Browse Topic: Inspections
ABSTRACT Machine learning (ML), artificial intelligence (AI), and computational photography (CP) are pushing the boundaries of how electro-optical (EO) and infra-red (IR) sensors are being used. Especially within military environments, users are asking much more from EO and IR sensor suites. While hardware capability continues to advance the state of the art, software has become the true differentiator for how well these sensor platforms perform for the warfighter. This paper presents work that Consolidated Resource Imaging (CRI) has been developing in the areas of machine learning and computational photography. In this effort, we will discuss two areas of understanding: imagery meant for machine vision and imagery meant for human consumption. We will show how the intersection of machine learning and computational photography allow the symbiotic relationship between the human and the computer. Citation: A. Paul Skentzos, B. Stephen Pizzo, “Balancing Between Computer and Machine Vision
ABSTRACT Optical distortion measurements for transparent armor (TA) solutions are critical to ensure occupants can see what is happening outside a vehicle. Unfortunately, optically transparent materials often have poorer mechanical properties than their opaque counterparts which usually results in much thicker layups to provide the same level of protection. Current standards still call for the use of a double exposure method to manually compare the distortion of grid lines. This report presents provides a similar method of analysis with less user input using items typically available in many mechanics labs: machine vision cameras and digital image correlation software. Citation: J. M. Gorman, “An Easier Approach to Measuring Optical Distortion in Transparent Armor”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2020. The views presented are those of the author and do not necessarily represent the views of DoD or
ABSTRACT Active thermography has been demonstrated to be an effective tool for detection of near-surface corrosion hidden under paint, as well as hidden material loss due to corrosion. Compared to established point inspection techniques (e.g. ultrasound, eddy current), thermography offers fast, wide-area inspection of flat or curved surfaces that does not require direct contact or coupling. In its simplest form, it can be used to perform qualitative inspection using a heat gun or lamp and an uncooled IR camera. Recent developments in thermographic signal processing, coupled with improved IR camera and thermal excitation technology have resulted in significant advances in resolution, sensitivity and probability of detection of near and far-surface corrosion, and the ability to perform quantitative characterization of corrosion
ABSTRACT The effective and safe use of Rough Terrain Cargo Handlers is severely hampered by the operator’s view being obstructed. This results in the inability to see a) in front of the vehicle while driving, b) where to set a carried container, and c) where to maneuver the vehicles top handler in order to engage with cargo containers. We present an analysis of these difficulties along with specific solutions to address these challenges that go beyond the non-technical solution currently used, including the placement of sensors and the use of image analysis. These solutions address the use of perception to support autonomy, drive assist, active safety, and logistics
Fangzheng Liu, Nathan Perry, Tobias Roeddiger, Sean Auffinger, Joseph Paradiso, Ariel Ekblaw MIT Media Lab Cambridge, MA
Pipeline inspection is a crucial aspect of maintaining the integrity, safety, and reliability of the planet’s energy infrastructure. However, due to cost and scale challenges, infrastructure operators struggle to conduct accurate, large-scale inspections. A French startup, HyLight, offers a solution to precisely detect issues on the infrastructure, such as methane leaks on pipelines and defects on power lines at an industrial scale, without emitting greenhouse gases
Pick-and-place machines are a type of automated equipment used to place objects into structured, organized locations. These machines are used for a variety of applications — from electronics assembly to packaging, bin picking, and even inspection — but many current pick-and-place solutions are limited. Current solutions lack “precise generalization,” or the ability to solve many tasks without compromising on accuracy
Designing non-destructive test (NDT) systems for aerospace clients can feel like engineering with blindfolds on. Even when the parts under test aren’t confidential, they can change rapidly as companies optimize their designs. This accelerated innovation helps launch more powerful, safer vehicles for use inside Earth’s orbit and beyond. But how do you create precision inspection systems without knowing what they’ll inspect in the field
This SAE Aerospace Recommended Practice establishes the requirements and procedures for eddy current inspection of open fastener holes in aluminum aircraft structures
This specification establishes the requirements for etch inspection of steel parts to detect overheating (rehardening or over-tempering) caused by abusive machining or grinding or to detect localized discontinuous carburization
Manually checking the quality of components or products in industry is labor-intensive for employees and error-prone on top of that. The Fraunhofer Institute for Mechatronic Systems Design IEM is unveiling a solution that provides total versatility in this area. In an it’s OWL supported collaboration with Diebold Nixdorf and software specialist verlinked, Fraunhofer IEM has created a combination of collaborative robot (cobot), AI-based image analysis and IoT platform. The system frees employees from having to perform visual inspections and can be incorporated into all kinds of testing scenarios. The Fraunhofer researchers presented a demonstrator of the cobot/IoT platform at the 2024 Hannover Messe Trade Show in February
A company says that its digital twin alignment system, incorporating a sophisticated AI algorithm and an off-the-shelf camera, has the potential to revolutionize the auto industry, potentially saving it up to a staggering $20 billion in the effort to detect defects on the manufacturing line. Generally, such inspections of spot welds, bolt holes and the like are handled one of three ways: Slow manual inspections that can have high error rates. Even slower inspection with coordinate-measuring machines (CMMs) that can take hours to inspect 150 spot welds. Tremendously expensive technology, such as lasers, that still aren't perfect
This specification establishes the classification, technical requirements, tests, and test procedures for the qualification, approval, and quality verification of all materials used in the liquid penetrant methods of inspection with the exception of those excluded in the application section
This recommended practice establishes the requirements and procedures for Barkhausen Noise (BN) inspection of ferromagnetic steel components. See Appendix B for a list of common materials for BN inspection. Applications of the method are listed in 1.2 through 1.5
This specification covers a polysulfide sealing compound with low adhesive strength, supplied as a two-component system that cures at room temperature
This specification establishes nondestructive testing methods, sampling frequency, and acceptance criteria for the inspection of metal castings
In the 1990s and early 2000s, the field of parallel kinematics was viewed as being potentially transformational in manufacturing, having multiple potential advantages over conventional serial machine tools and robots. Many prototypes were developed, and some reached commercial production and implementation in areas such as hard material machining and particularly in aerospace manufacturing and assembly. There is some activity limited to niche and specialist applications; however, the technology never quite achieved the market penetration and success envisaged. Yet, many of the inherent advantages still exist in terms of stiffness, force capability, and flexibility when compared to more conventional machine structures. This chapter will attempt to identify why parallel kinematic machines (PKMs) have not lived up to the original excitement and market interest and what needs to be done to rekindle that interest. In support of this, a number of key questions and issues have been identified
Additive manufacturing (AM) is currently being used to produce many aerospace components, with its inherent design flexibility enabling an array of unique and novel possibilities. But, in order to grow the application space of polymer AM, the industry has to provide an offering with improved mechanical properties. Several entities are working toward introducing continuous fibers embedded into either a thermoplastic or thermoset resin system. This approach can enable significant improvement in mechanical properties and could be what is needed to open new and exciting applications within the aerospace industry. However, as the technology begins to mature, there are a couple of unsettled issues that are beginning to come to light. The most common question raised is whether composite AM can achieve the performance of traditional composite manufacturing. If AM cannot reach this level, is there enough application potential to warrant the development investment? The answers are highly
This technical paper reports the development of an automatic defect detector utilizing deep learning for “polished skins”. Materials with a “polished skin” are used in the fabrication of the external plates of commercial airplanes. The polished skin is obtained by polishing the surface of an aluminum clad material, and they are visually inspected, which places a significant burden on inspectors to find minute defects on relatively large pieces of material. Automated inspection of these skins is made more difficult because the material has a mirror finished surface. Defects are broadly classified into three categories: dents, bumps, and discolorations. Therefore, a defect detector must be able to detect these types of defects and measure the defects’ surface profile. This technical paper presents details related to the design and manufacture of an inexpensive automated defect detector that demonstrates a sufficiently high level of performance. The system employs multiple line sensor
This SAE Aerospace Recommended Practice (ARP) covers visible surface defects on aerospace hose assemblies which have been installed and are functioning within a working environment at the time of visual inspection
This SAE Aerospace Standard (AS) covers water conditioning agents used to facilitate aqueous wet-method magnetic particle inspection
This specification covers a procedure for revealing the macrostructure and microstructure of titanium alloys
When deploying robots in an industrial setting, one of the primary goals is performance. In an industrial robot workcell, performance is often measured as cycle time: the time required to complete a set of tasks. Typical tasks include painting, welding, and inspecting. Regardless of the tasks, the goal is to complete them as fast as possible, so that the workcell can begin work on the next set of tasks. A long cycle time for a given cell can cause that cell to become the bottleneck on an assembly line
Ultrasonic Testing (UT) is a typical Non-destructive testing (NDT) method for examining the structural components for aircraft production. Manufacturing aircraft made of fiber metal laminates (FML) includes cascaded steps such as placement of aluminum, glass prepreg, adhesive, doublers, stringers, vacuum bagging and curing in an autoclave. Quality control (QC) is performed first at the layup of the component (without stringers) after curing and the quality assessment is visually evaluated. The manually performed examination of anomalies is very time-consuming. In addition, conducted NDT inspection using a manual UT phased array for Glass Reinforced (GLARE®) FML of A380, it lacked the high capacity of data and additionally an evaluation software
Machine vision dates back to the beginning of the modern industrial robot age in the 1980s. Augmenting collaborative robots (or ‘cobots’) with vision allows them to perform with higher precision, flexibility, and intelligence. However, integration is not a one-size-fits-all process as the specific requirements of each application can vary greatly
This standard establishes the requirements for performing and documenting FAI. It is emphasized the requirements specified in this standard are complementary (not alternative) to customer and applicable statutory and regulatory requirements
With funding from the US Departments of Transportation, Energy, Defense, and others, Airborne LiDAR Pipeline Inspection Sensor (ALPIS®) has evolved from a simple proof of concept model to a fully capable and successful commercial airborne pipeline inspection system. The ALPIS® system has undergone a long development period
Nearly every company in the world performs some level of quality inspection on their products before delivering them to customers. If you’re in the downloadable software business, this might involve making sure the product is bug-free and easy to use. But in the realm of physical products, the appearance of the product is nearly as important as its functionality. Would you want to purchase a new car that has scratches on the bumper or hubcaps? What if there was a crack in the windshield? From large to small, the same is true of many other items including appliances, laptops, cellphones, watches, and earbuds
Light from an object contains continuous various colors, the spectrum of light, that result from the interaction between light and the object. Spectral measurement is thus the basis of remote sensing, allowing for highly accurate material analysis and image recognition. Although the world is full of colors, human being and standard color cameras receive light through their eyes/sensors and perceive it as only three primary colors of red (R), green (G), and blue (B). Hyperspectral (HS) imaging is a technology that splits and detects light into more colors than humans and color cameras can. The richer spectral information of HS image is promising for machine vision to provide more information than human eyes or color cameras in visual inspection of foods, industrial products, and so on
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