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This laboratory exercise seeks to develop a cost effective prototype development. The exercise has the potential of linking part design, CAD, mold development, quality control, metrology, mold flow, materials testing, fixture design, automation, limited parts production and other issues as related to plastics manufacturing.
This viewgraph presentation provides an overview of the Space Shuttle Thermal Protection System (TPS) and the various products incorporated in the TPS. There are three tile systems which include pure silica, fibrous refractory composite insulation (FRCI), and alumina enhanced thermal barrier (AETB). Coating systems include reaction cured glass (RCG) and toughened uni-piece insulation (TUFI). The microstructures of these systems are explored as are the manufacturing processes associated with each. Microstructural investigation using methods such as automated X-ray spectral image analysis (AXSIA) is a crucial part of understanding the mechanical nature of these systems.
Current inspection methods for complex shapes and contours exemplified by aircraft engine turbine blades are expensive, time-consuming and labor intensive. The logistics support of new manufacturing paradigms such as integrated product-process development (IPPD) for current and future engine technology development necessitates high speed, automated inspection of forged and cast jet engine blades, combined with a capability of retaining and retrieving metrology data for process improvements upstream (designer-level) and downstream (end-user facilities) at commercial and military installations. The paper presents the opportunities emerging from a feasibility study conducted using 3-D holographic laser radar in blade inspection. Requisite developments in computing technologies for systems integration of blade inspection in production are also discussed.
Pixelated plastic scintillator arrays can serve as high efficiency and high resolution neutron imaging detectors. Manufacturing these arrays is intensive in both time and labor. This article presents a fabrication method based on additive manufacturing for two-dimensional plastic organic scintillator arrays using a custom-built automated assembly machine and a custom photocurable resin that has significant non-aromatic acrylate oligomer content. The process involves two main stages: fully autonomous production of one-dimensional layered arrays, followed by semi-autonomous cutting and stacking to form two-dimensional pixel arrays. One-dimensional arrays were manufactured at a rate of around 4 layers per hour with minimal defects and tight dimensional tolerances, while two-dimensional arrays up to 7 x 7 pixels and 70 mm in length were completed in approximately 3.5 hours. Final arrays exhibited dimensional deviations of less than 0.5 mm. Two-dimensional arrays read out by a multi-anode photomultiplier tube demonstrated per-pixel position resolution and pulse-shape discrimination, enabling gamma–neutron interaction separation in mixed radiation environments.
Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.
The addition of automation has greatly extended humans' capability to accomplish tasks, including those that are difficult, complex and safety critical. The majority of Human - Automation Interact~on (HAl) results in more efficient and safe operations, ho,,:,ever ~ertain un~~pected a~tomatlon behaviors or "automation surprises" can be frustrating and, In certain safety critical operations (e.g. transport~tion, manufacturing control, medicine), may result in injuries or. the loss of life.. (Mellor, 1994; Leveson, 1995; FAA, 1995; BASI, 1998; Sheridan, 2002). This pap~r describes ~he development of a design tool that enables on the rapid development and evaluation. of automat~on prototypes. The ultimate goal of the work is to provide a design platform upon which automation surprise vulnerability analyses can be integrated.
Mass driver tugs can be designed to move Apollo and Amor asteroids at opportunities of low velocity increment to the vicinity of the earth. The cost of transferring asteroids through a velocity interval of 3 km/sec by mass driver is about 16 cents per kilogram amortized over 10 years, about ten times less than that required to retrieve lunar resources during the early phases of a program of space manufacturing. About 22 per cent of a 200-meter diameter asteroid could be transferred to high earth orbit by an automated 100 megawatt solar-powered mass driver in a period of five years for a cost of approximately $1 billion. Estimates of the total investment of a space manufacturing program could be reduced twofold by using asteroidal instead of lunar resources; such a program could begin several years sooner with minimal concurrent development if asteroidal search programs and mass driver development are immediately accelerated.
A present disclosure relates to a system for automatic design and manufacturing of 3D printing units and 3D products. The system is configured to obtain specification of a target structure corresponding to a 3D product; automatically determine a design and a printing path of a 3D printing unit based on the specification of the target structure, wherein the 3D printing unit is a micronozzle unit configured to print the target structure of the 3D product; automatically determine a manufacturing procedure to print the 3D printing unit based on the specification of the target structure; and instruct a 3D printer to print the 3D printing unit according to the manufacturing procedure and the printing path of the 3D printing unit.
The Robotic Augmentation & Novel Applications team at Los Alamos National Laboratory is working to produce autonomous systems to augment staffing for tasks that are tedious, dangerous, or require high precision. This is intended to reduce risks to staff, programs and facilities while also freeing up personnel resources to better support laboratory missions, particularly pit manufacturing. The primary focus of this effort is to utilize cutting edge automation to augment long duration radiation surveys, allowing RCTs to work on critical production activities.
Projections of the U.S. electrical power demands over the next 30 years indicate that the U.S. could be in grave danger from power shortages, undesirable effluence, and thermal pollution. An appraisal of nonconventional methods of producing electrical power is conducted, giving particular attention to the conversion of solar energy into commercial quantities of electrical power by solar cells. It is found that 1% of the land area of the 48 states could provide the total electrical power requirements of the U.S. in the year 1990. The ultimate method of generating vast quantities of electrical power would be from a series of synchronous satellites which beam microwave power back to earth to be used wherever needed. Present high manufacturing costs of solar cells could be substantially reduced by using massive automated techniques employing abundant low cost materials.
The intelligence processing equipment (IPE) research and development (R&D) programs of the Department of Commerce are carried out within the National Institute of Standards and Technology (NIST). This institute has had work in support of industrial productivity as part of its mission since its founding in 1901. With the advent of factory automation these efforts have increasingly turned to R&D in IPE. The Manufacturing Engineering Laboratory (MEL) of NIST devotes a major fraction of its efforts to this end while other elements within the organization, notably the Material Science and Engineering Laboratory, have smaller but significant programs. An inventory of all such programs at NIST and a representative selection of projects that at least demonstrate the scope of the efforts are presented.
To create a small, portable, fully automated biosensor, a compact means of fluid handling is required. We designed, manufactured, and tested a "fluidics cube" for such a purpose. This cube, made of thermoplastic, contains reservoirs and channels for liquid samples and reagents and operates without the use of any internal valves or meters; it is a passive fluid circuit that relies on pressure relief vents to control fluid movement. We demonstrate the ability of pressure relief vents to control fluid movement and show how to simply manufacture or modify the cube. Combined with the planar array biosensor developed at the Naval Research Laboratory, it brings us one step closer to realizing our goal of a handheld biosensor capable of analyzing multiple samples for multiple analytes.
The V-Man project has developed an intuitive authoring and intelligent system to create, animate, control and interact in real-time with a new generation of 3D virtual characters: The V-Men. It combines several innovative algorithms coming from Virtual Reality, Physical Simulation, Computer Vision, Robotics and Artificial Intelligence. Given a high-level task like "walk to that spot" or "get that object", a V-Man generates the complete animation required to accomplish the task. V-Men synthesise motion at runtime according to their environment, their task and their physical parameters, drawing upon its unique set of skills manufactured during the character creation. The key to the system is the automated creation of realistic V-Men, not requiring the expertise of an animator. It is based on real human data captured by 3D static and dynamic body scanners, which is then processed to generate firstly animatable body meshes, secondly 3D garments and finally skinned body meshes.
Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.
Hybrid manufacturing is a promising route for producing complex aerospace components, yet systematic cost benchmarking across multiple additive-subtractive pathways remains limited. This study presents a comprehensive process-based cost analysis of seven hybrid manufacturing routes, including laser powder bed fusion (L-PBF), powder- and wire-directed energy deposition (DED), wire arc additive manufacturing (WAAM), additive friction stir deposition (AFSD), metal binder jetting (MBJ), and agility forging, followed by scanning and finish machining. Parametric cost models incorporating direct material, labor, and energy costs were developed. L-PBF results are discussed in detail for a pickle fork component and directly compared with commercial pricing. Across all hybrid routes, labor emerged as the dominant cost driver, contributing more than 70% of total manufacturing cost in some cases. AFSD exhibited the lowest cost for aluminum components, with MBJ being its 316 L stainless steel counterpart, after accounting for geometric scaling. Benchmarking against industrial quotes suggests that hybrid manufacturing can achieve cost levels comparable to those of commercial services, although labor-intensive processes exhibit greater deviation. The analysis highlights automation of material handling, setup, and supervision as key opportunities for improving economic competitiveness. Overall, the proposed framework provides a quantitative basis for evaluating and optimizing hybrid manufacturing pathways for aerospace applications.
A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (L C2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined L C2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.
Increased application of polymer matrix composite (PMC) materials in large vehicle structures requires consideration of non-autoclave manufacturing technology. The NASA Composites for Exploration project, and its predecessor, Lightweight Spacecraft Structures and Materials project, were tasked with the development of materials and manufacturing processes for structures that will perform in a heavy-lift-launch vehicle environment. Both autoclave and out of autoclave processable materials were considered. Large PMC structures envisioned for such a vehicle included the payload shroud and the interstage connector. In this study, composite sandwich panels representing 1/16th segments of the barrel section of the Ares V rocket fairing were prepared as 1.8 m x 2.4 m sections of the 10 m diameter arc segment. IM7/977-3 was used as the face-sheet prepreg of the autoclave processed panels and T40-800B/5320-1 for the out of autoclave panels. The core was 49.7 kg/sq m (3.1 lb/cu ft (pcf)) aluminum honeycomb. Face-sheets were fabricated by automated tape laying 153 mm wide unidirectional tape. This work details analysis of the manufactured panels where face-sheet quality was characterized by optical microscopy, cured ply thickness measurements, acid digestion, and thermal analysis.
Increased application of polymer matrix composite (PMC) materials in large vehicle structures requires consideration of non-autoclave manufacturing technology. The NASA Composites for Exploration project, and its predecessor, Lightweight Spacecraft Structures and Materials project, were tasked with the development of materials and manufacturing processes for structures that will perform in a heavy-lift-launch vehicle environment. Both autoclave and out of autoclave processable materials were considered. Large PMC structures envisioned for such a vehicle included the payload shroud and the interstage connector. In this study, composite sandwich panels representing 1/16th segments of the barrel section of the Ares V rocket fairing were prepared as 1.8 m x 2.4 m sections of the 10 m diameter arc segment. IM7/977-3 was used as the face-sheet prepreg of the autoclave processed panels and T40-800B/5320-1 for the out of autoclave panels. The core was 49.7 kilograms per square meters (3.1 pounds per cubic feet (pcf)) aluminum honeycomb. Face-sheets were fabricated by automated tape laying 153 mm wide unidirectional tape. This work details analysis of the manufactured panels where face-sheet quality was characterized by optical microscopy, cured ply thickness measurements, acid digestion, and thermal analysis.