine operators.  Begin testing the kit on different FDM systems and high temperature materials.  Demonstrate the prototype’s expanded modular capability by successfully using it on three different machines and 3 different materials.  Outline a way in which this prototype could be modified/replicated to function with Laser Powder Bed Fusion process.

 

PHASE III DUAL USE APPLICATIONS: This technology has tremendous use-case applications within not only the Army, or DoD as a whole.  It could revolutionize many aspects of AM in general.  Potential transition points and commercial markets include fabrication/manufacturing entities, biomedical institutions, research institutions, and auto manufacturers.  Such an “AM enhancement kit” could be sold as a standalone product, or marketed to 3D Printer manufacturers as an upgrade for their operating protocols.

 

REFERENCES:

  1. https://www.ornl.gov/news/inspection-method-increases-confidence-laser-powder-bed-fusion-3d-printing;
  2. https://www.ornl.gov/news/ai-software-enables-real-time-3d-printing-quality-assessment;
  3. https://www.sciencedirect.com/science/article/abs/pii/S221486042300430X?dgcid=author;
  4. https://commons.erau.edu/cgi/viewcontent.cgi?article=1655&context=edt;
  5. https://www.sciencedirect.com/science/article/abs/pii/S2214860420311210

 

KEYWORDS: Additive Manufacturing, optimization, 3D printing, in-situ monitoring, material properties

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