Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning
Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000 µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. We also introduce for the first time in the AM in situ sensing literature the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the belief of the authors that future studies should adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.
🪱🤖 GE Develops Worm-Inspired Robot For On-Wing Engine Inspections
Resembling an inchworm, the Sensiworm (Soft ElectroNics Skin-Innervated Robotic Worm) uses untethered soft robotics technology to move easily through the nooks, crannies and curves of jet engine parts to detect defects and corrosion. The robot is also able to measure the thickness of an engine’s thermal barrier coatings.
Developed in partnership with SEMI Flex Tech, Binghamton University and UES, Inc., Sensiworm is controlled by an operator using a device that GE says is similar to a gaming controller and can be programmed to follow specific paths. “It has a sticky, suction-like bottom that enables it to climb and adhere to steep surfaces. Also, because the robot is very soft and compliant, it won’t harm any surfaces or cause any damage during an inspection,” says a spokesperson for GE.
According to GE, Sensiworm could reduce unnecessary engine removals and downtime, enabling faster turnarounds. Although Sensiworm is currently focused on engine inspections, Trivedi says the OEM is developing new capabilities that would enable the robot to execute repairs once it finds a defect.
🗜️ Can OES Provide Inclusion Analysis during Steel Production?
Non-metallic inclusions are of considerable importance for the steel industry due to the dramatic influence that even small amounts of them may have on properties, mechanical and otherwise, of the metal or on the production process itself. Inclusions can have positive effects and may increase the value of the steel, but most often inclusions signify quality problems and reduced value.
The modern reference for inclusion analysis is the SEM/EDX (scanning electron microscope coupled with energy dispersive X-ray fluorescence spectroscopy). This analytical process takes typically several hours, including sample preparation and interpretation, which is far too long to be applicable to production control.
Within recent years we’ve seen the development of extremely fast and economical OES (optical emission spectrometry) methods which are able to provide inclusion information even during the steel production process. The method uses the principle of Single Spark Acquisition (SSA), where signals from the individual “single sparks” are not summed as in conventional OES acquisition, but processed with special algorithms.
Ultrasound Inspection Optimizes EV Battery Manufacturing
Battery cell inspection technology has been neglected in favor of other innovation categories. According to a recent MIT study, inspection has not been a factor in previous price declines and therefore increased use of cell interrogation should not come as a surprise. Seemingly this would not require an engineering leap. After all, ‘borrowed technology’ from previous chemistries and other industries has worked well enough in the past.
However, large-format cells have proven to be far more difficult to manufacture at scale, compared to their small-format counterparts that have dominated the market until recently. This difficulty is in part manifested by industry-wide low manufacturing yields. Based on reports and interviews with industry insiders, it can be estimated that large-format battery yield is somewhere between 70–90% with a ramp period of five years to reach steady state yield for a new production run.
Titan Advanced Energy Solutions (‘Titan’) is one of the companies working to meet a growing demand for better inspection technology. Their ultrasound sensing technology combined with a system-based approach to manufacturing provides early and actionable feedback to the manufacturing floor, positively impacting yield and scrap rates as well as overall cell production economics. Moreover, their scan-as-a-service business model does not burden customers with additional capital expense.
SwRI improves corrosion-detecting technology that detects leaks in pipes before they occur
“Pipeline corrosion resulting in leaks is very common,” said SwRI Staff Engineer Sergey Vinogradov, who developed the technology with Staff Engineer Keith Bartels and other SwRI staff members. “There are only a few current methods to detect defects before they cause leaks. Quite often, the pipe is repaired and re-inspected after a leak occurs. We’ve developed a technology that can consistently monitor the pipe’s condition, hopefully preventing leaks from happening in the first place.”
The technology is known as a Magnetostrictive Transducer (MsT) Collar. It was originally developed by SwRI in 2002. The updated version has a flat, thin design allowing it to be used on pipes in tight spaces. In custom configurations, it can withstand heat up to 400 degrees Fahrenheit. The new, segmented MsT design also features eight sensors that give the transducer the ability to more accurately identify where in the pipe corrosion is occurring.
Non-Destructive Testing (NDT) – Process, Types & Applications Explained
Non-destructive testing refers to the use of testing techniques that do not alter any of the properties of the tested product. These properties could be its strength, integrity, appearance, corrosion resistance, conductivity, wear resistance, toughness and so on.
When the scope of work is simple, using a single NDT process may be sufficient. But in a lot of cases, a combination of techniques and test methods are used for concrete information about the product characteristics.
The purpose of each type of testing is to ensure that we have a safe product. With destructive testing, however, the intention is to find the operational limits for a product through tests such as fatigue and tensile tests. On the other hand, with NDT, we check whether a manufactured product or one that is already in service is good enough to function satisfactorily in its service environment. We may also use it to assess the extent of wear and tear such as the use of ultrasonic thickness measurement for steel plating of ships.
Novel Predictive Tool Tests the Durability of Composite Materials
Field experts will assess the extent of the aircraft damage using ultrasound equipment. This information will be used by Davidson’s developed predictive tool for computational tests to calculate the composite structure’s operating life and failure risk. The study also seeks to provide answers to the issues of what repairs are necessary, how long it will take to complete those repairs, and whether the aircraft is now safe to fly.
“The data obtained from the field teams is often incomplete. I’m infilling missing data using machine learning and computational tools to determine composite life, durability and safety. We’ll do the impacts and stress tests on the aircraft composites virtually,” Davidson adds.
Behind the Foldable Phones in Our Pockets
Detecting dangerous gases to improve safety and reduce emissions
The primary advantage of differential optical absorption spectroscopy is its scalability. Two elements are required: a calibrated light source tuned to emit a specific wavelength, and a receiver able to read the same wavelength. In some cases, the receiver must also read a reference source for comparison. The two elements can be within the same housing to function as a point detector, but the source and receiver can also be separated, sending a beam across an open path, looking for a cloud of the target gas to move into its field of view.
A New Way to Discover a Reaction that Causes Cracks in Concrete
One phenomenon that shortens the life of concrete buildings and structures is the alkali-silica reaction (ASR). It is the reaction between alkali ions found in cement and silica, the two main components of concrete, which creates a gel that absorbs water and expands, causing internal pressures to build up within the concrete. To help identify the extent of ASR, researchers at the Argonne National Laboratory have discovered a harmless way to detect it that could reduce the level of expensive testing being done. Their new method relies on electrochemical impedance spectroscopy (EIS), which measures electrical conductivity.
Dual Linear Phased Array Corrosion Mapping
Asset health is paramount to the efficient and safe operation of facilities producing energy and manufactured goods. Ultrasonic corrosion mapping is a non-destructive testing (NDT) technique that uses data from ultrasonic measurements to map material thickness across a piece of equipment, such as tanks, pipes, and pressure vessels. The data is used to graph corrosion on the equipment for easy visual interpretation. Currently, there are a number of tools available to complete corrosion mapping inspections. However, one automated dual linear phased array technique offers increased productivity, accuracy, and data density over other methods.
Detecting Corrosion and Erosion in Horizontal Boiler Tube Assemblies
Boilers play an essential role in improving the efficiency of thermal power generation. Three boiler sections, economizer, superheater, and reheater, are tightly bundled tube assemblies inherent to the process by maintaining high temperature feedwater and steam that drives the steam turbine and generator. Tube assemblies can be vertical or horizontal, but the focus of this article are assemblies in the horizontal configuration. Because of the curved design, depth of tubing, location, and contents they are subject to a variety of corrosion and erosion mechanisms that can result in failure and unplanned outages.
The susceptibility for failure in a tube assembly is further exacerbated by inadequate inspection methods for detecting or predicting corrosion and erosion damage. However, specialized robot-based NDT techniques, such as Rapid Ultrasonic Gridding (RUG), offer unparalleled coverage and data compared to traditional methods, giving owner/operators the confidence that their equipment can operate optimally.
What to Expect From Ultrasonic Inspection
Ultrasonic testing is commonly integrated with preventive maintenance strategies. The method of inspection is widely employed across industries to evaluate the properties of material, components and structures for quality, anomalies or potential flaws without damaging the part. The aircraft industry uses it to look for wear and internal anomalies in the airplane. The railroad industry uses it to examine rails for signs of damage. In industrial manufacturing, components such as bearings, pumps, compressors, pipes and tubes can be inspected for signs of wear-and-tear.
An Automated Approach to Detecting Corrosion Under Insulation
Corrosion under insulation (CUI) is corrosion that occurs in the base metal of piping, storage tanks, pressure vessels, and other assets when moisture penetrates the outer insulation. Corrosion and damage to the insulation are difficult to detect without the costly process of removing portions of it and performing an inspection. Standard techniques help identify damage in isolated areas but are resource intensive if prioritizing the overall condition of the asset.
Alternatively, robotics-based NDT techniques, such as Rapid Ultrasonic Gridding (RUG), reveal CUI through an internal inspection without the need for scaffolding or removing insulation. This technique utilizes ultrasonic testing to measure the thickness of the insulated metal. When paired with data visualization tools, the readings are used to generate 2D or 3D corrosion heat maps of the entire asset.
Additive for Aerospace: Welcome to the New Frontier
Gao, a tech fellow and AM technical lead at Aerojet Rocketdyne, is particularly interested in the 3D printing of heat-resistant superalloys (HRSAs) and a special group of elements known as refractory metals. The first of these enjoy broad use in gas turbines and rocket engines, but it’s the latter that offers the greatest potential for changing the speed and manner in which humans propel aircraft, spacecraft, and weaponry from Point A to Point B.
“When you print these materials, they typically become both stronger and harder than their wrought or forged equivalents,” he said. “The laser promotes the creation of a supersaturated solid solution with fantastic properties, ones that cannot be achieved otherwise. When you combine this with AM’s ability to generate shapes that were previously impossible to manufacture, it presents some very exciting possibilities for the aerospace industry.”
Eric Barnes, a fellow of advanced and additive manufacturing at Northrop Grumman, says “Northrop Grumman and its customers are now in a position to more readily adopt additive manufacturing and prepare to enter that plateau of productivity because we have spent the past few years collecting the required data and generating the statistical information needed to ensure long term use of additive manufacturing in an aeronautical environment… In the future, you may be able to eliminate NDT completely. Comprehensive build data will also serve to reduce qualification timelines, and if you’re able to understand all that’s going on inside the build chamber in real-time, machine learning and AI systems might be able to adjust process parameters such that you never have a bad part.”
Robotic Inspection for Aboveground Storage Tanks
Aboveground Storage Tanks (AST) are vital assets for many industries including, power, paper and pulp, oil and gas, chemical, and even beverage production. Routine inspection of external and internal tank components is beneficial for understanding its condition and is required by federal and local laws and regulations. Robot-enabled ultrasonic testing (UT) offers a unique solution to AST inspections because they save plant operators valuable resources while providing more asset coverage and actionable data.
How to Integrate Robotic Inspections into Your Workflow
Data is the hallmark of a robotic inspection, providing up to 1,000 times more information than traditional methods. When deciding between drones and robot crawlers, data type and quality should be considered. Drones provide aerial footage and pictures, and can even provide B-scans of assets. But, as previously mentioned, this method doesn’t result in the level of quantitative data that robot crawlers can supply. Additionally, some robots are equipped with cameras to provide the best of both worlds.
AI In Inspection, Metrology, And Test
“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”
That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”