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This specification covers an aluminum alloy in the form of extruded bars, rods, wire, profiles, and tubing up to 5.000 inches (127.00 mm), inclusive, in nominal diameter or least thickness (see 8.5).

This specification covers a magnesium alloy in the form of welding wire (see 8.5).

This specification covers an aluminum alloy in the form of plate 4.000 to 10.000 inches (101.6 to 254.0 mm), inclusive, in nominal thickness (see 8.5).

This specification covers a precipitation hardenable corrosion- and heat-resistant nickel alloy in the form of seamless tubing.

This specification covers a corrosion- and heat-resistant nickel alloy in the form of welding wire.

The aviation, space, and defense industries rely on the development and manufacture of complex products comprised of multiple systems, subsystems, and components each designed by individual designers (design activities) at various levels within the supply chain. Each design or manufacturing activity controls various aspects of the configuration and specifications related to the product. When a change to design or process is requested or required, the change is typically required to be evaluated against the impacts to the entire system. Proposed changes to design data/information that the design activity identifies to be minor and have no effect on the product requirements or specifications, have the potential to be implemented and approved, where authorized to do so, but requires notification. Changes that affect customer mandated requirements or specifications shall be approved prior to implementation.

This recommended practice is derived from common test sequences used within the industry. This procedure applies to all on-road passenger cars and light trucks up to 4 540 kg of GVWR. This recommended practice does not address other aspects such as performance, NVH, and durability. Test results from this recommended practice should be combined with other measurements and dynamometer tests (or vehicle-level tests), and acceptance criteria to validate a given design or configuration.

This specification covers a corrosion-resistant steel in the form of sheet, strip, and plate 0.005 to 1.000 inches (0.13 to 25.40 mm) in nominal thickness in the solution heat-treated condition.

This SAE Aerospace Standard (AS) defines the nomenclature for surface finishes commonly used for sheet and strip in aerospace material specifications. It is applicable to steel and to iron, nickel, cobalt, and titanium base alloys.

The paramount importance of titanium alloy in implant materials stems from its exceptional qualities, yet the optimization of bone integration and mitigation of wear and corrosion necessitate advanced technologies. Consequently, there has been a surge in research efforts focusing on surface modification of biomaterials to meet these challenges. This project is dedicated to enhancing the surface of titanium alloys by employing shot peening and powder coatings of titanium oxide and zinc oxide. Comparative analyses were meticulously conducted on the mechanical and wear properties of both treated and untreated specimens, ensuring uniformity in pressure, distance, and time parameters across all experiments. The outcomes underscore the efficacy of both methods in modifying the surface of the titanium alloy, leading to substantial alterations in surface properties.

This training class provides a comprehensive overview of the principles, processes, and objectives of model testing – from requirements to model tests. We offer step-by-step guidance from creating requirements-based test specifications, through testing TargetLink and/or Embedded Coder models, to automated test evaluation based on test assessments and back-to-back/regression tests. In particular, we will emphasize ISO 26262-compliant test management and explain the test process for MiL and SiL, as well as tracing requirements to test specifications and test assessments.

This training class will introduce you to fundamental aspects of working with modeling guidelines and to the static model analysis of MATLAB Simulink/Stateflow, TargetLink, and Embedded Coder models. Furthermore, you will learn how to create MISRA- and ISO 26262-compliant models using proven modeling standards and best practices. The spotlight will be placed on how you can best integrate the MES Model Examiner (MXAM) into your process. Via several hands-on sessions, you will have the chance to practice reliably deploying guidelines with MXAM and ensure guideline compliance.

This training class provides a practical overview of developing and safeguarding embedded software on the basis of Simulink and code generators like Embedded Coder and TargetLink within the framework of serial projects. The training class takes participants through all process steps from designing and creating the simulation model in Simulink and Stateflow to generating production code. Model quality assurance consists of verifying the model and software architecture, safeguarding the modeling guidelines, as well as checking for functional compliance with requirements in the model test.

Model-based software development has become state of the art for automotive embedded applications. Toolchains have been established, and methods and procedures have been defined to address the strong requirements of functional safety standards. Best practices within general software development, however, propose to overcome strict waterfall process models and promote agile methods in order to address real-world challenges, such as late changes or vague requirements. These real-world scenarios exist in automotive software development, and agile methods will also be beneficial here.

This specification covers aircraft-quality, low-alloy steel in the form of round, seamless tubing.

This document covers all metal, self-locking wrenching nuts, plate nuts, shank nuts, and gang channel nuts made from a corrosion and heat resistant steel of the type identified under the Unified Numbering System as UNS S66286 and of 160 ksi tensile strength at room temperature, with maximum test temperature of parts at 1200 °F.

Explaining MOSA from the Team that Led the Army Aviation Mission Computing Environment Task Order What's the Best DC Motor for Your Commercial Aerospace Application? Aerospace Production: Overcoming Challenges in Composite Machining Understanding the Limits of Artificial Intelligence for Predictive Maintenance Pushing the Limits: Engineering Advanced RF Interconnects to Meet the Challenges of Hypersonic Missile Development Expanding Possibilities for Superconducting Qubits With Niobium Researchers Help Robots Navigate Efficiently in Uncertain Environments A new algorithm reduces travel time by identifying shortcuts a robot could take on the way to its destination.

Abstract Transient temperature analysis is involved in the thermal simulation of the heat treatment process, in which the hot metal temperature changes with respect to time from an initial state to the final state. The critical part of the simulation is to determine the heat transfer coefficient (HTC) between the hot part and the quenching medium or quenchant. In liquid quenching, the heat transfer between the hot metal part and water becomes complicated and it is difficult to determine HTC. In the current experimentation a medium carbon steel EN 9 rod with a diameter of 50 mm and length 100 mm was quenched in water and ethylene glycol mixture with different concentrations. A part model was created; meshed and actual boundary conditions were applied to conduct computational fluid dynamics (CFD) analysis. In order to validate CFD analysis the experimental trials were conducted.

Abstract In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite.