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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.

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.

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.

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.

Virtual human interactions provide an important avenue for training as emergent opportunities arise. In response to a new training need, we propose a framework to rapidly create experiential learning opportunities in the form of a question--answer chat interaction with virtual humans. This framework takes quickly generated case documents and breaks down the case into small tasks that can be crowdsourced by nonexperts. This framework can serve as a first step to rapidly bootstrapping new virtual humans. We have applied our framework to the task of preparing health care students and professionals to infrequent, but high-stakes, situations such as infectious diseases, cranial nerve disorders, and stroke. Our framework was utilized by medical professionals interested in providing new training experiences to students and colleagues. Over the course of two months, these professionals created seven scenarios on a diverse range of topics that included Ebola, cancer, and neurological disorders. These scenarios were developed for multiple target audiences such as medical students, residents, and fellows. As a first step, each scenario utilized our framework and crowdsourced workers to create an initial corpus over the course of two days. From these seven cases, we selected two to evaluate the quality of the resulting virtual-human corpuses. The two scenarios were compared to preexisting reference scenarios that have been in curricular use for several years. We found a reduction in author time commitment of at least 92% while creating a character that was at least 75% as accurate as its reference counterparts. The commitment reduction and accuracy achieved by our framework represents a first step towards rapid development of a virtual human. Our framework can then be combined with other creation processes for further virtual-human development in order to create a mature virtual human. As part of a virtual-human development process, our framework can help to rapidly develop new scenarios in response to emergent training opportunities.