Browse Topic: Quality, Reliability, and Durability
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Moan noise is a low-frequency noise occurring in the 170–500 Hz frequency ranges. While it frequently appears in vehicles equipped with a rear Coupled Torsion Beam Axle (CTBA), the exact cause, generation mechanism and clear solutions remain unidentified. For those reasons, we have developed a moan noise analysis method capable of representing the moan noise phenomenon in vehicles with rear CTBA along with an automation tool. From these results, we can use moan analysis models to reduce real moan noise problems. Consequently, this not only enhances customer satisfaction and vehicle quality but also significantly increases the work efficiency of vehicle designers through design modification in the preliminary stages of vehicle development
Tackling the heavy computation of affine formation control under switching topologies—rooted in frequent stress matrix recalculation—this paper presents a distributed control framework fusing consistency estimation with dynamic error constraints for efficient coordination. In a leader-follower architecture, affine transformation parameters are estimated by followers using local information—global stress matrix solutions are thus avoided. A time-varying constraint function and Lyapunov stability analysis are devised to ensure tracking errors converge to specified accuracy within a predetermined time. Both theoretical analysis and simulation results show that this method greatly simplifies computation. It also supports flexible formation transformations such as translation and scaling, making it a stable and reliable solution for dynamic scenes.
In the rapidly evolving arena of high-power laser technology, precision and reliability are becoming increasingly important. The increasing powers in laser applications into the 10's of kW and, in some cases, 100's of kW, present additional challenges when applied to internal laser system components. For Precitec, respected globally for their advanced laser processing heads, delivering consistent performance to customers is both a challenge and a necessity. This case study explores how Precitec's U.S. operation has integrated laser measurement technology into its workflow; a non-contact beam characterization system significantly elevates the company's service, quality assurance, and development processes for high-power laser applications.
To safely, efficiently, and high-quality complete the mechanical testing of batch-produced manned spacecraft during the China Space Station (CSS) phase, a series of optimization measures were proposed based on system engineering principles. These measures cover the entire mechanical testing process from preparation to implementation, including: establishing a standardized mechanical testing documentation system; reducing the number of mechanical sensors that do not affect result evaluation; pre-identifying and measuring background noise; digitizing test notching and evaluation methods; and standardizing and automating testing procedures. Additionally, targeted measures for test safety and quality control were implemented, including regular inspections of reusable spacecraft components, strict control of test hazards and operational risks, and standardized management of ground support equipment (GSE) through regular inspections. The proposed optimization and control measures have been validated through applications in batch-produced manned spacecraft during the CSS phase. The results show that: the generalization rate of mechanical testing documentation exceeds 80%; the number of mechanical sensors has been reduced by more than 10%; the test preparation period has been shortened by over 4 days; test efficiency has been improved by 30%; the single-direction test duration has been reduced by more than 50%; and the total test cycle has been shortened by 25%. These results indicate that the proposed optimization and control measures are reasonable and feasible, which effectively reduces redundant test operations and items, lowers potential test risks, improves test efficiency, shortens the overall test cycle, enhances test safety, and ensures the high-quality completion of mechanical testing for batch-produced manned spacecraft.
With the continuous improvement of performance requirements for aviation equipment, the importance and complexity of hydraulic systems as the core carrier of flight control are becoming increasingly prominent. The cleanliness of aircraft hydraulic pipelines directly affects the reliability and flight safety of hydraulic systems, and it is necessary to use specialized cleaning and testing equipment during design and manufacturing to achieve efficient cleaning. The design of traditional cleaning equipment relies on experience-driven development, with mechanical, hydraulic, and electrical systems developed independently. There are problems such as unclear requirement definitions, low efficiency of interdisciplinary collaboration, and lagging validation, making it difficult to achieve the goal of forward design. Therefore, this study introduces Model-based Systems Engineering (MBSE) method in the development process of pipeline cleaning test equipment, proposes a modeling process based on RFLP (Requirements-Function-Logical-Physical), and uses SysML system modeling language to construct a top down design model system for aircraft hydraulic pipeline cleaning equipment. Through requirement analysis modeling, functional behavior definition, and system architecture design, the significant advantages of MBSE method in the development of complex aviation test equipment have been verified, effectively improving the bold design capability and top down design efficiency. MBSE method can not only improve the design efficiency of equipment, but also promote the intelligent and efficient operation of equipment, which has important significance for the development of intelligent manufacturing and electromechanical integration technology.
In the conversation surrounding electrification, the vehicle itself typically dominates the headlines. But those operating on remote jobsites in the mining, construction and agriculture sectors know the machine is only half the equation. These industries prioritize reliability and uptime and require machines that can handle grueling shifts in demanding environments without compromise. Power providers in these heavy-duty, off-highway markets must move beyond the battery itself to explore a holistic approach to infrastructure when it comes to powering remote jobsites. The first step to success is understanding the fundamental differences between off-highway duty cycles and on-highway applications. While on-highway applications like long-hauling trucks benefit from steady-state operation and passive airflow for cooling, off-highway machines often operate at high torque for extended periods, with little to no forward movement. In these scenarios, there is no passive cooling to rely on or regular refueling stations at the next exit. Success, therefore, is defined by the engineering required to ensure that electric machines deliver the same productivity as diesel, even when operated at their limits in the most rugged, remote conditions.
A holistic approach to protective design can greatly reduce or eliminate the harmful effects of harsh environmental conditions on electric actuators. Tolomatic, Inc., Hamel, MN With their performance and efficiency advantages over hydraulics, electric actuators are increasingly used in unforgiving operating environments that demand hardened components. Yet, not every actuator is up the task of thriving or even surviving under the harshest conditions. Most off-the-shelf actuators are simply not engineered to withstand the corrosive agents, temperature extremes, ingress of foreign material, high shock loads and electromagnetic interference (EMI) that characterize many harsh environments. If you're designing a system that will need to perform in these conditions, here's an overview of what to look for in an actuator.
For decades, hydraulic systems have been relied upon to do all the heavy lifting in aerospace. They are powerful, reliable, and deeply embedded in how aircraft are designed, to the extent that - for many engineers - they are simply part of the landscape. Now, however, things are beginning to change. From advanced air mobility platforms now entering certification to next-generation commercial aircraft on 10-year horizons, electric and electro-hydraulic actuation is steadily replacing the heavy, centralized hydraulic architectures that have defined flight control for decades. Understanding why means stepping back from the actuator itself and looking at the aircraft as a whole system - and, increasingly, as an integrated motion control challenge.
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