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Typically, before constructing an object with an additive manufacturing system, the 3D object must be sent through a process called slicing. Slicing converts a 3D object commonly in the form of an STL file into a set of layers by horizontally intersecting a plane with the object at various heights. At each height, called a layer, multiple 2D polygons can be generated. Each polygon represents a boundary for solid geometry and is called an island. Each island is then comprised of multiple path types in an attempt to optimally fill the polygon. To move between each island and each islands’ paths, travels are inserted. Travels are simply motion by the system to move from one area of construction to another. Travels do not contribute to the construction of the object, and so, are considered wasted motion. In large-scale additive manufacturing, objects can be quite large and the distance between islands can be large as well. As a result, these travels can waste a significant amount of time. Ideally, travels would be as short as possible, however, computing global minimal travel paths is computationally expensive. To combat this problem, researchers at Oak Ridge National Lab developed a GPU-based approach to travel insertion based on a unique factoradic representation. This representation was then utilized by the GPU to solve the Traveling Salesman Problem (TSP). This algorithm was able to compute global minimal travel paths quickly resulting in faster object construction. A general investigation was also carried out to determine when a GPU vs CPU implementation would be beneficial.

The potential for site-specific, process-parameter control is an attribute of additive manufacturing (AM) that makes it highly attractive as a manufacturing process. The research interest in the functionally grading material properties of numerous AM processes has been high for years. However, one of the issues that slows developmental progress in this area is process planning. It is not uncommon for manual programming methods and bespoke solutions to be utilized for site-specific control efforts. This article presents the development of slicing software that contains a fully automated process planning approach for enabling through-thickness, process-parameter control for a range of AM processes. The technique includes the use of parent and child geometries for controlling the locations of site-specific parameters, which are overlayed onto unmodified toolpaths, i.e., a vector-based planning approach is used in which additional information, such as melt pool size for large-scale metal AM processes, is assigned to the vectors. This technique has the potential for macro- and micro-structural modifications to printed objects. A proof-of-principle experiment is highlighted in which this technique was used to generate dynamic bead geometries that were deposited to induce a novel surface embossing effect, and additional software examples are presented that highlight software support for more complex objects.

Defects can result in a failed part and are costly in terms of time and material. This cost is even greater in the context of large-scale additive manufacturing where the objects can be very large. As a result, a great deal of research has focused on defect identification and mitigation. To address defects during object construction, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated an in-situ control system comprised of two sensors: a thermal camera and laser profilometer. This control system adjusted material flow and build speed to mitigate three types of defects: low layer times, underfill, and overfill. Several test objects were constructed. The control system was found to adjust build parameters to handle low layer times of approximately 15 seconds and height deviations from -100% underfill (the absence of a layer) to 50% overfill. Within two layers, height deviations could be returned to within 10% of the expected layer height. Further, preliminary results suggest the system can compensate for uneven build surfaces.

Big Area Additive Manufacturing (BAAM) is a large format additive manufacturing (AM) process. The size scale has enabled many new applications for AM. However, at this scale, lack of high print resolution and extruder flowrate control lead to potentially significant geometric deviations in the printed part. This paper examines strategies to improve geometric quality in BAAM parts. Multi-resolution printing, extrusion diversion, and feedforward extruder control are examined herein. These methods were all found to be effective in mitigating phenomena detrimental to geometric part quality on the BAAM process.

Industry 4.0 represents a significant change in manufacturing with the inclusion of such technologies as additive manufacturing and data analytics. In support of these changes, infrared thermal imaging (IR) and very-near infrared (VNIR) have proven invaluable in numerous applications related to additive manufacturing. In this review, these applications will be presented as they relate to polymer and metal-based additive manufacturing processes. Applications including in-situ characterization, post-build analysis, experimental setups, modeling, and machine learning will be covered. With a general overview complete, the review will conclude with a discussion of challenges identified throughout the literature and thoughts on future trends.

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Ideally, objects are constructed as quickly as possible; however, short build times for a layer can present an issue. Short layer build times do not provide enough time for the material to cool sufficiently. As a result, an object cannot support its own weight nor deal with overhanging features. Additionally, the generation of support structures may be impractical either due to geometry or, in the case of large-scale polymer additive manufacturing, due to being difficult to remove. To address these issues, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated the use of a thermal camera mounted to a gantry to collect data on an object under construction. This data provided feedback for an in-situ control system to adjust layer build times. Adjustments were made in the form of additional waiting to allow the material to cool to specific thermal thresholds. This additional cooling time allowed the construction of objects with low layer build times or overhanging features that would have otherwise failed.

Additive manufacturing (AM), more commonly referred to as 3D printing, is revolutionizing the manufacturing industry. With any new technology comes new rules and guidelines for the optimal use of said technology. Big Area Additive Manufacturing (BAAM), developed by Cincinnati Incorporated and Oak Ridge National Laboratory’s Manufacturing Demonstration Facility, requires a host of new design parameters compared to small-scale 3D printing to create large-scale parts. However, BAAM also creates new possibilities in material testing and various applications in the manufacturing industry. Most of the design constraints of small-scale polymer 3D printers still apply to BAAM. Beyond those constraints, new rules and limitations exist because BAAM’s large-scale system significantly changes the thermal properties associated with small-scale AM. This work details both physical and software-related design considerations for additive manufacturing. After reading this guide, one will have a better understanding of slicing software’s capabilities and limitations, different physical characteristics of design and how to apply them appropriately for AM, and how to take the inherent nature of AM into consideration during the design process.

Industry 4.0 represents the next evolution of industrial technology. Some of the key characteristics of Industry 4.0 include augmented reality and additive manufacturing. In support of this evolution, researchers at (blinded) created a prototype augmented reality (AR) interface, ASMARI. This prototype acts as a common interface for both industrial additive and subtractive machines. Additionally, in the changing industrial workspace of Industry 4.0, the role of humans will also change. This interface focuses on increasing productivity and situational awareness, while providing new interaction modalities for interacting with hardware. The prototype was also evaluated by multiple users who provided positive feedback on the system. Ultimately, the prototype represents a first step towards significant integration between augmented reality and additive manufacturing.

Defect identification and mitigation is an important avenue of research to improve the overall quality of objects created using additive manufacturing (AM) technologies. Identifying and mitigating defects takes on additional importance in large-scale, industrial AM. In large-scale AM, defects that result in failed prints are extremely costly in terms of time spent and material used. To address these issues, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated the use of a laser profilometer and thermal camera to collect data concerning an object as it was constructed. These data provided feedback for an in situ control system to adjust object construction. Adjustments were made in the form of automated height control. This paper presents results for both a polymer- and metal-based system. Object construction for both systems was improved significantly, and the resulting objects were more geometrically identical to the ideal 3D representation.

Physician empathy is a complex phenomenon known to improve illness outcomes; however, few tools are available for deliberate practice of empathy. We used a virtual patient (VP) to teach empathic communication to first-year medical students. We then evaluated students' verbal empathy in a standardized patient (SP) interaction.Seventy medical students, randomly assigned to 3 separate study groups, interacted with (1) a control VP portraying depression, (2) a VP with a backstory simulating patient shadowing, or (3) a VP able to give immediate feedback about empathic communication (empathy-feedback VP). Subsequently, the students interviewed an SP portraying a scenario that included opportunities to express empathy. All SP interviews were recorded and transcribed. The study outcomes were (1) the students' verbal response to the empathic opportunities presented by the SP, as coded by reliable assessors using the Empathic Communication Coding System, and (2) the students' responses as coded by the SPs, using a communication checklist.There were no significant differences in student demographics between groups. The students who interacted with the empathy-feedback VP showed higher empathy in the SP interview than did the students in the backstory VP and the control VP groups [mean (SD) empathy scores coded on a 0-6 scale were 2.91 (0.16) vs. 2.20 (0.22) and 2.27 (0.21), respectively). The difference in scores was significant only for the empathy-feedback VP versus the backstory VP group (P = 0.027). The SPs rated the empathy-feedback and the backstory VP groups significantly higher than the control VP group on offering empathic statements (P0.0001), appearing warm and caring (P = 0.015), and forming rapport (P = 0.004).Feedback on empathy in a VP interaction increased students' empathy in encounters with SPs, as rated by trained assessors, whereas a simulation of patient shadowing did not. Both VP interventions increased students' empathy as rated by SPs, compared with the control VP group.