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In the Aerospace Industry there is a focus on Defect Prevention to ensure that quality goals are met. Failure Mode and Effects Analysis (PFMEA) and Control Plan activities are recognized as being one of the most effective, on the journey to Zero Defects. This two-day course is designed to explain the core tools of Design Failure Mode and Effects Analysis (DFMEA), Process Flow Diagrams, Process Failure Mode and Effects Analysis (PFMEA) and Control Plans as described in AS13100 and RM13004. It will show the links to other quality tools such as Characteristics Matrix and Measurement Systems Analysis (MSA).

The international standards D-326A (U.S.) and ED-202A (Europe) titled "Airworthiness Security Process Specification" are the cornerstones of the "DO-326/ED-202 Set" and they are the only Acceptable Means of Compliance (AMC) by FAA & EASA for aviation cyber-security airworthiness certification, as of 2019.

Pass-by noise measurement is mandatory for automotive manufacturers for conformity of production. With evolving of pass-by noise requirements (under 68 dB in 2024), all the stakeholders should be able to comply with this criterion. OEMs, suppliers of passive acoustic treatments, road manufacturers and tire manufacturers are concerned and should deploy efforts to provide solutions for control of exterior noise. In this regard, simulations are preferable over measurement campaigns as they can provide fast feedback on passive exterior treatments for exterior noise control. In the particular case of Lightyear vehicles, the main contributors to pass-by noise are tyres and in-wheel motors. Considering that, a contribution of each of these two sources of noise to pass-by noise will be described. Tyre sources and motor sources will be replaced by simple monopole sources. The results of this simulation will be compared to the pass-by noise measurements.

Electric motors have different sources of noise than traditional powertrains. The motors and power electronics greatly influence the noise generated by the powertrain. The motors have interactions between the magnets and the stator, creating torque ripple. The electronics have solid-state switches, which create noise but also influence the forces experienced by the stator, which can result in noise and vibration. This session will discuss the value of measuring these electrical signals to understand the contributing factors to noise and vibration from the electronics.

This contribution describes a novel method for visualizing leakages in automotive structures using a rotating linear array of a few digital ultrasound microphones in combination with a multi-frequency ultrasound transmitter. The rotating array scans the incident sound field generated by the ultrasound transmitter on a circular area. In a typical measurement setup, the ultrasound transmitter is placed in a cavity (e.g. car interior, trunk or similar) and operates at distinct harmonic frequencies at around 40kHz in an omnidirectional fashion. The rotating linear array is operated on the outside of the cavity and captures the sound field escaping through small leakages. While the reduced hardware complexity allows for the design of a lightweight, handheld sound imaging device, the algorithmic portion of the measurement system requires special attention.

The speakers in acoustic vehicle alerting system (AVAS) play a crucial role for pedestrian safety. Considering their directivity pattern is critical for accurately simulating the exterior sound field of electrical vehicles. This paper proposes a new process to recover the sound directivity pattern of AVAS speaker. The first step of the process is to perform an acoustic testing to measure the sound pressure data radiated from the speaker at a certain number of microphones. Based on the geometry of the speaker, the locations of the microphones and the measured sound pressure data, an inverse method, called pellicular analysis is adopted to recover a set of vibration pattern of the speaker surface. The recovered surface vibration pattern can then be used in the full vehicle model as an excitation for simulating the exterior sound field. The process is validated by both numerical data and real testing data. In all the examples, the microphones are categorized as two groups.

To empirically estimate the radiation of sound sources, a measurement with microphone arrays is required. These are used to solve an inverse problem that provides the radiation characteristics of the source. The resolution of this estimation is a function of the number of microphones used and their position due to spatial aliasing. To improve the radiation resolution for the same number of microphones compared to standard methods (Tikhonov and Lasso), we propose a method based on normalizing flows that uses neural networks to learn empirical priors from the radiation data. The method then uses these learned priors to regularize the inverse source identification problem. We simulate the effects of different microphone arrays on the accuracy of the method and finally evaluate the method using a real measurement.

Excessive in-vehicle road noise can lead to increased driver fatigue and an overall degradation on vehicle interior sound quality. Broadband noise can interfere with the ability of vehicle occupants to carry on a conversation or detract from the vehicle audio system perceived sound quality. Additionally, tonal noise (such as that often induced through excitation of the tire cavity resonant frequencies) can lead to general lack of perceived vehicle quality. Though these issues are not new, they have become much more prevalent with electric vehicles which lack the masking noise traditionally provided by internal combustion engines. The implementation of enablers on a luxury sport utility vehicle is used to illustrate the development process for reduction of road noise. The vehicle in this case study was launched into production with two tuned mass dampers for reduction of low frequency noise road noise content which was amplified by frame modes.

Sound source localization has always been based on the assumption that the line of sight between a source and a sensor is unblocked. This assumption restricts the sound source localization in open space up to an obstacle or a boundary surface. Meanwhile, the root causes of most NVH issues are often housed inside a solid structure, for example, noise emission from an engine, a gearbox, etc. In a recent pioneering research project, however, we have shown that it is possible to use laser beams and photoacoustic sensors to determine the precise locations of a sound source enclosed inside a solid structure. , In this paper, we extend this research work by developing and consolidating the lasers and photoacoustic sensors into a portable system, known as LaSonics so that it will be easy to set up, simple to transport, and fast to obtain results.

There are two key factors driving tyre manufacturers to increase their focus on improving the noise characteristics of the tyre: Strict homologation requirements for tyre and vehicle noise and the increasing prevalence of Electric vehicles. This paper represents the experimental analysis for understanding the effect of various operational parameters such as load, Inflation pressure and speed on overall tyre noise in a semi-anechoic chamber under controlled conditions. To understand the impact of the operational parameters, five tyres with different rim sizes (14" - 18") with the same tread patterns were used. The study includes measuring the overall tyre noise using microphones at three different locations inside the semi-anechoic chamber on a 2m steel drum i.e., leading edge, trailing edge, and centre.

Vehicle Acoustic Prototyping in the mid to high frequency range is challenging with numerical models only. To overcome this challenge, over the past decade, experimental techniques were developed that allow the engineer to incorporate Test-Based models in the simulation as well. Using Virtual Point Technology these Test-Based models serve well to describe, for example, the complex dynamics of the vehicle body / NTF. Here the high modal density and damping characteristics are simply measured on a mule or prototype vehicle and coupled using Dynamic Substructuring to cover the mid and high frequency ranges as well. As such accurate predictions and / or risk assessments can be made much earlier in the vehicle development stage. While test-based models serve well to describe the coupled vehicle dynamics, loads to compute actual vehicle responses are needed as well. Here so-called Equivalent or Blocked Forces are ideal as they are found to be independent of the vehicle dynamics.

In the late 1970’s and early 1980’s, Jing-Yau Chung along with Joseph Pope published several General Motors reports on the then novel measurement of sound intensity using the two-microphone, cross-spectral method. Application of this measurement method was then extended to sound intensity measurement in flow. Through component wind tunnel measurements, it was determined that intensity of noise sources could be accurately measured up to a level of 15 dB below the sound pressure level generated by flow noise on microphones. An initial application of this method was to the identification of noise sources alongside rolling truck tires. It was then extended to the measurement of the aerodynamic noise generated by protrusions added to automotive vehicle designs. These included items such as outside rearview mirrors, windshield wipers, A-pillar offsets, grille whistles, roof racks, underbodies, and fixed-mast radio antennas.

Active Safety, Advanced Driver Assistance Systems (ADAS) are now being introduced to the marketplace as they serve as key enablers for anticipated autonomous driving systems. Automatic Emergency Braking (AEB) is one ADAS application which is either in the marketplace presently or under development as nearly all automakers have pledged to offer this technology by the year 2022. This one-day course is designed to provide an overview of the typical ADAS AEB system from multiple perspectives.

Testing was conducted in daytime and nighttime conditions at four speeds – 35, 50, 55, and 60 miles per hour (mph) – to evaluate the performance of the audible and visual forward collision warning (FCW) system in a WABCO OnGuardACTIVE collision mitigation system (CMS) while approaching a foam stationary vehicle target (SVT). Testing measured the time to collision (TTC) values utilizing a VBOX data acquisition system as well as an “analog” system utilizing synced cameras and a reference line painted on the test track. WABCO Toolbox was utilized to download OnGuard data from the Freightliner after each test; this data was then compared to the data acquired by the VBOX data acquisition system. The results of the testing provide valuable information to collision investigators on the performance of the WABCO OnGuardACTIVE Collision Mitigation System on stationary vehicles.

The Daimler Detroit AssuranceⓇ 4.0 collision mitigation system is able to assist a driver in various aspects of safely operating their vehicle. One capability is the Active Brake Assist (ABA), which uses the Video Radar Decision Unit (VRDU) to communicate with the front bumper-mounted radar to provide information about potential hazards to the driver. The VRDU may warn the driver of potential hazards and apply partial or full braking, depending on the data being gathered and analyzed. The VRDU also records event data when an ABA event occurs. This data may be extracted from the VRDU using Detroit DiagnosticLink software. This paper presents an overview of the VRDU functionality and examines aspects of VRDU data such as the range and resolution of data elements, the synchronicity or timing of the recorded data, and application of the data for use in the analysis of crashes.