The lack of feedback on soil management for crop systems necessitates the collection of soil samples to derive insights into prevailing soil conditions. Achieving an accurate model of these plantations requires a substantial number of soil samples, which is a labor-intensive process that can suffer from inconsistencies in both the methodology of collection and the locations from which samples are gathered. Additionally, the close spacing of crop rows constrains the maneuverability of mobile agricultural equipment during sample collection. This manuscript proposes the use of agricultural robotics to facilitate the acquisition of composite soil samples with minimal human intervention, allowing for precise data collection for a Populus plant system. More specifically, this manuscript presents a case study on the design and construction of a compact agricultural robot for soil sample collection. The performance of the robot, including its soil collection efficacy, maneuverability, and positional accuracy, are evaluated.

Additive manufacturing (AM) of large objects has, over the last decade, required the scaling of existing material extrusion processes. The current generation of large-scale printers are primarily gantry robots with high throughput extrusion systems. With workspaces approaching 50m 3 , these printers have pushed the boundaries of achievable print volume while allowing the utilization of low-cost feedstocks, such as cementitious materials and polymer pellets, like those used in injection molding. Continued workspace expansion requires an examination of the inherent tradeoffs, which impact capital and operational costs. In this work, the authors examine these tradeoffs to determine fundamental scaling laws for existing system architectures, survey the state of the art for alternative system configurations and pose recommendations for future system designers to continue the evolution of large-scale AM systems.

The current state of smart manufacturing platforms for battery components and production remains immature, particularly in terms of scalability and system integration. This is largely due to the wide variety of equipment types and process configurations found across different manufacturing environments. Battery production processes are inherently complex, energy-intensive, and highly sensitive to variations in material properties and process conditions. Additionally, the lack of shared infrastructure and standardized, data-driven manufacturing solutions continues to hinder commercialization efforts and contributes to increased production costs. In this work, we present the development of a battery manufacturing testbed framework designed to accelerate the commercialization of innovative battery technologies. The proposed testbed will serve as a collaborative environment where stakeholders from industry, academia, and government can jointly address process challenges, conduct gap analyses, validate emerging technologies, and explore the integration of digital manufacturing tools. A particular focus is placed on advancing smart manufacturing capabilities by incorporating artificial intelligence (AI), machine learning (ML), and digital twin systems directly into battery production workflows.

Laser powder bed fusion (LPBF) enables the fabrication of intricate, geometrically complex structures with a sufficiently fine surface finish for many engineering applications with a diversity of available feedstock metals. However, the production rate of LPBF systems is not well suited for mass production in comparison to traditional manufacturing methods. LPBF systems measure their deposition rates in 100's of grams per hour, while other processes measure in kilograms per hour or even in the case of processes such as forming, stamping, and casting, 100's of kilograms per hour. To be widely adopted in industry for mass production, LPBF requires a new scalable architecture that enables many orders of magnitude improvement in deposition rate, while maintaining the geometry freedom of additive manufacturing. This article explores concepts that could achieve as much as four orders of magnitude increase in the production rate through the application of (1) rotary table kinematic arrangements; (2) a dramatic number of simultaneously operating lasers; (3) reductions of laser optic size; (4) improved scanning techniques; and (5) an optimization of toroidal build plate size. To theoretically demonstrate the possibilities of production improvements, a productivity analysis is proposed for synchronous reluctance motors with relevance to the electric vehicle industry, given the recent increase in the diversity of printable soft magnetic alloys. The analysis provides insights into the impact of the architecture and process parameters necessary to optimize rotary powder bed fusion for mass production.

3D-printing begins with the design of an object using computer aided design (CAD) software. The geometry of the object must be exported and saved in a data file format that can be used in the slicing process to generate machine instructions for printing. The standard method of saving the data is to tessellate the object as a triangulated mesh stored as a .stl file. Due to the flat triangular faces used to store the data, this mesh is a low-resolution representation of the high-fidelity object designed in CAD. The STL has its limitations with respect to geometrical accuracy, material information, and instances of invalid mesh data, but can still be used for the 3D-printing process. New file variants, such as OBJ, AMF, and 3MF, are being developed to fix some of these issues and increase the capabilities of geometry data storage for 3D-printing.

With all the necessary build pathing in place from the preceding steps, the final task is to organize the build pathing via directionality, ordering, and connectivity. Directionality controls closed-loop pathing by deciding whether construction occurs clockwise or counterclockwise. Ordering determines whether construction occurs outside-in or inside-out, by manipulating what path types will be printed first. Connectivity links all the pathing together, which is achieved through the insertion of travels. Connectivity has numerous approaches, but only the most common will be addressed in this chapter. In addition to these manipulations, other considerations, such as spiral pathing and unique cases for infill, will be discussed.

Hybrid additive manufacturing combines both additive and subtractive manufacturing processes to fabricate geometrically complex, dimensionally accurate parts that are difficult to make using either additive or subtractive manufacturing alone. Industrial applications for hybrid additive manufacturing include direct manufacturing (building whole parts), feature addition (adding onto existing parts), and remanufacturing or repair operations. To coordinate the additive and subtractive processes, computer-aided manufacturing (CAM) software must generate distinct toolpaths for each process. Each application requires different considerations and inputs when generating the toolpaths. For example, when building a part with an internal cavity that would otherwise be unmanufacturable due to reach or access limitations, a CAM must determine which layer(s) of deposition are appropriate to machine to create the internal cavity. This chapter explores the area of toolpath generation for hybrid manufacturing by discussing the necessary considerations that are unique to a hybrid process.

Closed-loop paths, also known as closed-loop contours, are paths that start and stop at the same point and are typically used to define the perimeter or outermost boundary of the polygon that represents a given layer of an object, referred to as the “layer polygon.” Finding the location of a closed-loop path involves applying two offsetting steps to the edge of the layer polygon. The offsetting steps respectively serve to (1) find the area that contains the toolpath and (2) find the centerline of that area, to define the path itself. After finding the perimeter path, the remaining geometry from the layer polygon can be used for additional path-planning steps. This chapter will discuss how the location for a closed-loop path is found, including various problem scenarios, and the different types of closed-loop paths used in additive manufacturing.

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.

Open-loop paths, as opposed to closed-loop paths, have start and end points that are at different locations. Open-loop paths, commonly called "skeletons," can be generated through multiple approaches. Most approaches utilize a shape's medial axis transform and build appropriate pathing around this representation. In this chapter, a specific implementation consisting of three major steps will be discussed. These steps are Voronoi diagram generation, skeleton pruning, and path consolidation. With the major components of the implementation covered, two additional optimizations will also be described. These two optimizations, chamfering and smoothing, will be highlighted as ways to improve overall pathing quality.

Kinematics is the study of how motion is achieved without reference to the forces that create the motion. This includes the basic structures of all robotic systems, such as links and joints, as well as their mathematical representations. This chapter will provide a brief overview of the types of motion used in additive manufacturing (AM) systems, as well as the mathematical equations that govern them. Rotations are explained using both Euler rotations and quaternions. Combined rotation and translations are shown using homogeneous transform matrices. The robot Jacobian, which relates joint velocities to the end effector translational velocities, is explained with an example calculation. This section will give the reader the basic mathematical foundation in kinematics that is necessary to understand the mechanical underpinnings of path planning for AM systems.

Directed energy deposition (DED) is a form of additive manufacturing that uses localized melted metal, similar to traditional arc welding, to construct an object. In comparison to polymer systems, metal systems can exert additional control over the build process through manipulation of the pool of melted metal, called the “melt pool.” This is especially important to maintain appropriate layer heights and create high quality builds. Additionally, metal systems allow site-specific control of the printed part, which enables advanced construction techniques that would otherwise not be possible. However, both of these types of control require support from the slicing software and, potentially, sensor feedback. This chapter will discuss these control processes and their basic implementation in a slicing program. The processes described here lay the foundation for more advanced solutions, particularly sensor feedback, that will be discussed later.

Space-filling toolpaths are used to fill whatever space remains on a layer after closed-loop contours have been planned. They provide structure to the object, both supporting the geometry and creating solid surfaces. As such, there are two main types of space-filling toolpaths: infill and skin. Infill paths are sparse and meant to cover a large area quickly. Infill is typically not visible once a print is complete, because infill is covered by skins. Skins are the solid space-filling toolpaths meant to solidify the top and bottom of the object, which, unlike the sides of the object, are not completely covered by layering contours. Space-filling paths are typically planned by projecting a pattern over the layer, clipping the pattern at the boundary of the object, and then linking the remaining portions of the pattern. A subcategory of skin paths, called gradual infill, can be employed to densify the infill when approaching a top skin layer so that the print path of the top skin is sufficiently supported. Space-filling paths come in a variety of patterns to optimize how the space is filled. This chapter will discuss space-filling path categories, how to find the space for each path type, and how to apply the path type to generate toolpaths.

Within a machine, mechanisms and motion are organized in what is known as a "kinematic arrangement," which helps classify machines based on how they move. The most common kinematic arrangements for additive manufacturing systems are Cartesian, followed by delta, and then six-degrees-of-freedom robotic arms. However, there are a multitude of less common systems, such as the SCARA, polar robots, cable driven parallel robots, mobile platforms, and multi-agent systems. This chapter surveys these various kinematic arrangements to give a broad understanding of the mechanisms underlying motion within additive manufacturing systems. Understanding these mechanisms and their resulting motion provides a framework for discussing path planning for all scales and families of additive manufacturing.

There are a variety of extrusion and deposition-based processes for additive manufacturing, such as fused deposition modeling (FDM), fused filament fabrication (FFF), and directed energy deposition (DED). These processes can use a variety of materials, including thermoplastic pellets, thermoplastic filament, thermoset, concrete, metal wire, metal powder, and more. For all these processes and materials, the underlying toolpath strategies for 3D-printing an object are the same: the object to be printed is sliced into layers, then the layer is optimally fit with toolpaths of a given width, based on specific user settings. However, based on the specific process being used, additional modifiers and toolpathing strategies may be employed to maximize the capabilities of the process and successfully construct an object. This chapter explores the different pathing considerations and additional strategies to be employed in extrusion processes that use thermoplastics, thermosets, and concretes.

Optimization of prefabricated component installation using a real-time evaluator (RTE) connection locating system Nolan W Hayes, Bryan P Maldonado, Mengjia Tang, Peter Wang, Diana Hun Pages 65-72 (2024 Proceedings of the 41st ISARC, Lille, France, ISBN 978-0-6458322-1-1, ISSN 2413-5844) Abstract: Prefabrication promises to industrialize the construction industry. By constructing elements within a manufacturing environment, producers can better control quality and maximize production efficiency. Since the major adoption of prefabrication, a wide variety of prefabricated components have been produced for varying applications such as new construction and exterior wall retrofits. While the production processes of these prefabricated components have seen much innovation, the installation process has remained relatively unchanged for decades. To innovate the installation process with modern technologies, a real-time evaluator (RTE) has been developed to reduce the installation cost of prefabricated components by reducing installation time, decreasing rework, and improving accuracy. The RTE uses developed software solutions with off-the-shelf hardware to assist erectors in completing an installation by measuring the real-time positions of connections and prefabricated components, providing installation guidance through a graphical user interface, and monitoring the accumulated installation errors. An overview of the RTE and proposed workflow is presented. A connection locating system that guides users in expediting the installation of connections is introduced. Laboratory experiments were conducted to determine the accuracy improvement and time savings of the RTE in installing connections for prefabricated components. RTE enabled a time saving of up to 37% compared to traditional connection installation methods using handheld measurement tools. Keywords: prefabrication, installation, real-time, automation, accuracy, time DOI: https://doi.org/10.22260/ISARC2024/0010 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

Traditionally, slicing processes have assumed a horizontal plane that moves up the Z axis to produce layers. However, through relatively straightforward modifications, this process can be extended to allow planar slicing at any angle and along any axis in a process referred to as off-axis slicing. Additionally, more advanced pathing solutions are moving away from planar approaches entirely by printing directly on non-flat surfaces. Both of these alternative approaches to slicing, as well as some common issues associated with them, are discussed in this chapter to provide a foundation for more advanced pathing solutions.