Engineering Metrics Research Articles

Interactive infill topology optimisation guided by user drawn patterns

ABSTRACT Widespread use of topology optimisation as a design tool for additive manufacturing faces major inhibiting obstacles, such as high computational costs and complexity, concern for other failure modes, and manufacturability. Interactive infill topology optimisation presents an alternative approach to circumvent some of these barriers. The novel contribution of the present work prompts the user to draw a tailored infill pattern, specify regions of interest to locate the infill, and control how strictly the pattern is replicated in the material layout of the design using appearance constraints. This approach improves engineering metrics not directly included in the optimisation formulation by incorporating the user’s engineering experience, thereby avoiding increased computational costs, parameter tuning, and numerical artifacts associated with complex objective functions and constraints. Two 2D benchmark examples increase the linear buckling resistance and energy absorption, respectively, and a 2.5D example minimises compliance while reducing the quantity of overhang supports for additive manufacturing.

Thermal Performance of Cattaneo-Christov Heat Flux in MHD Radiative Flow of Williamson Nanofluid Containing Motile Microorganisms and Arrhenius Activation Energy

The aim of this study is to characterize the Cattaneo-Christov heat flux in magnetohydrodynamics (MHD) radiative flow of Williamson nanofluid through a stretched sheet when confronted with heat source, Arrhenius activation energy and motile microorganisms. Also, the Darcy-Forchheimer model is a widely used and precise technique for modeling fluid dynamics in porous medium. The controlling partial differential equations (PDE) are minimized to ordinary differential equations (ODE) using suitable similarities. The final outcomes were computed numerically using the BVP4C MATLAB interface. The graphical portrayal serves to comprehensively show and build upon the effect of several aspects. The results reveal the increased velocity distribution with the intensifying values of magnetic field, porosity, suction, and Forchheimer number. The thermal gradient is enhanced with the increase in heat source, and radiation, while it diminishes with the augmentation of the thermal relaxation parameter. Furthermore, the concentration maximizes when the activation energy is large. This research shows a favorable link between Peclet quantity and motile microbe density. Additionally, this work numerically analyses and tabulates important engineering metrics such as skin friction, Nusselt number, Sherwood number, and motile density number. The present study has huge interest due to its applications such as food processing, crystal formation in rocks, microelectronic semiconductors, crystal fiber, electronic power plants, and many more.

The significance of quadratic thermal radiative scrutinization of a nanofluid flow across a microchannel with thermophoretic particle deposition effects

Abstract The investigation of thermal radiation and thermophoretic impacts on nano-based liquid circulation in a microchannel has a significant impact on the cooling of microscale equipment, microliquid devices, and many more. These miniature systems can benefit from the improved heat transfer efficiency made possible by the use of nanofluids, which are designed to consist of colloidal dispersion of nanoparticles in a carrier liquid. Understanding and precisely modeling the thermophoretic deposition (TPD) of nanoparticles on the channel surfaces is of utmost importance since it can greatly affect the heat transmission properties. This work examines the complex interaction between quadratic thermal radiation, magnetohydrodynamics, and TPD in a permeable microchannel. It aims to solve a significant knowledge gap in microfluidics and thermal and mass transport. The governing equations are simplified by applying suitable similarity restrictions, and computing solutions to the resulting equations is done using the Runge‒Kutta Fehlberg fourth‒fifth-order scheme. The results are shown using graphs, and significant engineering metrics are analyzed. The outcomes show that increased Eckert number, magnetic, and porous factors will improve the thermal distribution. Quadratic thermal radiation shows the greater thermal distribution in the presence of these parameters, while Linear thermal radiation shows the least thermal distribution. The rate of thermal distribution is higher in the linear thermal distribution case and least in the nonlinear thermal radiation case in the presence of radiation and solid fraction factors. The outcomes of the present research are helpful in improving the thermal performance in microscale devices, electronic devices cooling, health care equipment, and other microfluidic applications.

Assessment of modified chitosan composite in acidic reservoirs through pilot and field-scale simulation studies

Chemical flooding through biopolymers acquires higher attention, especially in acidic reservoirs. This research focuses on the application of biopolymers in chemical flooding for enhanced oil recovery in acidic reservoirs, with a particular emphasis on modified chitosan. The modification process involved combining chitosan with vinyl/silane monomers via emulsion polymerization, followed by an assessment of its rheological behavior under simulated reservoir conditions, including salinity, temperature, pressure, and medium pH. Laboratory-scale flooding experiments were carried out using both the original and modified chitosan at conditions of 2200 psi, 135,000 ppm salinity, and 196° temperature. The study evaluated the impact of pressure on the rheological properties of both chitosan forms, finding that the modified composite was better suited to acidic environments, showing enhanced resistance to pressure effects with a significant increase in viscosity and an 11% improvement in oil recovery over the 5% achieved with the unmodified chitosan. Advanced modeling and simulation techniques, particularly using the tNavigator Simulator on the Bahariya formations in the Western Desert, were employed to further understand the polymer solution dynamics in reservoir contexts and to predict key petroleum engineering metrics. The simulation results underscored the effectiveness of the chitosan composite in increasing oil recovery rates, with the composite outperforming both its native counterpart and traditional water flooding, achieving a recovery factor of 48%, compared to 39% and 37% for native chitosan and water flooding, thereby demonstrating the potential benefits of chitosan composites in enhancing oil recovery operations.

Minimizing electric fields and increasing peripheral nerve stimulation thresholds using a body gradient array coil.

To demonstrate the performance of gradient array coils in minimizing switched-gradient-induced electric fields (E-fields) and improving peripheral nerve stimulation (PNS) thresholds while generating gradient fields with adjustable linearity across customizable regions of linearity (ROLs). A body gradient array coil is used to reduce the induced E-fields on the surface of a body model by modulating applied currents. This is achieved by performing an optimization problem with the peak E-field as the objective function and current amplitudes as unknown variables. Coil dimensions and winding patterns are fixed throughout the optimization, whereas other engineering metrics remain adjustable. Various scenarios are explored by manipulating adjustable parameters. The array design consistently yields lower E-fields and higher PNS thresholds across all scenarios compared with a conventional coil. When the gradient array coil generates target gradient fields within a 44-cm-diameter spherical ROL, the maximum E-field is reduced by 10%, 18%, and 61% for the X, Y, and Z gradients, respectively. Transitioning to a smaller ROL (24 cm) and relaxing the gradient linearity error results in further E-field reductions. In oblique gradients, the array coil demonstrates the most substantial reduction of 40% in the Z-Y direction. Among the investigated scenarios, the most significant increase of 4.3-fold is observed in the PNS thresholds. Our study demonstrated that gradient array coils offer a promising pathway toward achieving high-performance gradient coils regarding gradient strength, slew rate, and PNS thresholds, especially in scenarios in which linear magnetic fields are required within specific target regions.

Data-Driven Car Drag Prediction With Depth and Normal Renderings

Abstract Generative artificial intelligence (AI) models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is necessary. However, the currently used representations of 3D shapes either require extensive computational resources to learn or suffer from significant information loss, which impairs their effectiveness in surrogate modeling. To address this issue, we propose a new 2D representation of 3D shapes. We develop a surrogate drag model based on this representation to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 4535 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an R2 value above 0.84 for various car categories. Our model is implemented using deep neural networks, making it compatible with recent AI image generation tools (such as stable diffusion) and a significant step toward the automatic generation of drag-optimized car designs. Moreover, we demonstrate a case study using the proposed surrogate model to guide a diffusion-based deep generative model for drag-optimized car body synthesis.

Selecting optimal software code descriptors-The case of Java.

Over the last 25 years, a considerable proliferation of software metrics and a plethora of tools have emerged to extract them. While this is indeed positive concerning the previous situations of limited data, it still leads to a significant problem arising both from a theoretical and a practical standpoint. From a theoretical perspective, several metrics are likely to result in collinearity, overfitting, etc. From a practical perspective, such a set of metrics is difficult to manage and companies, especially small ones, may feel overwhelmed and unable to select a viable subset of them. Still, so far it has not been fully understood what is a viable subset of metrics suitable to properly manage software projects and products. In this paper, we attempt to address this issue. We focus on the case of programs written in Java and we consider classes and methods. We use Sammon error as a measure of the similarity of metrics. Utilizing both Particle Swarm Optimization and Genetic Algorithm, we adapted a method for the identification of a viable subset of such metrics that could solve the mentioned problem. Furthermore, we experiment with our approach on 800 projects coming from GitHub and validate the results on 200 projects. With the proposed method we got optimal subsets of software engineering metrics. These subsets gave us low values of Sammon error at more than 70% at class and method levels on a validation dataset.

Exergy-based ecological network analysis for building and community energy systems

Although buildings are transitioning towards complex, dynamic, and interconnected systems, traditional engineering metrics that dominate today don't capture several important whole-network properties. To address this, this paper adopts ecological network analysis (ENA), which has numerous successes for natural ecosystems and socio-technical systems but has yet to be applied for buildings. After translating ENA into comprehensive mathematical models for engineering applications, the novel ENA method is demonstrated with building and community energy systems. For the models to suit building energy systems, which have multiple energy types and non-negligible dynamics, ENA is formulated on an exergy basis, with dynamic flows and balances (i.e., time-varying storages). To demonstrate the proposed approach, ENA is used to redesign the heating and cooling systems for an office building and data center, coupling the buildings together via ambient-loop district energy. The models are implemented using the equation-based Modelica language. Results indicate that the ENA-guided redesign reduces the source energy by 15%. The energy consumed by the heating and cooling systems is reduced by 84% with negligible sacrifice to the thermal performance. Surprisingly, the redesign also reduced the exergy efficiency of the total system from 60% to 34% due to a greater decrease in exergy output relative to exergy input with low-exergy system designs. This indicates that ENA and other network approaches that classify system organization may outperform traditional efficiency-based metrics for building and community energy systems when whole-system perspectives are desired.

Heat transfer rate and thermal energy analysis of MHD powell-eyring fluid in a permeable medium

This study is of significant importance as it focuses on exploring the intricate dynamics of viscous dissipation over a layered stretching surface in the context of MHD free and forced convective flow influenced by Powell-Eyring fluid. The primary aim of this research is to analyze the impact of heat emission and immersion phenomena on the flow, and to achieve this, conformable transformations of similarity have been pragmatic to adapt the governing equations from a set of non-linear PDE's into ODE's. The research methodology employed in this study involves numerical analysis using a 4th order Runge-Kutta formulation, implemented through MATLAB. The study precisely examines the stimulus of different flow constraints on velocity and temperature profiles. The outcomes of this investigation are presented through graphical representations, providing a comprehensive view of the flow characteristics. Furthermore, this study goes beyond the basic analysis by presenting important engineering metrics, the Nusselt number and skin-friction coefficient, explicitly across dissimilar variables. In conclusion, the findings of this research reveal as the Eyring-Powell fluid velocity profile rises, the temperature profile decreases, while the temperature profile rises with rising radiation.

Examining human factors and ergonomics aspects in a manufacturing organisation’s metrics system: measuring up to stakeholder needs

This research examines the status of human factors and ergonomics (HF/E) metrics in the case context of product realisation in an electronics manufacturing company. Interactions with 100+ stakeholders over a five year period were thematically analysed for metrics-related views and content. A disconnect between engineering metrics and HF/E metrics was evident. Engineers and HF/E specialists expressed different understandings of the gap between the disciplines and how to generate HF/E metrics that would fit the organisation. Other emerging themes provided insight for metrics development including improving indicator relatability, considerations for communication of information, and barriers to implementation of metrics. The results led to seven recommendations to help guide practitioners in developing and refining HF/E metrics as part of an organisation’s metrics system. This macroergonomic case study provides key points for consideration when developing HF/E focussed metrics to support organisations being more proactive with HF/E in work system design. Practitioner summary: Metrics’ presence, stakeholder views on metrics, and metrics-related content in a case organisation were thematically analysed with a macroergonomics focus. Human factors and ergonomics metrics (HF/E) were disconnected from engineering metrics thus limiting the design team’s ability to handle human factors in design. Factors influencing HF/E metrics creation and integration were identified, resulting in seven recommendations for developing HF/E metrics.

Do We Need Exotic Models? Engineering Metrics to Enable Green Machine Learning from Tackling Accuracy-Energy Trade-offs

Machine learning (ML) has been shown to bypass key limitations of traditional methods (i.e., physical testing and numerical simulations) and hence presents itself as an attractive technology in engineering. While the integration of ML brings exciting opportunities, it also introduces unique challenges. One such challenge is related to the heavy reliance of ML on large datasets and computing facilities – for algorithm development, training, deployment, and storage. To realize Green ML (GML), this paper argues that ML users are to be cognizant of the hidden costs of energy consumption and subsequent carbon emissions arising from ML modeling, and hence they are ethically bound to apply ML responsibly. In this pursuit, a series of simple and exotic ML algorithms are examined, and their performance on a relatively large dataset (∼8000 observations) is documented on five fronts; predictive performance, model size, training time, energy consumption, and generatedcarbon emissions. In addition, this work also examines the influence of algorithmic architecture, processing language, number of features, as well as dataset size on model predictivity and energy consumption. Findings from this investigation infer that a 23–99% reduction in energy consumption and carbon emissions can be attained (while maintaining a comparable level of accuracy) by adopting simple as opposed to exotic ML models. The same findings have also led to the development of two new metrics that can tie the predictivity (i.e., level of accuracy) to the amount of energy consumed per algorithm. These metrics can be used to compare model performance in a similar manner to that traditionally used to assess the accuracy of ML predictions, thereby integrating energy-based awareness as a key dimension for model comparison.

Software quality metrics in the automatic evaluation of Python introductory programming

Numerous virtual environments with automatic program evaluation have emerged to assist the teaching-learning process, allowing timely feedback. In a review of these environments, we find few studies that focus on an approach centered on refactoring: where students are strongly encouraged to refactor, improving the submitted code to also meet quality criteria. In a traditional environment, the student submits the answer and if it is dynamically correct, he goes to the next question. In this work, we propose a complementary approach based on software engineering metrics, which allow a finer evaluation of the code where the programmer, after having his dynamically correct answer, is invited and encouraged to refactor his solution towards an optimal code that also meets the software quality metrics. The work is based on source code in the Python language and shows which software quality metrics can be used with the purpose of encouraging students to refactor their code in programming fundamentals disciplines.

Resistivity Scaling Transition in Ultrathin Metal Film at Critical Thickness and Its Implication for the Transparent Conductor Applications

AbstractUnderstanding of ultrathin metal film's electrical and optical properties at sub‐10 nm thickness may provide important engineering insight on its application as a transparent conductor. Here, a rapid change is observed in the ultrathin metal film's electrical and optical scaling properties as the thickness shrinks to below a certain critical thickness dc. Below this thickness, the metal film's electrical properties are shown to be strongly influenced by the inhomogeneity of the film which can be modeled via general effective media theory by incorporating size‐effect contribution. As a result, below dc, carrier's scattering time rapidly decreases with a reduced mean free path leading to a rapid rise in resistivity. Also, the film's optical loss increases while the optical transmission plateaus below dc. As one promising application of thin metal film is transparent conductor where the film's electrical and optical properties are equally important, its maximum theoretical figure‐of‐merit is shown, which is determined at this dc serving as an important engineering metric.

Evaluating network embedding techniques’ performances in software bug prediction

Software bug prediction techniques can be very helpful in testing and code inspection. Over the past decade, network measures have been successfully used in bug prediction. Following the same intuition, recently, researchers started using network embedding techniques in bug prediction. However, existing studies only evaluated the Skip-gram and CBOW models with random walk. Considering network embedding is a fast-developing research direction, it is important to evaluate other network embedding techniques’ performances in bug prediction. Moreover, existing studies have not investigated the application and performance of network embedding in effort-aware bug prediction, which is thought to be a more realistic scenario that evaluates the cost effectiveness of bug prediction models. In this paper, we conduct an extensive empirical study to evaluate network embedding algorithms in bug prediction by utilizing and extending node2defect, a newly proposed bug prediction model that combines the embedded vectors with traditional software engineering metrics through concatenation. Experiments are conducted based on seven network embedding algorithms, two effort-aware models, and 13 open-source Java systems. Experimental results show that node2defect outperforms traditional metrics by + 14.64% in terms of MCC score, and by + 7.51% to + 16.57% in effort-aware bug prediction. More interestingly, when combined with CBS + , the embedded vectors alone can achieve the best performance. Among different network embedding algorithms, the newly proposed algorithm ProNE has the best performance.

Integrating sustainability and remanufacturing strategies by remanufacturing quality function deployment (RQFD)

The objective of this paper is to ensure remanufacturing by considering the possible design, material and process changes. A novel remanufacturing quality function deployment (RQFD) was developed to bring out the possible changes to the existing product. To accomplish the above objective, RQFD phase I (voice of customer to engineering metrics) and RQFD phase II (engineering metrics to components of case product) were developed. Based on the results, the improvements options in design, process and materials were identified. The sustainability performance for the original and modified design was identified to understand the environmental benefits achieved through the proposed method. The proposed method has been applied to brake caliper components. The practical applications of the research are expected to help the manufacturers of brake calipers to minimize negative impacts on the environment, energy conservation and natural resources and are safe for stakeholders and are economically sound.

A kind of new method of intelligent trust engineering metrics (ITEM) for application of mobile ad hoc network

PurposeThis paper aims to put forward a kind of new method of intelligent trust engineering metrics for application of mobile ad hoc network (MANET).Design/methodology/approachThe new method calculates the communication trust by using the number of data packets among the nodes of MANET, predicts intelligently the trust and calculates the comprehensive trust based on the historical trust; then calculates the energy trust based on the residual energy of the nodes of MANET, calculates the direct trust based on the communication trust and energy trust. The new method calculates the recommendation trust based on the recommendation reliability; adopts the adaptive weighting to calculate the integrated direct trust by considering the direct trust with recommendation trust.FindingsBased on the integrated direct trust and the factor of trust propagation distance, the indirect trust among the nodes of MANET is calculated. The above process can be optimized based on the dynamic machine learning presented in this study. The advantage of the new method is its intelligent ability to discover malicious nodes.Originality/valueThe advantage of the new method is its intelligent ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and proceeds to protect the network. The authors have done the experiments based on the tool kits such as NS3, QualNet, OMNET++ and MATLAB. The experimental results show that this study’s approach can effectively avoid the attacks of malicious nodes, and more conformable to the actual engineering application of MANET.

Electrical vs Optical Cell Stimulation: A Communication Perspective

Over the recent years, an increasing amount of research effort has been directed toward the development and optimization of optical cell stimulation (OCS) techniques, while electrical cell stimulation (ECS) has been perfected. This article investigates the existing ECS and OCS methods from a communication perspective and, after summarizing the main breakthroughs in both fields, introduces a novel system model that treats the stimulation as a communication process. Additionally, we discuss the main particularities of ECS and OCS models and conduct a comparative analysis that quantifies the performance of the aforementioned methods based on suitable engineering metrics such as average latency, power consumption, transmission distance, as well as stimulation frequency, pulse width, and amplitude. Finally, future research directions emerge from the provided insightful discussion.

Shu-Ha-Ri for RE? An Agile Approach to Requirements Engineering Practitioner Maturity

An interesting phenomenon occurred a couple of months ago when I elicited thoughts and feedback on meaningful requirements engineering (RE) metrics from a few RE practitioners and peers. Project teams across industry are often expected to put forth some sort of indicators for management indicating that their company's investment in the work of writing requirements provides some sort of payoff in the end. As I began my career in RE, I pored over a study from Raytheon1 that comfortably concluded that yes, it is far better to discover lack of alignment or shared understanding in what a team is to develop early. Defects cost less to fix when the system to be released is still in its definition phases, rather than when development hours have been expended, when potential releases are being tested, and certainly before a product is released to its customers as complete. The cost of quality in the form of better practice is lower than the cost of cleanup later.

Toward robust concurrent product development across the supply chain: a risk assessment framework

ABSTRACTStrategic relationships with key suppliers are required for sustaining growth and creating competitive product development advantage. Managing key partners and suppliers who have a significant role in the design of their product’s major subsystems, and the increased risk that they introduce into the product development process is a key activity that is crucial to their successful integration. The common challenges faced by product integrators and suppliers during concurrent product development highlight the need for framework that allows integrators and suppliers to effectively design systems and subsystems concurrently and robustly. In this work, metrics are developed to quantitatively assess risk from supplier interactions. This is accomplished by quantifying the relative importance, the degree of customisation and the degree of coupling at the level of the engineering metrics, of the individual supplier subsystems and at the project-level. With these metrics, Intra-Supplier, Inter-Supplier and Inter-Projects risks can be assessed. The detailed development of these metrics, as well as their application to the development of a monochrome multi-functional printer to demonstrate the utility of the assessment framework is presented in this paper.

Design a knowledge base system (KBOSR) for enhancing software reusability

As a result of the development of software engineering , especially with regard to improving the software systems within a short time to enhance the development process and reduce cost as much as possible . Software reusability is one of the most important software engineering metrics which aim to reduce the effort and time in the development process . It is the process of using pre-existing software components for implementing and updating new software systems , thereby reducing effort , time and cost in addition to increasing the quality and productivity of software systems . Many factors affect the reusability of a software , of these factors , coupling and cohesion are used in this search for the aforementioned purpose . Reusability of classes is determined based on coupling and cohesion results . Cohesion measures the intra-dependability of functions and variables inside the class , while coupling measures the inter-dependability among classes in the same package . The proposed expert system also used for computing coupling , cohesion and reusability status , and produce suggestions to enhance the reusability status of unreusable classes by offering intelligence advices which depending on the weakness of the tested codes . Proposed knowledge Base System (KBOSR) contains (61) rules to provide and demonstrate why the items are not reusable by displaying code weaknesses , and to provide advice, guidance and modifications to the code for making the product reusable and displaying these guidelines and instructions on user interfaces in a clear and easy-to-understand way, KBOSR is the first knowledge base system to determine the status of reuse and improve it . The knowledge base was tested using 13 classes were distributed in two packages , first package contains 5 classes , second package contains 8 classes , coupling and cohesion values of classes are computed . Coupling , cohesion and reusability status of these classes are determined , then , by proposed expert system . Finally , suggestions of improvement of unresable classes are displayed . The results of testing process have proved the strength and validity of the proposed knowledge base in determining reusability status of the software components , and the accuracy of the advices that were suggested , these advices are practically applied on the codes of interest and the modified codes proved to be highly reusable .