Reliable Components Research Articles

Effect of Dispensing Type On Void Formation Using Convolutional Neural Network

Abstract Underfilling in flip chip packages is a critical component of reliability. This study utilized I-type, L-type, and U-type dispensing methods to address the issue namely voiding that creates empty spaces which compromises reliability. An automated solution using Convolutional Neural Network (CNN) is propose for void detection in chip images to replace the conventional manual inspection approach. The CNN model built on MobileNetV2, attains a mean average precision of 0.533. This method calculates void percentage, adhering to IPC standards, to determine product acceptance or rejection, offering an efficient solution for quality control in flip-chip package manufacturing.

Build Plate Roughness Study on Part Bonding for the Laser Powder Bed Fusion Process

AbstractAdditive manufacturing (AM) processes have emerged as valuable partners in conventional manufacturing, facilitating the production of low-batch components with complex geometries across diverse industries. However, despite ongoing advancements in various AM technologies, consistently achieving reliable and defect-free components remains a challenge. In powder metal AM, the use of substrates or build plates to support the entire build plays a crucial role in ensuring build stability. Build plate preparation typically involves surface grinding followed by finishing sanding, leading to variations in surface roughness between different manufacturing runs. This study aimed to elucidate the bonding characteristics at the build plate-part interface by investigating the porosity and build plate-part strength at different substrate surface roughness. To this end, a multi-roughness build plate was designed and fabricated for tensile testing via laser powder bed fusion (LPBF) processing of upright specimens. The specimens were subjected to computed tomography (CT) scans for porosity assessment, followed by tensile tests to evaluate the mechanical performance at the build plate-part interface (bp-p). CT inspection revealed no porosity at the interface for any roughness level. Furthermore, analysis of the tensile behavior in relation to substrate roughness (Ra values of 0.8 μm, 1.4 μm, 3.5 μm, and 4.4 μm) did not reveal statistically significant differences.

An analysis of polymer material selection and design optimization to improve Structural Integrity in 3D printed aerospace components

This paper presents an analysis of material selection and design optimization techniques to enhance the structural integrity of 3D printed aerospace components. The study highlights the importance of considering material characteristics and design factors such as shape, orientation, and support structures in order to achieve reliable and high-performance components. Various materials, including metals and polymers, commonly used in aerospace applications are evaluated, along with their properties and limitations in the context of 3D printing. Furthermore, the impact of different printing parameters on the structural integrity of the components is discussed. The study identifies optimization strategies such as topology optimization, lattice structures, and infill patterns, which can significantly improve the strength and durability of 3D printed parts. The results demonstrate the potential of these techniques to optimize the design and material selection of aerospace components, leading to lighter, more efficient, and reliable parts for air and space vehicles.

Methodology for determining effect of starting modes of internal combustion engine on change in engine performance characteristics

Introduction. As part of solving the urgent problem of forecasting technical impacts and residual life depending on operating time, a method for determining the effect of the starting mode on a change in the structural parameter determining reliability is presented. The essence of this technique and its novelty consists in combining two full-factor experiments to determine the effect of operating conditions on changes in the structural parameters of the engine. The purpose of the scientific study is to determine the effect of the starting internal combustion engine on the change in the performance characteristics of the engine.The novelty of the article lies in the combination of two full-factor experiments to determine the effect of operating conditions on changes in the structural parameters of the engine that determine its reliability.The reliability of the car (individual reliability properties or technical readiness) is influenced by the following factors: operating modes, operating conditions, quality of spare parts, qualifications and experience of drivers, etc. These factors show the direction and degree of the influence on the car and its reliability (technical readiness). The paper examines the influence of each factor on the reliability of the car.Methods and materials. To determine the effect of the starting mode of an internal combustion engine on a change in the structural parameter during forecasting, it is necessary to develop a methodology for determining the effect of the starting modes of internal combustion engines on changes in the structural parameters of the engine that determine its reliability. This technique involves conducting a full-factor experiment with four factors, i.e. 16 experiments. The combinations of different levels of factors are accepted as operational modes.To measure the magnitude of the influence of each factor on reliability, the article considers their measured values - indicators of the factor. There are from one to several indicators per factor. The values of the indicators are not constant, but change in accordance with changes in external influences (factors). Indicators are calculated based on the values of certain indicators of the direction of the influencing environment that a particular indicator describes.The total duration of the tests is assumed to be equal to the resource before major repairs.Results. The authors propose a method for determining the effect of starting modes of internal combustion engines on changes in the structural parameters of the engine that determine its reliability.The starting modes of the internal combustion engine and the dependence of reliability properties on external factors affecting the car at the time of engine start are considered. It is established that the degree of influence of factors is measured by the values of their indicators.Conclusion. As a result of using the proposed methodology for determining the effect of starting modes of internal combustion engines on changes in the structural parameters of the engine that determine its reliability, the principal possibility of an integrated approach to determine the effect on the structural parameters that determine reliability in the course of one series of experiments of both the influence of operating modes and engine starts, as well as the possibility of determining the influence and extreme values of factors is shown for reliability.Thus, the calculation of the values of one or another indicator enables to obtain the final values of the components of reliability (technical readiness). Indicators enables to determine the variability of factors affecting the components of reliability (technical readiness).

Kulla‐RIV: A composing model with integrity verification for efficient and reliable data processing services

AbstractThis article presents the design and implementation of a reliable computing virtual container‐based model with integrity verification for data processing strategies named the reliability and integrity verification (RIV) scheme. It has been integrated into a system construction model as well as existing workflow engines (e.g., Kulla and Makeflow) for composing in‐memory systems. In the RIV scheme, the reliability (R) component is in charge of providing an implicit fault tolerance mechanism for the processes of data acquisition and storage that take place in a data processing system. The integrity verification (IV) component is in charge of ensuring that data transmitted/received between two processing stages are correct and are not modified during the transmission process. To show the feasibility of using the RIV scheme, real‐world applications were created by using different distributed and parallel systems to solve use cases of satellite and medical imagery processing. This evaluation revealed encouraging results as some solutions that assumed the cost (overhead) of using the RIV scheme, for example, Kulla (the Kulla‐RIV solution), achieve better response times than others without the RIV scheme (e.g., Makeflow) that remain exposed to the risks caused by to the lack of RIV strategies.

Adhesive Solutions for Closed Cavity Packaging

The trend towards autonomous driving demands continuously increasing safety requirements and hence reliable components such as CMOS image sensors or communication devices. To provide such components, manufactures are facing ever greater challenges as the sensor packages need to be airtight sealed over their whole lifetime. Typical defects that can be observed when using conventional lid attach materials for closed cavity packages in tests at automotive level (e.g., according to AEC Q100) are pop-up effects and delamination. To avoid these defects and to meet the increasing reliability requirements of the semiconductor industry, DELO Industrial Adhesives has developed special adhesives that not only ensure reliable bonding while keeping narrow and high bondlines, but also improve process stability. Features such as light/heat B-stage or light fixation help to keep the attached lid in place during heat curing and other subsequent steps like temperature cycling, humidity storage and even reflow.

A review of components of reliability for the evaluation of Programmable logic controller

A review of components of reliability for the evaluation of Programmable logic controller

Application of machine learning in chemical engineering: outlook and perspectives

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.

Adhesive Solutions for Closed Cavity Packaging

The trend towards autonomous driving demands continuously increasing safety requirements and hence reliable components such as CMOS image sensors or communication devices. To provide such components, manufactures are facing ever greater challenges as the sensor packages need to be airtight-sealed over its whole lifetime. Typical defects that can be observed when using conventional lid attach materials for closed cavity packages in tests at automotive level (e.g., according to AEC Q100) are pop-up effects and delamination. To avoid these defects and to meet the increasing reliability requirements of the semiconductor industry, DELO Industrial Adhesives has developed special adhesives that not only ensure reliable bonding while keeping narrow and high bondlines, but also improve process stability. The required adhesive properties were defined by an FE analysis model. To test suitability of the new developments, dummy image sensor packages were built up, which passed through the testing of the bearing, according to AEC Q 100 standard. Finally, the tightness of the bondline was checked via microscope and other methods. Features such as light/heat B-stage or light fixation help to keep the attached lid in place during heat curing and other subsequent steps like temperature cycling, humidity storage or reflow.

Geometrical projection improved multi-objective particle swarm optimization for unsupervised nonlinear hyperspectral unmixing

ABSTRACT In recent years, a series of bilinear mixture models (BMMs) have been well studied, based on which nonlinear unmixing algorithms for hyperspectral images have been proposed. However, the performance of unsupervised nonlinear spectral unmixing can be largely affected by the collinearity and the nonconvex unmixing problem’s inherent complexity. To solve the problems, this paper proposes a geometrical projection improved multi-objective particle swarm optimization (GPMOPSO) method for nonlinear unmixing, which consists of two dynamically updating procedures. First, under the assumption of the BMMs, observed pixels are geometrically projected to their approximate linear mixture components to alleviate the collinearity. Second, the linear mixture components are efficiently decomposed to refined endmembers and abundances in an improved linear unmixing framework using multi-objective particle swarm optimization. Two advanced multi-objective optimization strategies are adopted to balance the influence of an endmember distance constraint and an abundance sparsity constraint on the achieved unsupervised linear unmixing process, respectively. Then, accurate unmixing variables can be fed back to the geometrical projection procedure to produce more reliable linear mixture components which facilitate the execution of the former in turn. Experimental results of both simulated data and real hyperspectral remote sensing data verify the superior nonlinear unmixing performance of the proposed method.

Improving the OWC Wave Energy Converter Power Take-Off Efficiency throughout Experimental and Numerical Characterisation of an SCIG

The increasing interest in the use of renewable energy technologies is directing attention towards the potential contribution of marine energy technologies, especially ocean wave energy, to world energy demand. While open-sea demonstrations of full-scale devices have been carried out to validate several technologies, the focus now is shifting to optimising the components for efficiency and reliability. The efficiency of the electrical generator plays a crucial role in wave-to-wire numerical models for converting wave energy into usable electricity. It provides essential data that enables the industry to reduce technical risks and uncertainties. Wave-to-wire models typically simplify the generator’s efficiency through assuming a single curve based on the load. This curve is usually provided by the machine manufacturers for the nominal rotational speed. However, the rotational speed varies in the case of air turbines used in OWC devices. Therefore, to accurately estimate decision variables derived from these models, a comprehensive efficiency map is necessary. This map should demonstrate the performance at different rotational speeds and loads, as it directly influences the estimation of key parameters. The main objective of the present work is to improve the generator behaviour of an OWC for different generator operation regimes. For this purpose, a numerical model of the generator’s efficiency will be developed throughout the segregation of losses and validated experimentally. Finally, an optimal control law will be presented to maximise the electrical power output of the wave energy converter, considering the efficiency of both the generator and the turbine.

How Freelance Developers Embrace Software Reuse Practice? A perspective analysis using clustering techniques

The advancement of ICT technologies has modernized software development practices, thus presenting opportunities for developers to reuse software components in developing new software. These practices allow developers to use reliable reusable software components to quickly develop software. However, not much research has reported how freelance developers capitalize on these practices, despite the rapid rise of freelancing development. This research investigates how freelance developers embrace software reuse practices to achieve project success. A survey that gathers 351 responses from freelancers was analyzed using clustering techniques, revealing high levels of self-efficacy and satisfaction towards deploying software reuse practices and highlighting the need for software development firms to embrace this method.

The utilization of 3D printing in surgery as an innovative approach to preoperative planning.

3D printing, a concept over 40 years old, is finding broader application in clinical practice thanks to technological advancements. At University Hospital Ostrava, 3D printing is utilized to create anatomically accurate models of specific patients before surgical procedures based on imaging data. 3D printing is employed as a complement to conventional imaging methods to produce morphologically precise models of anatomical structures of individual patients. These models primarily serve for preoperative planning in elective abdominal, vascular, and thoracic surgery. They are also used in planning osteosynthesis of complex fractures and corrective osteotomies. Multicolor printing, although increasing the process's time demands, allows better clarity and differentiation of individual anatomical structures within a single model. Compared to 2D images, 3D models provide better spatial orientation and awareness of the operated structures, contributing to improved surgical outcomes. The benefits of 3D printing in preoperative planning and patient education are confirmed by studies across the fields ranging from cardiac surgery to traumatology. After overcoming initial challenges, 3D printing has become a reliable component of the surgical arsenal at University Hospital Ostrava for elective surgery. While 3D printing does not represent a universal answer to all medical challenges, its role is highly beneficial and promising in many indicated cases.

You don't know why you (don't) exercise: The relationship between automatic processes and physical activity (or sedentary behavior): A meta-analysis.

This meta-analysis aimed to examine the relationship between automatic processes and behavior as well as their moderators. There were 126 effect sizes (Fisher's Z) extracted from 55 independent research studies involving 10,432 participants. Meta-analyses were conducted using Comprehensive Meta-Analysis Version 3.3. Univariate meta-regression models were used to analyze underlying moderators. Meta-analysis after correction found that automatic processes could significantly and positively predict behavior, but the effect size was small (Fisher's Z = 0.057, p < 0.001, 95% CI [0.043, 0.070]). Different components of automatic processes, task reliability, and study design moderated the relationship between automatic processes and behavior. Future research could explore automatic measures at a neurophysiological level or use population-matched stimuli for specific populations to improve measure validity. In addition, future research should accumulate evidence on how to reduce sedentary behavior.

Development of EN AW-6082 Metal Foams and Stochastic Foam Modeling for the Individualization of Extruded Profiles

AbstractLightweight design and hybrid components enable innovative and new component concepts, especially when combining structurally reliable metal components with individualized polymer components. In this research, a process for additive manufacturing polymers on the surface of extruded aluminum profiles is examined. The extrusion process is adapted to produce foamable aluminum profiles, which can be utilized to enable a form fit between the two materials and ensures sufficient bond strength. For this purpose, a novel aluminum block material based on the standard wrought alloy EN AW-6082 was developed. It consists of a solid EN AW-6082 core and powder metallurgically produced outer layer, which allows local foaming of the aluminum profile surface. The main objective of this study was to optimize the bond strength of the hybrid aluminum-polymer components. The methods employed include fabricating aluminum test specimens, performing mechanical tests, x-ray microscopy to analyze the pore structure and evaluating the 3D pore distribution and the wall thickness. Virtual foam models were created to numerically investigate suitable pore sizes and foam geometries for form-fit with the polymer. The porosity achieved as a function of the processing of the components are discussed and a comparison is made between the real and virtual pore structures.

Investigating the effect of external heat flux on the crack growth process in an aluminum nanoplate using molecular dynamics simulation

The effect of heat flux (HF) is crucial for understanding the behavior of materials, particularly in terms of crack propagation. HF can induce thermal stresses that accelerate crack growth, which can compromise the structural integrity and safety of materials. However, by studying the effect of HF, we can gain insights into the mechanisms that lead to crack initiation and growth. The importance of this study lies in its contribution to understanding the impact of external HF (EHF) on the cracking behavior of Aluminum nanoplates. For this purpose, EHF with 0.01, 0.02, 0.03, 0.05, and 0.1 W/m2 are added to the initial structure. By inserting EHF into the modeled samples, the atomic evolution of them is investigated. This study was performed using LAMMPS software and molecular dynamics (MD) simulation. Numerically, the maximum velocity of atoms increased from 4.81187 Å/ps to 4.81236 Å/ps as the EHF rose from 0.01 W/m2 to 0.1 W/m2. The maximum stress of samples increases from 151.689 GPa to 151.712 GPa by the EHF ratio enlarging. Finally, the crack length reaches to the maximum value (33.492 Å) by the EHF ratio set to 0.03 W/m2.By investigating the atomic evolution and crack growth process under different EHF values, the study provides valuable insights into the underlying mechanisms and thermal effects that influence crack propagation in these materials. This knowledge can be utilized to optimize the design and performance of aluminum nanoplates in practical applications, such as in the development of more robust and reliable structural components or devices.

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling.

Reliability-Related Interpretations of Algebraic Inequalities

New results related to maximizing the reliability of common systems with interchangeable redundancies at a component level have been obtained by using the method of algebraic inequalities. It is shown that for systems with independently working components with interchangeable redundancies, the system reliability corresponding to a symmetric arrangement of the redundant components is always inferior to the system reliability corresponding to an asymmetric arrangement of the redundant components, irrespective of the probabilities of failure of the different types of components. It is also shown that for series–parallel systems, the system reliability is maximized by arranging the main components in ascending order of their probabilities of failure, whereas the redundant components are arranged in descending order of their probabilities of failure. Finally, this article derives rigorously the highly counterintuitive result that if two components must be selected from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> batches containing reliable and faulty components with unknown proportions, the likelihood that both components will be reliable is maximized by selecting both components from a randomly selected batch.

Black-Box Fuzzing for Security in Managed Networks: An Outline

Service providers are adopting open-source technology and open standards in their next-generation networks. This gives them great flexibility and spurs innovation. But it also means that they must ensure proper interoperability between components; otherwise, vulnerabilities might get introduced in their networks. Unfortunately, state-of-the-art vulnerability scanning tools are unable to handle the complexity of service provider networks. In this paper we show how interoperability issues between seemingly reliable components introduce an injection vulnerability that allows us to control a firewall-protected network management system. We also extend the state-of-the-art in black-box fuzzing to give service providers a tool for combating similar issues.

Metal-metal and metal-composite joints with 3D printed aluminium substrates: effect of surface treatment on the mode I fracture toughness

ABSTRACT Adhesive bonding of composite laminates to metals is an increasingly employed technique to manufacture lightweight structures. However, this type of joints is sensitive to defects and composite delamination near the bonded area. Thus, increasing their damage tolerance is fundamental to design reliable multi-material components. This work is the first step in a project aimed at improving the fracture toughness of Carbon Fibre Reinforced Polymer (CFRP)/metal joints, exploiting Additive Manufacturing (AM) to produce small features on the metal adherend. This first step is devoted to the qualification of the Mode I fracture toughness of the as-built AM surface. Thus, Double Cantilever Beam (DCB) tests are carried out on metal/metal secondary bonded and CFRP/metal co-bonded joints. The metal part is obtained by Selective Laser Melting (SLM) of AlSi10Mg aluminium alloy, without surface pre-treatment to preserve the as-built roughness. The fracture toughness of the AM un-treated joints is also compared to that of joints in which the metal adherend is sandblasted. Optical profilometry is employed to evaluate the surface morphology of both as printed and sandblasted adherends. The results show that the untreated joints have comparable fracture toughness to their treated counterparts, despite manifesting dissimilar failure modes due to the different surface morphologies.