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Internal audits are a requirement of the AS9100, AS 13100 and RM 13005 and are intended to verify the compliance and effectiveness of an organization's quality management system. The methods and techniques for performing internal audits have significantly changed in the aviation, space and defense industries, and internal auditors must be knowledgeable of these requirements and the expectations as identified in the standard.

This specification covers a corrosion- and heat-resistant steel in the form of bars, wire, forgings, mechanical tubing up to 5.00 inches (127 mm), inclusive, in nominal diameter or least distance between parallel sides (thickness), and stock for forging or heading.

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety,optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle type recognition algorithms, this paper proposes an improved vehicle type recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.

At the dawn of battery electric vehicles (BEVs), protection of automotive battery systems as well as passengers, especially from severe side impact, has become one of the latest and most challenging topics in the BEV crashworthiness designs. Accordingly, two material-selection concepts are being justified by the automotive industry: either heavy-gauge extruded aluminum alloys or light-gauge advanced high-strength steels (AHSSs) shall be the optimal materials to fabricate the reinforcement structures to satisfy both the safety and lightweight requirements. In the meantime, such a justification also motivated an ongoing C-STARTM (Cliffs Steel Tube as Reinforcement) Protection project, in which a series of modularized steel tube assemblies, were demonstrated to be more cost-efficient, sustainable, design-flexible, and manufacturable than the equivalent extruded aluminum alloy beams as BEV reinforcement structures.

The electrification of vehicles marks the introduction of new products to the automotive market and a continued effort to optimize their performance. The electric motor is an important component with which a further optimization of efficiency, power density and cost can be achieved. Additional benefits can be realized in the laminated core. This paper presents an innovative method to produce laminated stacks by a chain of processes different from conventional ways. The process chain presents a sequence of precision blanking, buffering, heat treatment and gluing. The effect of these processes is compared with existing solutions that typically contain some individual features but usually not the combination that enhances the overall effect. The heat treatment decreases residual stresses from previous process steps and reduces power losses in the laminated core. Depending on the design, benefits around 20% are found.

General Motors (GM) is working towards a future world of zero crashes, zero emissions and zero congestion. It’s “Ultium” platform has revolutionized electric vehicle drive units to provide versatile yet thrilling driving experience to the customers. Three variants of traction power inverter modules (TPIMs) including a dual channel inverter configuration are designed in collaboration with LG Magna e-Powertrain (LGM). These TPIMs are integrated with other power electronics components inside Integrated power electronics (IPE) to eliminate redundant high voltage connections and increase power density. The developed power module from LGM has used state-of-the art sintering technology and double-sided cooled structure to achieve industry leading performance and reliability. All the components are engineered with high level of integration skills to utilize across TPIM variants.

As a key tool to maintain urban cleanliness and improve the road environment, road cleaning vehicles play an important role in improving the quality of life of residents. However, the traditional road cleaning vehicle requires the driver to monitor the situation of road garbage at all times and manually operate the cleaning process, resulting in an increase in the driver 's work intensity. To solve this problem, this paper proposes a road garbage recognition algorithm based on improved YOLOv5, which aims to reduce labor consumption and improve the efficiency of road cleaning. Firstly, the lightweight network MobileNet-V3 is used to replace the backbone feature extraction network of the YOLOv5 model. The number of parameters and computational complexity of the model are greatly reduced by replacing the standard convolution with the deep separable convolution, which enabled the model to have faster reasoning speed while maintaining higher accuracy.

The IncQuery AUTOSAR-UML Bridge is an innovative solution for Assisted Documentation Creation and Automated Handover, aiming at driving a paradigm shift in integrated digital engineering in the automotive domain. The AUTOSAR-UML Bridge is addressing a well-known gap in the engineering ecosystem of automotive design, where the co-design of AUTOSAR models and other model-based artifacts is often hampered by tedious workflows involving manual syncing of model contents between AUTOSAR and UML/SysML tools. The Bridge is aiming at streamlining the workflow by generating high-quality UML models from AUTOSAR projects, with built-in ISO26262 and ASPICE compliance. Automotive software architects and systems engineers spend a lot of time with creating ISO26262-compliant documentation, by creating UML models from AUTOSAR architecture designs, or establishing traceability between requirements captured in SysML and design artefacts that exist in both modeling languages.

To satisfy the stringent regulations for exhaust gas emissions from gasoline-powered vehicles, large amounts of Rh and Pd have often been employed in three-way catalysts (TWCs) as the main active components. On the other hand, Pt-based TWCs are not often used in gasoline vehicles because Pt is readily sintered by its exhaust gases at approximately 1000 °C [1, 2]. In general, Pt-based TWCs must be located away from large thermal loads to maintain the active sites for gas purification. Based on this background, we previously reported that employing a small amount of CeO2 calcined at 1000 °C (cal-CeO2) in Pt-based TWCs was one of the most effective approaches for improving the catalytic activity without increasing the amount of Rh and Pd [3]. The effect of cal-CeO2 was attributed to the higher redox performance and Pt dispersion derived from the strong interactions between Ce and Pt.

This study delves into the microstructural and mechanical characteristics of AlSi10Mg alloy produced through the Laser Powder Bed Fusion (L-PBF) method. The investigation identified optimal process parameters for AlSi10Mg alloy based on Volume Energy Density (VED). Manufacturing conditions in the L-PBF process involve factors like laser power, scan speed, hatching distance, and layer thickness. Generally, high laser power may lead to spattering, while low laser power can result in lack-of-fusion areas. Similarly, high scan speeds may cause lack-of-fusion, and low scan speeds can induce spattering. Ensuring the quality of specimens and parts necessitates optimizing these process parameters. To address the low elongation properties in the as-built condition, heat treatment was employed. The initial microstructure of AlSi10Mg alloy in its as-built state comprises a cell structure with α-Al cell walls and eutectic Si.

Bicycle computers record and store global position data that can be useful for forensic investigations. The goal of this study was to estimate the absolute error of the latitude and longitude positions recorded by a common bicycle computer over a wide range of riding conditions. We installed three Garmin Edge 530 computers on the handlebars of a bicycle and acquired 9 hours of static data and 96 hours (2214 km) of dynamic data using three different navigation modes (GPS, GPS+GLONASS, and GPS+Galileo satellite systems) and two geographic locations (Vancouver, BC, Canada and Orange County, CA, USA). We used the principle of error propagation to calculate the absolute error of this device from the relative errors between the three pairs of computers. During the static tests, we found 16 m to 108 m of drift during the first 4 min and 1.4 m to 5.0 m of drift during a subsequent 8 min period. During the dynamic tests, we found a 95th percentile absolute error for this device of ±8.04 m.

Aluminum alloy has become an indispensable part of the automotive industry because of its excellent mechanical properties such as lightweight, high strength, high reliability, maintainability, and low cost. Aluminum alloy is used in automobiles, such as engine blocks, cylinder heads, intake manifolds, brake components, and fuel tanks. Fatigue and fracture are the main reasons for its engineering failure. Surface strengthening techniques, such as ultrasonic shot peening (USP), are often used to improve the fatigue resistance of aluminum alloys. This article expounds on the working principle of USP and elucidates the influence of USP process parameters on the surface characteristics of aluminum alloy. Experimental results observed the effects of USP parameters on surface properties such as surface roughness, microhardness, and surface morphology.

Cast austenitic stainless steels, such as 1.4837Nb, are widely used for turbo housing and exhaust manifolds which are subjected to elevated temperatures. Due to assembly constraints, geometry limitation, and particularly high temperatures, thermomechanical fatigue (TMF) issue is commonly seen in the service of those components. Therefore, it is critical to understand the TMF behavior of the cast steels. In the present study, a series of fatigue tests including isothermal low cycle fatigue tests at elevated temperatures up to 1100°C, in-phase and out-of-phase TMF tests in the temperature ranges 100-800°C and 100-1000°C have been conducted. Both creep and oxidation are active in these conditions, and their contributions to the damage of the steel are discussed.