Browse Topic: Quality control
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.
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
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
The continuous improvement of validation methodologies for mobility industry components is essential to ensure vehicle quality, safety, and performance. In the context of mechanical suspensions, leaf springs play a crucial role in vehicle dynamics, comfort, and durability. Material validation is based on steel production data, complemented by laboratory analyses such as tensile testing, hardness measurements, metallography, and residual stress analysis, ensuring that mechanical properties meet fatigue resistance requirements and expected durability. For performance evaluation, fatigue tests are conducted under vertical loads, with the possibility of including "windup" simulations when necessary. To enhance correlation accuracy, original suspension components are used during testing, allowing for a more precise validation of the entire system. Additionally, dynamic stiffness measurements provide valuable input for vehicle dynamics and suspension geometry analysis software, aiding in
This specification controls surface condition, manufacturing defects and inspection requirements, and defines methods of measurement for elastomeric toroidal sealing rings (O-rings) for static (including gasket) applications.
This specification covers grease for use on aircraft wheel bearings. It also defines the quality control requirements to assure batch conformance and materials traceability and the procedures to manage and communicate changes in the grease formulation and brand. This specification invokes the Performance Review Institute (PRI) product qualification process. Requests for submittal information may be made to the PRI at the address in 2.2, referencing this specification. Products qualified to this specification are listed on a qualified products list (QPL) managed by the PRI. Additional tests and evaluations may be required by individual equipment builders before a grease is approved for use in their equipment. Approval and/or certification for use of a specific grease in aero and aero-derived marine and industrial applications is the responsibility of the individual equipment builder and/or governmental authorities and is not implied by compliance with or qualification to this
This specification covers a leaded bronze in the form of sand and centrifugal castings (see 8.6).
This specification covers an aluminum bronze alloy in the form of centrifugal and chill castings (see 8.5).
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.
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