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Conventional robotic wire arc additive manufacturing technologies enable the rapid production of moderate-sized components using low-cost wire feedstocks and robotic welding systems. Efforts to date have primarily focused on single robot solutions. However, new configurations are possible with coordination of multiple robots and multi-degree of freedom positioners. This paper describes a new multi-agent control paradigm that enables multiple robots to work collaboratively on manufacturing a single component on a rotating platform. The advantages of this approach are increased deposition rate and productivity. This paper demonstrates this control strategy on a 19 degrees-of-freedom platform based on three wire arc additive systems surrounding a single rotating platform.

Wire-arc direct energy deposition (wire-arc DED) has been developed to manufacture large-scale metal products with high deposition rates, low material cost, and high material efficiency. However, dynamically varying printing conditions and complex geometries frequently lead to unfavorable part distortions during and after printing which are magnified as part sizes increase. In this study, an effective computational simulation method was developed for large-scale 316 L stainless steel parts using finite element method. The model was validated with the measured distortion using a 3D laser scanner. The distribution of deviation is within 16 % (=1.6 mm) against a measured value for a 483.6 mm tall part with 248 layers, with excellent agreement with the spatial pattern of distortion. The dynamic part deformation during printing and cooling was tracked using vision camera to investigate the thermo-mechanical deformation mechanism. The result showed that long pauses during machine maintenance pauses have strong influence on part distortion.

At the onset of the COVID-19 (Coronavirus Disease 2019) pandemic, USA faced supply chain issues causing a nationwide shortage of N95 respirators and PAPR devices. Researchers at Oak Ridge National Laboratory's (ORNL) Manufacturing Demonstration Facility (MDF) were sent to work from home to help slow the spread of the pandemic, and during that time they were tasked with using their manufacturing expertise to research alternative methods of producing face masks. To rapidly iterate on face mask designs without access to a research or manufacturing facility, 3D (3-Dimensional) printing and silicone casting were used. Molds and components for the masks are modeled with computer-aided design (CAD), printed with common home desktop 3D printers, and test poured with skin-safe silicone rubber. The resultant masks are form fitting and comfortable to wear. To ease the shortage of N95 filter media, the masks are designed to use existing filter materials such as home HVAC (heating, ventilation, and air conditioning) filters. The masks can be sanitized, and the filter replaced to allow the same mask to be reused. The result is not equivalent to an N95 mask, and not meant to be a replacement, but is meant to be a stopgap mask that is still able to provide the wearer filtered protection. Filtration testing has not been performed because it is not the intention of the authors to make medical claims. This research is meant to demonstrate the rapid design, iteration, and manufacturing processes of a stopgap mask.

Metal Big Area Additive Manufacturing (MBAAM), an additive manufacturing based on wire-arc process, is progressively evolving from rapid prototyping to the industrial scale production. In MBAAM, the height of printed part can easily reach eight feet, and the printing can last for hours or days. For such large printed structures, distortion and residual stress management are primary challenges in production process. Although transient thermo-mechanical simulations with very small time increments have resulted in accurate process predictions on small parts, such time resolutions are not computationally feasible for large components. Hence, the time increment in thermo-mechanical simulations of large structures needs to be evaluated with respect to simulation accuracy and computational feasibility. In this work, two thin walls were printed using MBAAM, and temperature and process parameters were recorded and used to calibrate and validate the model results. The part distortion and residual stresses were measured before and after stress relaxation by neutron beam diffraction in High Flux Isotope Reactor (HFIR). These measurements were compared to the predicted simulation results. In this work, we investigated the robustness of the computational model and the effects of time increment magnitude on the large-scale MBAAM simulations in terms of accuracy and model efficiency. We found that a coarse time increment of 20 s effectively captured the overall part distortion, but the model was not able to capture the development of residual stresses in the base plate. We determined a combination of fine and coarse time increments that offers an optimal computational efficiency and accuracy for residual stress prediction. A fine time increment of 1 s can be used to resolve the thermal interactions between the wall and base plate during the printing of the first few layers, as well as for other transitions in the geometry or process conditions. These findings provide general guidelines for selection of simulation time increments and offer a general understanding of the effect of time increments on computational efficiency and accuracy in prediction.