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The limitations of commonly used materials such as steel in withstanding high temperatures led to exploring alternative alloys. For instance, Inconel 825 is a nickel-based alloy known for its exceptional corrosion resistance. Thus, the Inconel 825 is used in various applications, including aerospace, marine propulsion, and missiles. Though it has many advantages, machining this alloy at high temperatures could be challenging due to its inadequate heat conductivity, increased strain hardening propensity, and extreme dynamic shear strength. The resultant hardened chips generated during high-speed machining exhibit elevated temperatures, leading to tool wear and surface damage, extending into the subsurface. This work investigated the influence of varying process settings on the machinability of Inconel 825 metal, using both uncoated and coated tools.

Created to elevate expertise in testing, verification, and validation with industry-specific terminology, readers are empowered to navigate the complex world of quality assurance. From foundational concepts to advanced principles, each entry provides clarity and depth, ensuring the reader becomes well-versed in the language of precision. This dictionary is an indispensable companion for both professionals and students seeking to unravel the nuances of testing methodologies, verification techniques, and validation processes. Readers will be equipped with the tools to communicate effectively, make informed decisions, and excel in projects. In addition, references to SAE Standards are included to direct the reader to additional information beyond a practical definition.

To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions.

AIR5317 establishes the foundation for developing a successful APU health management capability for any commercial or military operator, flying fixed wing aircraft or rotorcraft. This AIR provides guidance for demonstrating business value through improved dispatch reliability, fewer service interruptions, and lower maintenance costs and for satisfying Extended Operations (ETOPS) availability and compliance requirements.

Abstract An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly.

This standard defines requirements for the preparation and execution of the audit process. In addition, it defines the content and composition for the audit reporting of conformity and process effectiveness to the 9100-series standards, the organization's QMS documentation, and customer and statutory/regulatory requirements. The requirements in this standard are additions or represent changes to the requirements and guidelines in the standards for conformity assessment, auditing, and certification as published by ISO/IEC (i.e., ISO/IEC 17000, ISO/IEC 17021-1). When there is conflict with these standards, the requirements of the 9101 standard shall take precedence.

Abstract In this article, a formation flying technique designed for a multiple unmanned aerial vehicles (multi-UAV) system to provide low-cost and efficient solution for civilian and military applications is presented. First, a modular leader-follower formation algorithm was developed to accomplish the formation flying with off-the-shelf low-cost components and sensors. Second, a proportional-integral-derivative (PID) controller was utilized for velocity control of the UAVs to maintain the tight formation. Third, a particle swarm optimization-optimized reciprocal velocity obstacles (PSO-RVO) algorithm was utilized for obstacles avoidance and collision avoidance between the UAVs while navigating, with the aid of sonar ranging sensors onboard. The formation flying algorithm developed was tested through both simulation and experiment using two quadcopters with global positioning system (GPS) signals.

Tradespace analysis is used to define the characteristics of the solution space for a vehicle design problem enabling decision-makers (DMs) to evaluate the risk-benefit posture of a vehicle design program. The tradespace itself is defined by a set of functional objectives defined by vehicle simulations and evaluating the performance of individual design solutions that are modeled by a set of input variables. Of special interest are efficient design solutions because their perfomance is Pareto meaning that none of their functional objective values can be improved without decaying the value of another objective. The functional objectives are derived from a combination of simulations to determine vehicle performance metrics and direct calculations using vehicle characteristics. The vehicle characteristics represent vendor specifications of vehicle subsystems representing various technologies.

Quality management professionals across the global aerospace and defense community are convening for one hour – Wednesday, October 27th, starting at 10 am Pacific Daylight Time (PDT) – to discuss the AS9100 international standard. Register to take part in the free AeroTech webinar, hosted by SAE International and Tektronix, designed to help manufacturers, contractors, and subcontractors throughout the global aviation, space, and defense supply chain keep pace with and meet the requirements of AS9100 international quality management system standard.

While the pandemic continues, aerospace companies are rising to embrace new and emerging challenges at a time when there’s so much innovation. This innovation can be seen in the emergence of urban air mobility (UAM), the rebirth of supersonic flight, the drive towards a “zero emission” aircraft, and the continued use of autonomous drones for delivery, freight, search & rescue, and defense. There are exciting new developments in space as companies are developing products for commercial exploration and space tourism, and new ways to launch satellites. A new generation of engineering is also emerging in the defense sector and its development of not only aircraft, but also ships, tankers, and even flight trainers.

This document defines the minimum requirements for auditors, CBs, Auditor Authentication Bodies (AABs), Training Provider Approval Bodies (TPABs), and Training Providers (TPs) who participate in the IAQG Industry Controlled Other Party (ICOP) scheme. The requirements in this standard supplement those defined within the 9104/1, 9104/2, ISO/IEC 17021-1, and ISO/IEC 17021-3 standards. Data protection for the parties subject to this document and other relevant requirements of the ICOP scheme are managed via bi-lateral contracts between the joint controllers of the data.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.

The “holy grail” for prognostics and health management (PHM) professionals in the aviation sector is to have integrated vehicle health management (IVHM) systems incorporated into standard aircraft maintenance policies. Such a change from current aerospace industry practices would lend credibility to this field by validating its claims of reducing repair and maintenance costs and, hence, the overall cost of ownership of the asset. Ultimately, more widespread use of advanced PHM techniques will have a positive impact on safety and, for some cases, might even allow aircraft designers to reduce the weight of components because the uncertainty associated with estimating their predicted useful life can be reduced. We will discuss how standard maintenance procedures are developed, who the various stakeholders are, and – based on this understanding - outline how new PHM systems can gain the required approval to be included in these standard practices.

Game-changing opportunities abound for the application of vehicle health management (VHM) across multiple transportation-related sectors, but key unresolved issues continue to impede progress. VHM technology is based upon the broader field of advanced analytics. Much of traditional analytics efforts to date have been largely descriptive in nature and offer somewhat limited value for large-scale enterprises. Analytics technology becomes increasingly valuable when it offers predictive results or, even better, prescriptive results, which can be used to identify specific courses of action. It is this focus on action which takes analytics to a higher level of impact, and which imbues it with the potential to materially impact the success of the enterprise. Artificial intelligence (AI), specifically machine learning technology, shows future promise in the VHM space, but it is not currently adequate by itself for high-accuracy analytics.

This SAE Aerospace Information Report (AIR) is prepared for stakeholders seeking information about the evolution, integration, and approval of SHM technologies for military aircraft systems. The report provides this information in the form of (a) two military organizations’ perspectives on requirements, and (b) general SHM challenges and industry perspectives. The report only provides information to generate awarness of prespectives for military aircraft and, hence, assists those who are involved in developing SHM systems understanding the broad range of regulations, requirements, and standards published by military organizations that are available in the public domain from the military organizations.

This interface standard applies to fuzes used in airborne weapons that use a 3-in fuze well. It defines: Physical envelope of the fuze well at the interface with the fuze. Load bearing surfaces of the fuze well. Physical envelope of the fuze and its connector. Mechanical features (e.g., clocking feature). Connector type, size, location and orientation. Retaining ring and its mechanical features (e.g., thread, tool interface). Physical envelope of the retaining ring at the interface with the fuze. Physical space available for installation tools. Torque that the installation tool shall be capable of providing. This standard does not address: Materials used or their properties. Protective finish. Physical environment of the weapon. Explosive interface or features (e.g., insensitive munitions (IM) mitigation). Charging tube. Torque on the retaining ring or loads on the load bearing surfaces.