F-35 Lightning II是一架全天候隐身战斗机，旨在以高达1.6 MACH的速度执行战争罢工任务和电子监视能力。
F-35 Lightning II是一架全天候隐身战斗机，旨在以高达1.6 MACH的速度执行战争罢工任务和电子监视能力。Composites comprise 35% of the airframe weight, with the majority being bismaleimide, and some carbon nanotube-reinforced epoxy — which has a tensile strength of approximately 100 times greater than steel. Any deviations in external dimensions can interfere with stealth capabilities, and at supersonic speeds, prove catastrophic to both plane and pilot.
Therefore, it is critical that the “as-built” metrology is confirmed as “near perfect” to the design. Currently, inspection is done by hand at Lockheed Martin’s production facility in Fort Worth, Texas. Gantries are assembled above the craft, and workers use handheld scanners extended at arm’s length. This manual process introduces variables and also puts workers at risk as they attempt to straddle the smooth-contoured surfaces of the aircraft. To generate faster results while increasing accuracy and safety for all involved — factory worker, customer, and user — the transition to digital inspection is required.
“There needs to be a bold first adopter of aerial digital inspection in the industry, and Lockheed Martin hopes to fulfill that role with this use case,” said Chris Colaw, Lockheed Fellow, Quality & Mission Success. “This is important in the quality space because it helps us peel away the reliance on so much human involvement.”
目前，许多设备允许在较小级别上对组件进行数字测量。但是，问题在于如何扩展现有的商业设备以检查大型，完全建造的项目，例如大型飞机，船舶，甚至潜艇，同时仍然保持紧张的公差。
Currently, inspection is done by hand at Lockheed Martin’s production facility in Fort Worth, Texas. Gantries are assembled above the craft, and workers use handheld scanners extended at arm’s length.
“For 100 years in aviation, we have been using humans for quality assurance, but roughly 75% of my costs come from inspection, and 66% of that requires humans to perform, which involves some degree of subjectivity,” noted Colaw. “But in our digital future, we need to embrace things in a different way.”
Lockheed Martin sought an automated metrology solution to confirm and document measurements to the tightest tolerances in a faster, more accurate, and more repeatable process.
“我们对数字未来的外观进行了自己的研究，但是我们选择与CAD / CAM合作，因为他们有机会与一家扫描仪公司和德克萨斯大学进行无人机控制，” Colaw说。“这使我们免于必须自己完成所有工作。合作是很自然的。”
在2021年春季，CAD / CAM服务获得了小型企业技术转移研究（STTR）奖，以解决F-35战斗机的表面计量问题。它的工作是组装一个行业领先的供应商团队，该团队将向洛克希德·马丁（Lockheed Martin）提供无人机安装的扫描仪，该扫描仪可以准确（±0.025mm）测量大型组件并将该数据传输到最终创建CAD文件的系统，以最终为首次文章检查创建CAD文件或维护目的。该公司位于德克萨斯州，自1988年以来一直在全球提供3D建模和CAD转换服务。
CAD / CAM Services的首席执行官Scott Shuppert表示：“我们正在采用各种商业，现成的组件，对它们进行稍微修改，并将其捆绑在一起，以创建一个新系统来解决行业所需的问题。”
The synergistic result started with the drone and the camera inspection software, which had to actively search for dents, cracks, deformations, corrosion, and alignment issues, and then reconcile the measured results to the design model.
“Our team decided we needed to have both a drone and a robot to inspect on the assembly line,” continued Shuppert. “The drones will fly above and around the craft, while the wheel-mounted robot will work underneath the plane. For the drones, we went with Airgility because they had most of what we were looking for.”
Based in College Park, MD, Airgility, Inc. specializes in integrating AI and autonomy into their unmanned aerial vehicles (UAV). Their drones met the requirements for maneuverability (continuously adjustable tilt motors), control accuracy (ability to hold a flight path to ± 6mm), compliance (NDAA and TAA), and carrying capacity. The guidance and control (G&C) system uses a multi-loop architecture that computes the error between a desired reference position and the current drone position and then synthesizes the desired 3-axis movements of the rotors at an 80 Hz sampling rate. This allows the drone to operate without the benefit of global positioning satellites.
“由于GPS信号无法穿透重型飞机机库，因此无人机必须依靠内部G＆C系统，”气动性首席执行官兼联合创始人Pramod Raheja说。“该系统通过独立的推力矢量系统调节无人机的角度取向，因此它可以根据飞机的物理尺寸遵循3D参考轨迹。”
Raheja解释如何achi态势感知eved by an algorithm that incorporates data from numerous, redundant sensors. This allows the craft to fly in narrow spaces, like over and under a gantry or aircraft wing. Also included in the intelligence is a self-contained on-onboard AI failsafe mechanism, so if the software crashes for any reason, the drone will simply back away, avoiding any obstacles and the land itself.
Collision avoidance is critical since, in addition to the aircraft itself, the Lockheed Martin factory floor presents numerous physical obstacles including scaffolding, pilot ladders, auxiliary power units, tails, canopies, and people.
“Before we let a drone fly next to an $80 million jet, we wanted to test it within a lab environment,” noted Lockheed Martin’s Colaw.
This is where Animesh Chakravarthy, Ph.D., Professor of Mechanical and Aerospace Engineering and Flight Control at the University of Texas at Arlington, was brought in. Chakravarthy’s research in collision avoidance has been recognized by his receiving a prestigious CAREER award from the National Science Foundation (NSF).
“The goal is to have the drone performing multiple precise orbits around the aircraft, at just the right speed, while at the same time ensuring that the scanner is properly oriented towards the aircraft,” explains Chakravarthy. “The trick is not getting so close as to cause a collision, but not so far away as to distort the readings.”
Chakravarthy’s advanced students will develop the mechanical robot that carries the scanner under the plane along the plant floor. It will be equipped with its own G&C system—conceptually like that of the drone — that will autonomously track the reference trajectory while ensuring collision avoidance.
“Sometimes drone technology gets a lot of attention because it is neat and intriguing, but there has to be a business value behind it,” said Colaw. “Using drones and this type of scanning technology really opens the door to better understand our product and to cost-effectively substantiate the quality of our products in a way that we can’t currently do because we are limited by human bandwidth.”
当前的STTR奖包括进一步进步的选项。对于洛克希德·马丁（Lockheed Martin）来说，非常重要的是确定F-35复合皮肤中的任何分层。由于不能总是从表面看到或测量层和空隙的分离，因此需要一种非破坏性测试方法。在这里，高度敏感的IR相机可用于在飞船的复合表面下进行检查，以有效地可视化和识别任何异常。
Teledyne Flir的全球业务发展经理Desmond Lamont说：“ IR闪光灯是通过材料转移热量的激发源。”“由于空隙和间隙不能像固体那样有效地传递热量，因此热量会产生，并且相机可以识别这些热点并指出相邻的断层区域。”
洛克希德·马丁生产设施的机上无人机检查。
While there were several challenges the team encountered, the issue of removing the wiring tether from the scanner was considered one of the most significant. A wireless approach is much safer and more capable of dodging personnel and physical infrastructure on the factory floor. The Airgility team will solve this problem by using AI drones that only transmit crucial data, thus greatly reducing bandwidth.
“Since the intelligent drone knows what anomalies to look for, it only sends that info and ignores the expected results,” points out Raheja. “Therefore, you don’t need to transmit a lot of data continuously.”
这项合作的最终结果是一个可靠的，高度准确的（加或减去0.025毫米）的大型车型的检查平台，可消除制造环境中的人为错误和安全风险。
“有了F-35，肯定有一种用例使用该技术，可以成功进入，”科拉劳总结道。“想法是将其扩展到其他洛克希德·马丁业务部门，然后成为在其他适用行业中使用该技术的主要支持者。
CAD / CAMServices
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