Browse Topic: Quality control
In 1994, the SAE G-11 Reliability, Maintainability, Supportability and Logistics (RMSL) Division chartered a software committee, G-11SW, to create several software standards and guidance documents across the RMSL spectrum, including a software reliability program standard and implementation guidelines. The committee was formed as a cross section of international representatives from commercial industries and governments. The G-11SW committee has developed a standard (JA1002) and these implementation guidelines (JA1003) that are consistent with a SAE G-11 system level reliability program standard (JA1000) and guidelines (JA1000-1), augmented by necessary software-specific information. The G-11SW committee believes these documents reflect the best current commercial practices, and meet the objectives of the United States Department of Defense Acquisition Reform initiative and the North Atlantic Treaty Organization (NATO) Reliability Program. The JA1002 program standard is intended to be
The importance of reliability in design engineering has significantly grown since the early Sixties. Competition has been a primary driver in this growth. The three realities of competition today are: world class quality and reliability, cost-effectiveness, and fast time-to-market. Formerly, companies could effectively compete if they could achieve at least two of these features in their products and product development processes, often at the expense of the third. However, customers today, whether military, aerospace, or commercial, have been sensitized to a higher level of expectation and demand products that are highly reliable, yet affordable. Product development practices are shifting in response to this higher level of expectation. Today, there is seldom time, or necessary resources to extensively test, analyze, and fix to achieve high quality and reliability. It is also true that the rapid growth in technology prevents the accumulation of historical data on the field performance
This standard establishes the common requirements for training of DPRV personnel for use at all levels of the aerospace engine supply chain. This standard shall apply when an organization elects to delegate product release verification by contractual flow down to its suppliers (reference 9100 and 9110 standards) and to perform product acceptance on its behalf. It is intended that organizations specify their DPRV requirements through the application of AS9117. While the delegating organization will use the AS13001 standard as the baseline for establishing DPRV process and product training, it may include additional contractual training requirements to meet its specific needs. The DPRV training material was primarily developed for aerospace engine supply chain requirements. However, this standard may also be used in other aerospace industry sectors where a DPRV process requiring specific training can be of benefit.
This FMEA standard describes potential failure mode and effects analysis in design (DFMEA), supplemental FMEA-MSR, and potential failure mode and effects analysis in manufacturing and assembly processes (PFMEA). It assists users in the identification and mitigation of risk by providing appropriate terms, requirements, rating charts, and worksheets. As a standard, this document contains requirements—”must”—and recommendations—”should”—to guide the user through the FMEA process. The FMEA process and documentation must comply with this standard as well as any corporate policy concerning this standard. Documented rationale and agreement with the customer are necessary for deviations in order to justify new work or changed methods during customer or third-party audit reviews.
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.
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.
This SAE Standard provides requirements and guidance to: Develop a Materiel authenticity plan. Procure Materiel from reliable sources. Assure authenticity and conformance of procured Materiel, including methods such as certification, traceability, testing, and inspection appropriate to the Commodity/item in question. Control Materiel identified as counterfeit. Report Suspect or Counterfeit Materiel to other potential users and Authorities Having Jurisdiction.
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
With the continuous improvement of information technology in aerospace manufacturing enterprises, the need for the integration and connection of various links in the product development process is becoming increasingly urgent. This article mainly introduces the research on BOM product structure, BOM effectiveness management, and product dataset management solutions for electromechanical products, and elaborates on the key technical content involved in detail, providing a basic capability framework for the comprehensive implementation of XBOM construction in the future.
Automating harvesters started out as a necessary solution to a severe labor shortage in 1990, Trebro Manufacturing states on its website. The Billings, Montana-based manufacturer has been producing turf harvesting machines since 1999, and its automated sod harvesters and entire harvesting process feature self-driving, automated-control functions. The company's tag line, “The Future of Turf Harvesting,” refers to its position of being the first in the industry to offer automated turf harvesting products. Trebro's AutoStack 3 harvester is an automated combine for turf that steers itself while an operator monitors and performs quality control actions when needed. The harvesting process combines several automated control processes.
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
This standard is for use by organizations that procure and integrate EEE Parts. These organizations may provide EEE Parts that are not integrated into assemblies (e.g., spares and/or repair EEE Parts). Examples of such organizations include, but are not limited to, the following: Original Equipment Manufacturers; contract assembly manufacturers; maintenance, repair, and overhaul (MRO) organizations; and suppliers that provide EEE Parts or assemblies as part of a service. These requirements are intended to be applied (or flowed down as applicable) through the supply chain to all organizations that procure and integrate EEE Parts and/or systems, subsystems, or assemblies. The mitigation of Counterfeit EEE Parts in this standard is risk based. These mitigation steps will vary depending on the criticality of the application and desired performance and reliability of the equipment/hardware. The requirements of this document are used in conjunction with the organization’s higher-level
In today’s competitive landscape, industries are relying heavily on the use of warranty data analytics techniques to manage and improve warranty performance. Warranty analytics is important since it provides valuable insights into product quality and reliability. It must be noted here that by systematically looking into warranty claims and related information, industries can identify patterns and trends that indicate potential issues with the products. This analysis helps in early detection of defects, enabling timely corrective actions that improve product performance and customer satisfaction. This paper introduces a comprehensive framework that combines conventional methods with advanced machine learning techniques to provide a multifaceted perspective on warranty data. The methodology leverages historical warranty claims and product usage data to predict failure patterns & identify root causes. By integrating these diverse methods, the framework offers a more accurate and holistic
The United States Marine Corps enlists the JCB 4CX backhoe loader as its latest recruit. JCB recently announced that it has secured a contract to provide 4CX backhoe loaders to the United States Marine Corps. According to JCB, the agreement includes not only machines but also attachments testing and hands-on operator training. “The 4CX is the direct result of more than 70 years of continuous improvement,” said Chris Giorgianni, vice president of government and defense for JCB North America. “It's built to perform in the most demanding environments, whether that's military engineering missions or high-pressure construction jobsites.”
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
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