Tackling residual stress in large-scale additive manufacturing

Large-scale thermoplastic polymer extrusion-based additive manufacturing (AM) has been used to fabricate precast concrete formworks. There are some limitations inherent to the large-scale AM process that need to be overcome to design complex, multipart additively manufactured formworks to be used for precast concrete. This research work uses a large-scale polymer composite AM process to manufacture two-part formworks. Postprocessing was used to repair imperfections, create smooth casting surfaces, achieve precise dimensional tolerance, and incorporate assembly mechanisms for multipart formwork. Two biodegradable polymer composites (wood-fiber polylactic acid and wood-fiber amorphous polylactic acid) and a conventional polymer composite (carbon fiber acrylonitrile butadiene styrene) were selected to manufacture four sets of two-part formwork. Design details, including the cellular infill pattern, continuous toolpath, and layer time selection, are presented. Postprocessing and repairs performed on the manufactured formworks to get the required dimensional tolerance and surface smoothness are discussed.

Additive manufacturing (AM) of large-scale polymer and composite parts using robotic arms integrated with extruders has received significant attention in recent years. Despite the contributions of great technical progress and material development towards optimizing this manufacturing method, different failure modes observed in the final printed products have hindered its application in producing large engineering structures used in aerospace and automotive industries. We report failure modes in a variety of printed polymer and composite parts, including fuel tanks and car bumpers. Delamination and warpage observed in these parts originate mostly from thermal gradients and residual stresses accumulated during material deposition and cooling. Because printing large structures requires expensive resources, process simulation to recognize the possible failure modes can significantly lower the manufacturing cost. In this regard, accurate prediction of temperature distribution using thermal simulations is the first step. Finite element analysis (FEA) was used for process simulation of large-scale robotic AM. The important steps of the simulation are presented, and the challenges related to the modeling are recognized and discussed in detail. The numerical results showed reasonable agreement with the temperature data measured by an infrared camera. While in small-scale extrusion AM, the cooling time to the glassy state is less than 1 s, in large-scale AM, the cooling time is around two orders of magnitudes longer.

Large-scale additive manufacturing (LSAM) has a similar mechanism to the fused filament fabrication (FFF) and is capable of fabricating a part in large size. This capability provides LSAM with potentials in a variety of industries, including aerospace and automotive manufacturing. Product quality and production efficiency are two main concerns, as LSAM is implemented. It has been proven that print surface temperature is a major factor that impacts the quality of final products. Therefore, it needs to be controlled throughout the process. As an infrared camera is implemented, the real-time data of surface temperature of parts are available. A dynamic approach is studied in this article to perform real-time layer time control based on the real-time data from the infrared camera to improve both product quality and production efficiency. A regression model is formulated and proved to fit the cooling dynamics. To deal with the layerwise change of cooling dynamics, due to humidity and airflow, the Gaussian process is used to keep the regression model updated. The regression model together with the Gaussian process can predict the surface temperature of a part accurately, even in a dynamic environment. This method to predict surface temperature is then combined into an optimization model for real-time layer time control. Specifically, more than one position on the surface is monitored and considered in the optimization model, and the resulting layer time for each layer by solving the optimization model has the quality requirement satisfied and improves production efficiency. The improved system performance is presented in a case study. This article provides practitioners of LSAM with a useful tool to improve the process through manufacturing automation. Note to Practitioners-Carbon fiber reinforced thermoplastic material is used for large-scale additive manufacturing (LSAM) to fabricate parts in large size. This technology is new compared with other additive manufacturing technologies, and several key problems are to be addressed before it is widely applied in industry. One issue is product quality, which depends largely on print surface temperature. Quality problems caused by improper print surface temperature include cracking, warping, and deformation. Another problem is the operation inefficiency, which results in a high cost. Currently, it takes hours to print a single part. This article provides a framework to improve both quality and efficiency of LSAM by employing the real-time data captured from the infrared thermal camera. Specifically, a regression model is formulated to describe the surface temperature with high accuracy. Then, a layer time control method is proposed to schedule printing operations in real time to guarantee high printing efficiency and quality.

Photopolymer formulation towards large scale additive manufacturing of autoclave capable tooling

Wire Arc Additive Manufacturing by Robot Manipulator: Towards Creating Complex Geometries

The global coronavirus disease (COVID)-19 pandemic has led to an international shortage of personal protective equipment (PPE), with traditional supply chains unable to cope with the significant demand leading to critical shortfalls. A number of open and crowdsourcing initiatives have sought to address this shortfall by producing equipment such as protective face shields using additive manufacturing techniques such as fused filament fabrication (FFF). This paper reports the process of designing and manufacturing protective face shields using large-scale additive manufacturing (LSAM) to produce the major thermoplastic components of the face shield. LSAM offers significant advantages over other additive manufacturing technologies in bridge manufacturing scenarios as a true transition between prototypes and mass production techniques such as injection molding. In the context of production of COVID-19 face shields, the ability to produce the optimized components in under 5 min compared to what would typically take 1 – 2 h using another additive manufacturing technologies meant that significant production volume could be achieved rapidly with minimal staffing.

Characterizing material transitions in large-scale Additive Manufacturing

Large-scale additive manufacturing (LSAM) has been developing a huge potential to address certain tasks in industrial applications over the last years. Particularly granule extrusion technologies enable the processing of an enormous variety of materials but also introduce new challenges in printing large-scale parts. Compared to fused layer modelling and due to larger nozzle diameters as well as higher extrusion rates strand geometry and consequently, process-related voids are enlarged. A promising approach to improve part quality is the integration of carbon dioxide laser (CO2) radiation into the additive manufacturing process to weld deposited strands by increasing the interface temperature. Experiments are conducted for polymethyl methacrylate (PMMA) and styrene-acrylonitrile (SAN) which are very good absorbers of the wavelength 10.6 µm. Due to the locally defined heat treatment, merely certain areas of the strands are heated to a desired temperature. This leads to a more complete diffusion. At the same time, temperature gradients in the overall part are avoided. By means of a thermographic camera, the temperatures at the re-melting process of deposited strands can be precisely monitored. Therefore, the relation between laser intensity and resulting temperature can be transferred into a repeatable process window. The interaction between laser and deposited material leads to a wider contact area between stacked strands. While flexural strength is not significantly affected, compared to specimens manufactured without any heat treatment bending force is increased by 66% (PMMA) and 48% (SAN), respectively. In addition, voids between adjacent strands are reduced by up to 57%.

Large scale additive manufacturing has process mechanisms similar to Fused Filament Fabrication (FFF) and can print parts with large sizes, such as car bodies. This capability provides large scale additive manufacturing great application potentials in a variety of industries, including aerospace, automotive manufacturing, and construction. To make large scale additive manufacturing a viable manufacturing solution, both production efficiency and product quality need to be considered. Specifically, the printing process is subject to constraints on print surface temperature to guarantee good product quality. In this paper, we propose a novel method for controlling layer time during the printing process by using thermal images. Specifically, several thin wall test components are printed by the Thermwood Large Scale Additive Manufacturing (LSAM™) machine, and the print surface temperature is monitored by a FLIR™ thermal camera. A regression model based on real-time thermal imaging data is built to predict the surface temperature. Then a layer time control method is proposed based on the temperature prediction model. By comparing the proposed method to the fixed layer time policy, the experiment results suggest that the control method by using real-time data can siginificantly reduce the layer time, and subsequently the total printing time, while satisfying the quality requirement.

An innovative digital image correlation technique for in-situ process monitoring of composite structures in large scale additive manufacturing

Cellulose is the most abundant and broadly distributed organic compound and industrial by-product on Earth. However, despite decades of extensive research, the bottom-up use of cellulose to fabricate 3D objects is still plagued with problems that restrict its practical applications: derivatives with vast polluting effects, use in combination with plastics, lack of scalability and high production cost. Here we demonstrate the general use of cellulose to manufacture large 3D objects. Our approach diverges from the common association of cellulose with green plants and it is inspired by the wall of the fungus-like oomycetes, which is reproduced introducing small amounts of chitin between cellulose fibers. The resulting fungal-like adhesive material(s) (FLAM) are strong, lightweight and inexpensive, and can be molded or processed using woodworking techniques. We believe this first large-scale additive manufacture with ubiquitous biological polymers will be the catalyst for the transition to environmentally benign and circular manufacturing models.

Genesis of a novel high-rate composite manufacturing process using large-scale additive manufacturing – compression molding (AM-CM) system: Possibilities and limitations∗

Additive Manufacturing (AM) has the potential to completely reshape the manufacturing space by removing the geometrical constraints of commercial manufacturing and reducing component lead time, especially for large-scale parts. Coupling robotic systems with direct energy deposition (DED) additive manufacturing techniques allow for support-free printing of parts where part sizes are scalable from sub-metre to multi-metre sizes. This paper offers a holistic review of large-scale robotic additive manufacturing, beginning with an introduction to AM, followed by different DED techniques, the compatible materials and their typical as-built microstructures. Next, the multitude of robotic build platforms that extend the deposition from the standard 2.5 degrees of freedom (DOF) to 6 and 8 DOF is discussed. With this context, the decomposition and slicing of the computerized model will be described, and the challenges of planning the deposition trajectory will be discussed. The different modalities to monitor and control the deposition in an attempt to meet the geometrical and performance specifications are outlined and discussed. A wide range of metals and alloys have been reported and evaluated for large-scale AM parts. These include steels, Ti, Al, Mg, Cu, Ni, Co–Cr and W alloys. Different post-processing steps, including heat treatments, are discussed, along with their microstructures. This paper finally addresses the authors' perspective on the future of the field and the largest knowledge gaps that need to be filled before the commercial implementation of robotic AM.

The parts produced by additive manufacturing are inherently subjected to discretization effects due to their layer-based addition. The stair-stepping effect on the surface quality is inevitable for most of the techniques and it becomes more dominant for the regions having small surface inclinations. The stair-stepping influences the mechanical properties as well as the aesthetic perception. Many researchers have been presented several approaches to overcome or minimize the stair-stepping effects and improve the surface quality of additively manufactured parts. The attempts have been made generally for the FDM-printed objects, however, there is no or fewer efforts have been made for parts of large-scale additive manufacturing (LSAM). Due to higher deposition rates (up to 50 kg/hrs.) and larger nozzle diameters (i.e. bead size) the discretization effect is more in large-scale additive manufacturing. In this paper, the presented methods to mitigate the stair-stepping effect and improving the surface quality of additive manufacturing are reviewed and practicing in large-scale 3D printing is discussed. A preliminary experimental study of 3D printing with a non-planar toolpath was carried out and the results were presented.

Recent advances in large-scale thermoplastic additive manufacturing (AM), using fused deposition modelling (FDM), have shown that the technology can effectively produce large aerospace tools with common feed stocks, costing 2.3 $/kg, such as a 20% carbon-filled acrylonitrile butadiene styrene (ABS). Large-scale additive manufacturing machines have build-volumes in the range of cubic meters and use commercially available pellet feedstock thermoplastics, which are significantly cheaper (5–10 $/kg) than the filament feedstocks for desktop 3D printers (20–50 $/kg). Additionally, large-scale AM machines have a higher material throughput on the order of 50 kg/h. This enables the cost-efficient tool production for several industries. Large-scale 3D-printed tooling will be computerized numerical control (CNC)-machined and -coated, to provide a surface suitable for demolding the composite parts. This paper outlines research undertaken to review and improve the adhesion of the coating systems to large, low-cost AM composite tooling, for marine or infrastructure composite applications. Lower cost tooling systems typically have a lower dimensional accuracy and thermal operating requirements than might be required for aerospace tooling. As such, they can use lower cost commodity grade thermoplastics. The polymer systems explored in the study included polypropylene (PP), styrene-maleic anhydride (SMA), and polylactic acid (PLA). Bio-based filler materials were used to reduce cost and increase the strength and stiffness of the material. Fillers used in the study included wood flour, at 30% by weight and spray-dried cellulose nano-fibrils, at 20% by weight. Applicable adhesion of the coating was achieved with PP, after surface treatment, and untreated SMA and PLA showed desirable coating adhesion results. PLA wood-filled composites offered the best properties for the desired application and, furthermore, they have environment-friendly advantages.