Browse Topic: Quality standards
This digital standard is a requirements extract of AS13001A Delegated Product Release Verification Training Requirements. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a requirements extract of AS9145 Requirements for Advanced Product Quality Planning and Production Part Approval Process. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
This digital standard is a digital model of AS9100D Quality Management Systems - Requirements for Aviation, Space, and Defense Organization. This file contains an MBSE model in a mdzip file for use in modeling applications.
In the rapidly evolving aerospace and defense landscape, simply keeping pace with trends isn't enough. Technology is advancing faster than ever, and in mission critical applications, failure is not an option. Systems must endure harsh environments while meeting uncompromising quality standards - an imperative that demands relentless innovation. Enter the Coyotes: WOLF's specialists in next generation rugged embedded systems, small form factor design, and bold, practical ideas. Whether on Earth or in orbit, they expand what high performance embedded computing can do across ground, orbital, lunar and deep space operations. Their work spans R&D, rapid prototyping and new product development for edge computing and artificial intelligence (AI) enabled imaging.
Gasoline direct injection (GDI) engines are the most common technology on American roadways in 2025, and soon, an industrywide gasoline quality standard will better reflect their unique operational needs. Here's why that's important. It's no secret that fuel economy has been one of the greatest driving forces of automotive evolution over the past several decades. As corporate average fuel economy (CAFE) standards have grown increasingly lofty, OEMs eke out new efficiencies from every area of the vehicle. One of those areas, of course, is the engine, and many OEMs have deployed gasoline direct injection (GDI) technology, which is becoming the most common engine technology on American roadways. But while GDI engines proliferate, varying fuel additization throughout North America has not necessarily kept pace with their unique needs and can, in fact, hinder those engines from meeting and sustaining their full fuel economy potential.
Accurate defect quantification is crucial for ensuring the serviceability of aircraft engine parts. Traditional inspection methods, such as profile projectors and replicating compounds, suffer from inconsistencies, operator dependency, and ergonomic challenges. To address these limitations, the 4D InSpec® handheld 3D scanner was introduced as an advanced solution for defect measurement and analysis. This article evaluates the effectiveness of the 4D InSpec scanner through multiple statistical methods, including Gage Repeatability and Reproducibility (Gage R&R), Isoplot®, Youden plots, and Bland–Altman plots. A new concept of Probability of accurate Measurement (PoaM)© was introduced to capture the accuracy of the defect quantification based on their size. The results demonstrate a significant reduction in measurement variability, with Gage R&R improving from 39.9% (profile projector) to 8.5% (3D scanner), thus meeting the AS13100 Aerospace Quality Standard. Additionally, the 4D InSpec scanner improved detection accuracy, provided automated defect quantification, and eliminated the need for time-consuming replication processes. Beyond performance improvements, the adoption of the 4D InSpec scanner led to a 75% reduction in direct labor time, significant cost savings, and the elimination of ergonomic risks and human error associated with traditional inspection methods, and enhanced defect reporting and data collection. The article closes with implementation requirements and areas for future improvement.
The effective reduction of particulate emissions from modern vehicles has shifted the focus toward emissions from tire wear, brake wear, road surface wear, and re-suspended particulate emissions. To meet future EU air quality standards and even stricter WHO targets for PM2.5, a reduction in non-exhaust particulate (NEP) emissions seems to be essential. For this reason, the EURO 7 emissions regulation contains limits for PM and PN emissions from brakes and tire abrasion. Graz University of Technology develops test methods, simulation tools and evaluates technologies for the reduction of brake wear particles and is involved in and leads several international research projects on this topic. The results are applied in emission models such as HBEFA (Handbook on Emission Factors). In this paper, we present our brake emission simulation approach, which calculates the power at the wheels and mechanical brakes, as well as corresponding rotational speeds for vehicles using longitudinal dynamics equations integrated in the simulation tool PHEM (Passenger Car and Heavy duty Emission Model). This simulation model is applicable for both LDV and HDV in the case of NEP. The brake wear emissions, including PM10, PM2.5, PN23, and PN10, are interpolated from characteristic brake emission curves that depend on braking power and vehicle speed. These characteristic curves are generated from a database containing data from literature, partners, and measurement campaigns conducted by our team. The model also considers brake energy recuperation in hybrid and battery electric vehicles, as well as the use of retarders in heavy-duty vehicles The physical approach enables the simulation of all propulsion systems in various driving cycles for all vehicle categories. Furthermore, we will present the projected development of PM and PN emissions for European PC, LDV and HDV traffic through 2050, considering different propulsion technology scenarios including also exhaust particle emissions for comparison.
Repartly, a startup based in Guetersloh, Germany, is using ABB’s collaborative robots to repair and refurbish electronic circuit boards in household appliances. Three GoFa cobots handle the sorting, visual inspection and precise soldering tasks enabling the company to enhance efficiency and maintain high quality standards.
Low-level flight, defined by high-speed operations near terrain, represents a significant challenge in military rotorcraft missions while providing strategic advantages, such as radar evasion and heightened surprise. Recent conflicts highlight the urgent need for advanced low-level flight capabilities in the design of new rotorcraft. The close proximity to ground obstacles, combined with the complexities of piloting, necessitates precise control and robust handling qualities to prevent accidents. However, existing handling quality standards, such as MIL-DTL-32742, reveal limitations in assessing low-level maneuvers. Given the diverse array of new rotorcraft designs, driven by initiatives like the U.S. Army's Future Vertical Lift and NATO's Next Generation Rotorcraft Capabilities, a customized handling qualities evaluation for each design is impractical. In response, a performance-driven strategy has been implemented, scaling Mission Task Elements to align with aircraft performance capabilities. This approach identifies handling quality gaps across the Operational Flight Envelope, concentrating on the aircraft’s effectiveness in achieving task success under varied conditions. Prior simulator studies validate the effectiveness of this method for assessing different configurations. This paper presents flight test results using DLR's ACT/FHS research helicopter, confirming a set of scalable Mission Task Elements developed at DLR's AVES and NASA's VMS simulators. Pilots utilized a Head-Mounted Display for task cueing, eliminating the need for physical infrastructure. The Mission Task Elements proved suitable for evaluating the low-level handling qualities of the ACT/FHS. Although the provided Head-Mounted Display facilitated Handling Qualities evaluations, it encountered some hardware limitations. The scaling for different airspeeds met pilot expectations, and wind compensation functioned as anticipated, enhancing the independence of flight tests from environmental conditions. These findings lead to recommended updates for task descriptions and course cueing requirements, confirming desired performance tolerances.
In recent years, battery electric vehicles (BEVs) have experienced significant sales growth, marked by advancements in features and market delivery. This evolution intersects with innovative software-defined vehicles, which have transformed automotive supply chains, introducing new BEV brands from both emerging and mature markets. The critical role of software in software-defined battery electric vehicles (SD-BEVs) is pivotal for enhancing user experience and ensuring adherence to rigorous safety, performance, and quality standards. Effective governance and management are crucial, as failures can mar corporate reputations and jeopardize safety-critical systems like advanced driver assistance systems. Product Governance and Management for Software-defined Battery Electric Vehicles addresses the complexities of SD-BEV product governance and management to facilitate safer vehicle deployments. By exploring these challenges, it aims to enhance internal processes and foster cross-geographical collaborations, assisting automotive product managers in integrating comprehensive considerations into product strategies and requirements. Click here to access the full SAE EDGETM Research Report portfolio.
A sensing technology that can assess the quality of components in fields such as aerospace could transform UK industry. University of Bristol, Bristol, UK In a study, published in the Journal Waves in Random and Complex Media, researchers from the University of Bristol have derived a formula that can inform the design boundaries for a given component's geometry and material microstructure. A commercially viable sensing technology and associated imaging algorithm to assess the quality of such components currently does not exist. If the additive manufacturing (3D printing) of metallic components could satisfy the safety and quality standards in industries there could be significant commercial advantages in the manufacturing sector.
The Auto industry has relied upon traditional testing methodologies for product development and Quality testing since its inception. As technology changed, it brought a shift in customer demand for better vehicles with the highest quality standards. With the advent of EVs, OEMs are looking to reduce the going-to-market time for their products to win the EV race. Traditional testing methodologies have relied upon data received from various stakeholders and based on the same tests are planned. The data used is highly subjective and lacks variety. OEMs across the world are betting big on telematics solutions by pushing more and more vehicles with telematics devices as standard fitment. The data from such vehicles which gets generated in high levels of volume, variety and velocity can aid in the new age of vehicle testing. This live data cannot be simply simulated in test environments. The device generates hundreds of signals, frequently in a fraction of seconds. Multiple such signals can be combined to create KPIs that correspond to specific traits of vehicle health. Specific telematics KPIs can be used as inputs in test benches. With this data, the vehicle gets tested against real-world conditions. Another important aspect of vehicle testing is road conditions. OEMs can only do so much in testing the vehicles in various different road conditions. However, with telematics devices road condition data can be directly generated corresponding to the international roughness index. This data along with KPIs will bring in a new perspective of vehicle development testing and enable the manufacturer to better understand market problems and to take effective countermeasures. With these KPIs, Possibilities are limitless, and these data can be used to create a digital twin of the vehicle, enabling the OEM to assess the vehicle without even physically attending.
The automotive industry is going through one of its greatest restructuring, the migration from internal combustion engines to electric powered / internet connected vehicles. Adapting to a new consumer who is increasingly demanding and selective may be one of the greatest challenges of this generation, Original Equipment Manufacturers (OEM) have been struggling to keep offering a diversified variety of features to their customers while also maintaining its quality standards. The vehicles leave the factory with an embedded SIM Card and a telematics module, which is an electronic unit to enable communication between the car, data center. Connected vehicles generate tens of gigabytes of data per hour that have the potential to be transformed into valuable information for companies, especially regarding the behavior and desires of drivers. One of the techniques used to gather quality feedback from the customers is the NPS it consists of open questions focused on top-of-mind feedback. Here is where AI and ML comes into play, using NLP and several other computational techniques to download, extract, structure, read, process, understand and categorize all this data into specific predetermined categories, allowing engineers to accelerate fixing quality issues and improving user experience. The ML model developed in this article identify costumer complains in an enormous data lake and groups them into categories. After a significative amount of data is collected and grouped into it enables the algorithm to predict future trends and together with real time connected vehicle data the model can alert the responsible engineers to develop an action to solve the problem without more customers even actually experience the failure. The ML algorithm is still on its development phase, but the initial results are promising, we have successfully processed more them 6 millioncustomers feedback finding problems with precision and accuracy close to 90%.
This SAE Information Report is intended to be used for routine (or periodic) monitoring of filling station performance. It is not intended to provide process quality control requirements for any portion of the product delivery cycle.
This specification establishes acceptance criteria for discontinuities revealed by magnetic particle inspection of parts made from wrought, ferromagnetic materials.
Heavy-duty truck vehicles are generally equipped with leaf spring suspensions. Conventionally, beam elements are used in multibody software to build the leaf spring model to calculate virtual loads. Beam elements require a high computation time due to their numerous degrees of freedoms and force components introduced by beam connections, interleaf contacts, friction, etc. Again, in these simulations, solvers frequently fail in durability loads analysis due to sudden spike in accelerations and high suspension articulation coming from severe road profiles. These drawbacks lead to the use of simplified three-link mechanism models to simulate the leaf spring’s behavior, which is computationally faster. However, the current approach is less accurate as compared to the beam element model because this model has only a torsional spring which accounts for vehicle bounce condition. In reality, the leaf spring suspension system is also subjected to cornering and braking while running on road profiles like pave, potholes, bumps etc. This paper presents a novel simplified leaf spring model based on a three-link mechanism that captures leaf spring suspension’s bounce, braking, and cornering behaviors. Leaf spring parameters are identified from the beam element model using virtual Kinematics & Compliances tests; using a detailed FEA model or physical Kinematics & Compliances test data is also viable. Proposed simplified leaf spring suspension behavior is further validated through load cases such as vertical push, axle windup, cornering, suspension roll, etc. The suspension analysis using spindle loads from different road profiles are carried out and results validated, by comparing with the beam element model for results like Pseudo damage, max-min at different locations. The results of the simplified leaf spring model were closely matched with the beam model. With this simplified leaf spring model, loads can be quickly generated maintaining desired quality standards.
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