Browse Topic: Reliability
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
In view of the complexity of railway engineering structure, the systematicness of professional collaboration and the high reliability of operation safety, this paper studied the spatial-temporal information data organization model with all elements in whole domain for Shuozhou-Huanghua Railway from the aspect of Shuozhou-Huanghua Railway spatial-temporal information security. Taking the unique spatial-temporal benchmark as the main line, the paper associated different spatial-temporal information to form an efficient organization model of Shuozhou-Huanghua Railway spatial-temporal information with all elements in the whole domain, so as to implement the effective organization of massive spatial-temporal information in various specialties and fields of Shuozhou-Huanghua Railway; By using GIS (Geographic Information System) visualization technology, spatial analysis technology and big data real-time dynamic rendering technology, it was realized the real-time dynamic visualization display
Researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Vienna University of Technology (TU Wien) have invented a new type of tunable semiconductor laser that combines the best attributes of today’s most advanced laser products, demonstrating smooth, reliable, wide-range wavelength tuning in a simple, chip-sized design.
ACT Expo 2025 had a fleet of new commercial vehicle launches as well as displays for models already on the market. One such existing chassis was the Workhorse W56, an electric step van designed for Class 5/6 last-mile delivery. Unlike many of its competitors, Workhorse did not set out to be a technological leader with the W56. Rather, the company took the approach of leveraging the best of the currently available and applicable technologies to produce a durable, reliable and producible product that just happened to be powered by electrons.
Letter from the Guest Editors
At a time when medical technology is advancing rapidly, the demand for precision in manufacturing has never been greater. The medical device industry is pushing the boundaries of design, requiring components that are not only smaller and more intricate but also biocompatible, reliable, and capable of meeting stringent regulatory standards. To address these challenges, manufacturers are increasingly turning to photochemical etching (PCE) — a process that is proving indispensable in high-precision medical applications.
In the highly regulated world of medical device manufacturing, post-production cleaning is essential for ensuring safety, compliance, and best performance. Beyond removing surface contamination, it must address intricate geometries, sensitive materials, and strict industry standards. Effectively managing these challenges is key to meeting regulatory requirements and ensuring reliable device function.
A continuous effort to improve reliability and efficiency of processes is at the forefront of any successful business. One methodology that can have a crucial impact in this effort is Lean Six Sigma (LSS), which aims to reduce variability and wasteful activities within a company’s processes, in turn leading to improvements in areas such as customer satisfaction, employee morale, regulatory compliance, and profitability. In the medical device industry, where a seemingly minor error could be life-threatening, LSS can play a pivotal role in patient safety. This article presents a case study illustrating the benefits of LSS for a medical device manufacturing company, as well as one of its key customers.
Researchers at the Beijing Institute of Technology have unveiled an innovative electrothermal microgripper that promises to improve microelectronics, biomedical engineering, and MEMS applications. With its remarkable deformation capabilities, excellent size compatibility and reliable catch strength, the microgripper enables the manipulation and assembly of micro- and nano-scale objects with exceptional efficiency. This technological advancement is poised to enhance microscale engineering and pave the way for innovations across various high-tech industries.
Manufacturers in all industries rely on networks of specialized suppliers to effectively source the components they need to serve their customers. Trust, reliability, and consistency are important — and for producers of medical devices, these qualities are especially critical, given the often life-saving nature of their end-use products.
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
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 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.
Accurate object pose estimation refers to the ability of a robot to determine both the position and orientation of an object. It is essential for robotics, especially in pick-and-place tasks, which are crucial in industries such as manufacturing and logistics. As robots are increasingly tasked with complex operations, their ability to precisely determine the six degrees of freedom (6D pose) of objects, position, and orientation, becomes critical. This ability ensures that robots can interact with objects in a reliable and safe manner. However, despite advancements in deep learning, the performance of 6D pose estimation algorithms largely depends on the quality of the data they are trained on.
Since the early 1980s, the automotive industry has used hydraulically actuated (servo-hydraulic) test systems to simulate operating speeds and road conditions for testing OEM components and fully assembled vehicles. They have helped unlock vast improvements in the quality, safety, and reliability of the cars and trucks coming off the world’s assembly lines.
Unmanned Underwater Vehicles (UUVs) are used around the world to conduct difficult environmental, remote, oceanic, defense and rescue missions in often unpredictable and harsh conditions. A new study led by Flinders University and French researchers has now used a novel bio-inspired computing artificial intelligence solution to improve the potential of UUVs and other adaptive control systems to operate more reliability in rough seas and other unpredictable conditions.
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.
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