Browse Topic: Quality management systems
This specification covers quality assurance sampling and testing procedures used to determine conformance to applicable specification requirements of carbon and low-alloy steel forgings.
Gear noise is a common challenge that all gear manufacturers must contend with. In tractors, while it is often sufficiently low in intensity to not pose a significant issue, there are instances where gear whine may occur which is noticeable. In such cases, identifying the source and effectively addressing the problem can prove to be particularly difficult. This paper addresses the root cause analysis carried out for the evaluation of factors influencing whine noise behavior of Spiral bevel gear pair (SO2) in a tractor transmission system. Numerous publications have been published on gear noise of spiral bevel gear pair, too many to list here. However, once the gearbox assembled into the transmission, such models are of limited practical value. The work explained in this paper is a typical example offers avenues in correcting the issue using more limited means.
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 study investigates the phenomenon of receptacle icing during Compressed Natural Gas (CNG) refueling at filling stations, attributing the issue to excessive moisture content in the gas. The research examines the underlying causes, including the Joule-Thomson effect, filter geometries, and their collective impact on flow interruptions. A comprehensive test methodology is proposed to simulate real-world conditions, evaluating various filter types, seal materials and moisture levels to understand their influence on icing and flow cessation. The findings aim to offer ideas for reducing icing problems. This will improve the reliability and safety of CNG refueling systems.
Oil pressure, the most fundamental to engine's performance and longevity, is not only critical to ensure that the engine components are properly lubricated, cooled, and protected against wear and contamination, but also ultimately contributing to reliable engine performance. Due to several factors of engine such as, rotational fluctuation, aeration, functioning of hydraulic components there are fluctuations in oil pressure. In engines, with a crank-mounted fixed displacement oil pump (FDOP), these inherited pressure fluctuations cannot be eliminated completely. However, it is very necessary to control the abnormal oil pressure fluctuation because abnormal pressure fluctuation may lead to malfunction of hydraulic component functioning like variable valve timing (VVT), hydraulic lash adjuster (HLA) and dynamic chain tensioner which can further cause serious issues like excessive or sudden load drops, unstable engine performance, valve train noise, improper valve lift operation etc. In
This study introduces a novel Large Language Model (LLM)-driven approach for comprehensive diagnosis and prognostics of vehicle faults, leveraging Diagnostic Trouble Codes (DTCs) in line with industry-standard automation protocols. The proposed model asks for significant advancement in automotive diagnostics by reasoning through the root causes behind the fault codes given by DTC document to enhance fault interpretability and maintenance efficiency, primarily for the technician and in few cases, the vehicle owner. Here LLM is trained on vehicle specific service manuals, sensor datasets, historical fault logs, and Original Equipment Manufacturer (OEM)-specific DTC definitions, which leads to context-aware understanding of the vehicle situation and correlation of incoming faults. Approach validation has been done using field level real-world vehicle dataset for different running scenarios, demonstrating model’s ability to detect complex fault chains and successfully predicting the
The interior noise and thermal performance of the passenger compartment are critical criteria for ensuring driving comfort [1]. This paper presents the optimization of air conditioning (AC) compressor noise, specifically for the low-powered 1.0 L - ICE engine paired with a 120 cc IVDC compressor. This combination is quite challenging due to the high operational load & higher operating pressure. To enhance better in-cabin cooling efficiency, compressor’s operating efficiency must be improved, which necessitates a higher displacement of the compressor. However, increased displacement results in greater internal forces which leads to more structure-borne induced noise inside the cabin. For this specific configuration, the compressor operating pressure reached up to 25 bars under most driving conditions. During dynamic driving scenario, a metallic tonal noise from the compressor was reported in a compact vehicle segment. It is reported as very annoying to passengers inside. A comprehensive
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
This specification covers quality assurance sampling and testing procedures used to determine conformance to applicable material specification requirements of wrought corrosion- and heat-resistant steel and alloy products and of forging stock.
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
Eaton's decompression engine braking technology for medium and heavy-duty diesel engines delivers high braking power and provides several advantages to the commercial truck owner. Eaton offers rocker arm-based 1 stroke, 1.5 stroke, and 2 stroke systems for overhead cam and cam in block engine architectures. The Compression Release (CR) engine brake avoids overheating and fading of primary friction brake. It reduces or eliminates the need for a driveline retarder. One of the failure modes for Engine Brake (EB) system is excessive lateral displacement of the exhaust valve, caused by non-uniform pressure distribution across the valve during Brake Gas Recirculation (BGR) and Compression Release modes. This excessive deformation is referred to as Valve Wagging. Valve wagging significantly affects the structural stability of the engine brake mechanism. Analyzing its behavior is essential to minimize excessive wear on valve guide and Valve Seat Insert in new designs. Since evaluating the
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