Browse Topic: Quality, Reliability, and Durability
The half vehicle spindle-coupled multi-axial input durability test has been broadly used in the laboratory to evaluate the fatigue performance of the vehicle chassis systems by automotive suppliers and OEMs. In the lab, the front or rear axle assembly is usually held by fixtures at the interfaces where it originally connects to the vehicle body. The fixture stiffness is vital for the laboratory test to best replicate the durability test in the field at a full vehicle level especially when the subframe of the front or rear axle is hard mounted to the vehicle body. In this work, a multi-flexible body dynamics (MFBD) model in Adams/Car was utilized to simulate a full vehicle field test over various road events (rough road, braking, steering). The wheel center loads were then used as inputs for the spindle coupled simulations of the front axle with a non-isolated subframe. Three types of fixtures including trimmed vehicle body, a rigid fixture with softer connections and a rigid fixture
This paper reviews the current situation in the terms and definitions that influence the development of testing and prediction in automotive, aerospace and other areas of engineering. The accuracy of these terms and definitions is very important for correct simulation, testing and prediction. This paper aims to define accurate terms and definitions. It also includes the author’s recommendations for improving this situation and preparing new standards.
Video analysis plays a major role in many forensic fields. Many articles, publications, and presentations have covered the importance and difficulty in properly establishing frame timing. In many cases, the analyst is given video files that do not contain native metadata. In other cases, the files contain video recordings of the surveillance playback monitor which eliminates all original metadata from the video recording. These “video of video” recordings prevent an analyst from determining frame timing using metadata from the original file. However, within many of these video files, timestamp information is visually imprinted onto each frame. Analyses that rely on timing of events captured in video may benefit from these imprinted timestamps, but for forensic purposes, it is important to establish the accuracy and reliability of these timestamps. The purpose of this research is to examine the accuracy of these timestamps and to establish if they can be used to determine the timing
This paper introduces an innovative digital solution for the categorization and analysis of fractures in Auto components, leveraging Artificial Intelligence and Machine Learning (AI/ML) technologies. The proposed system automates the fracture analysis process, enhancing speed, reliability, and accessibility for users with varying levels of expertise. The platform enables users to upload images of fractured parts, which are then processed by an AI/ML engine. The engine employs an image classification model to identify the type of fracture and a segmentation model to detect and analyze the direction of the fracture. The segmentation model accurately predicts cracks in the images, providing detailed insights into the direction and progression of the fractures. Additionally, the solution offers an intuitive interface for stakeholders to review past analyses and upload new images for examination. The AI/ML engine further examines the origin of the fracture, its progression pattern, and the
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
At present, electric head restraints have been developed locally, so overseas mechanisms are used. In this study, two concept mechanisms were developed, and in addition, one patent for a wing-out head restraint mechanism was additionally applied. The new mechanism has had an excellent effect on cost reduction and improvement of operating noise compared to the current one.
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
Aerospace and defense system designers are demanding scalable and high-performance I/O solutions. While traditional mezzanine standards have proven reliable, they often fall short of meeting modern bandwidth, size, and flexibility requirements. This challenge is particularly evident in aerospace and defense applications where high-speed data processing must align with stringent size, weight, and power (SWaP) constraints. Current mezzanine solutions also face significant limitations in scalability, thermal management, and I/O density. These constraints can lead to compromised system performance and limited upgrade paths in applications where adaptability is crucial. This article explores how the new VITA 93 (QMC) standard addresses these challenges through its innovative QMC architecture, enabling unprecedented flexibility, scalability, and rugged reliability while maintaining compatibility with existing and future systems. It also covers how VITA 93 (QMC) builds on lessons learned from
The objective of this research is to develop an optimization strategy for the Electrochemical Drilling process on Nimonic alloy material, taking into account various performance factors. The optimization strategy relies on the integration of the Taguchi method with Grey Relational Analysis (GRA). Nimonic is extensively utilized in aerospace, nuclear, and marine industries, specifically in situations that are prone to corrosion. The experimental trials are structured based on Taguchi's principle and encompass three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This inquiry examines performance indicators like the rate of material removal, surface roughness, as well as geometric parameters such as overcut, shape, and orientation tolerance. Based on the investigation, it is determined that the feed rate is the primary factor that directly affects the intended performance criteria. In order to enhance the accuracy of predictions, multiple regression
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
Fused Deposition Modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability, making it well-suited for use in industries such as footwear, automotive, and consumer goods. Hoses, gaskets, seals, external trim, and interior components are just a few of the many uses for thermoplastic polyurethanes (TPU) in the automobile industry. The objective of this study is to enhance the performance of Fused Deposition Modeling (FDM) by optimizing the parameters specifically for Thermoplastic Polyurethane (TPU) material. This will be achieved by employing a Taguchi-based Grey Relational Analysis (GRA) method. The researchers conducted experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
A 20-cell self-humidifying fuel cell stack containing two types of MEAs was assembled and aged by a 1000-hour durability test. To rapidly and effectively analyze the primary degradation, the polarization change curve is introduced. As the different failure modes have a unique spectrum in the polarization change curve, it can be regarded as the fingerprint of a special degradation mode for repaid analysis. By means of this method, the main failure mode of two-type MEAs was clearly distinguished: one was attributed to the pinhole formation at the hydrogen outlet, and another was caused by catalyst degradation only, as verified by infrared imaging. The two distinct degradation phases were also classified: (i)conditioning phase, featuring with high decay rate, caused by repaid ECSA change from particle size growth of catalyst. (ii) performance phase with minor voltage loss at long test duration, but with RH cycling behind, as in MEA1. Then, an effective H2-pumping recovery is conducted
The present research explores the potential of high-performance thermoplastics, Polymethyl Methacrylate and Polyurethane, to enhance the passive safety of automotive instrument panels. The purpose is to evaluate and compare the passive safety of these two materials through the conduct of the Charpy Impact Test, Tensile Strength Test, and Crush Test —. For this, five samples were prepared in the case of each material via injection moulding, which enabled reliability, and consistency of the findings. As a result, it was found that in the case of the Charpy Impact Test, the average impact resistance varies with PMMA exhibiting a level of 15.08 kJ/m2 as opposed to the value of 12.16 kJ/m2 for PU. The Tensile Strength Test produced the average tensile strength of 50.16 for PMMA and 48.2 for PU, which implied superior structural integrity under tension for the first type of thermoplastic. Finally, the Crush Test showed that PMMA is more resistant to crushes on average than PU with the
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