Designs

With the qualitative development of DC microgrids, the usage of different loads with unique conditions and features is now possible in electric power grids. Due to the negative impedance features of some loads, which are called constant power loads (CPLs), the control of DC power converters faces huge challenges from a stability point of view. Despite the significant advances in semiconductors, there is no upgrade in the control of gate drivers to exploit all potential of power electronic systems. In this paper, quantum computations are incorporated into artificial intelligence (AI) to stabilize a full-bridge (FB) DC-DC boost converter feeding CPL. Aiming to improve the bus voltage stabilization of the FB DC-DC boost converter, a quantum deep reinforcement learning (QDRL) control methodology is developed. By defining a reward function according to the specification of the FB power converter, the desired performance and control objectives are fulfilled. The main task of QDRL is to adjust the control coefficients embedded in the feedback controller to suppress the negative impedance effect resulting from deploying the CPLs. By deploying the potential advantages of quantum fundamentals, the deep reinforcement learning improved by quantum specifications will not only enhance the performance of the DRL algorithm on conventional processes but also advance related research areas such as quantum computing and AI. Unlike the basic quantum theory, which requires real quantum hardware, QDRL can be executed on classic computers. To examine the feasibility of the QDRL scheme, hardware-in-the-loop (HiL) examinations are conducted using the OPAL-RT. The comparison of the proposed controller with the classic state-of-the-art methodologies reveals the superiority and feasibility of QDRL-based control schemes in both the transient and steady-state conditions to other schemes. Analysis using various performance criteria, including the integral absolute error (IAE), integral time absolute error (ITAE), mean absolute error (MAE), and root mean square error (RMSE), demonstrates the dynamic improvement of the proposed scheme over sliding mode control (approximately 50%) and proportional integral control (approximately 100%). Full article

The current prioritisation of road safety enhancement in the automotive sector is leading toward the near future implementation of Advanced Driver Assistance Systems (ADASs), aiming at the simultaneous intervention of braking and steering for impact avoidance in case of an impending collision. However, it is partially unclear how new technologies for controlling the steering will actually behave in the case of inevitable collision states; the need consequently emerges to propose and tune efficient ADAS strategies to handle the complexity of critical road scenarios. An adaptive intervention logic on braking and steering for highly automated vehicles is applied in the context of a “lane departure”, two-vehicle critical road scenario; the ADAS implementing the logic activates to minimise the injury risk for the ego vehicle’s occupants at each time step, adapting to the eventual scenario evolution consequent to actions by other road users. The performance of the adaptive logic is investigated by a software-in-the-loop approach, varying the mutual position of the involved vehicles at the beginning of the criticality and comparing the injury risk outcomes of the eventual impacts with those connected to the Autonomous Emergency Braking (AEB). The results highlight a twofold benefit from the adaptive logic application in terms of road safety: (1) it decreases the frequency of impacts compared to the AEB function; (2) in inevitable collision states, it decreases injury risk for the vehicles’ occupants down to 40% compared to the AEB. This latter condition is achieved thanks to the possibility of reaching highly eccentric impact conditions (low impact forces and occupants’ injury risk as a consequence). The obtained highlights expand the literature regarding the adaptive logic by considering a diverse critical road scenario and investigating how fine variations on the vehicles’ mutual position at the beginning of the criticality reflect on the injury outcomes for different types of intervention logic. Full article

Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research. Full article

Breast cancer poses the greatest long-term health risk to women worldwide, in both industrialized and developing nations. Early detection of breast cancer allows for treatment to begin before the disease has a chance to spread to other parts of the body. The Internet of Things (IoT) allows for automated analysis and classification of medical pictures, allowing for quicker and more effective data processing. Nevertheless, Fog computing principles should be used instead of Cloud computing concepts alone to provide rapid responses while still meeting the requirements for low latency, energy consumption, security, and privacy. In this paper, we present CanDiag, an approach to cancer diagnosis based on Transfer Deep Learning (TDL) that makes use of Fog computing. This paper details an automated, real-time approach to diagnosing breast cancer using deep learning (DL) and mammography pictures from the Mammographic Image Analysis Society (MIAS) library. To obtain better prediction results, transfer learning (TL) techniques such as GoogleNet, ResNet50, ResNet101, InceptionV3, AlexNet, VGG16, and VGG19 were combined with the well-known DL approach of the convolutional neural network (CNN). The feature reduction technique principal component analysis (PCA) and the classifier support vector machine (SVM) were also applied with these TDLs. Detailed simulations were run to assess seven performance and seven network metrics to prove the viability of the proposed approach. This study on an enormous dataset of mammography images categorized as normal and abnormal, respectively, achieved an accuracy, MCR, precision, sensitivity, specificity, f1-score, and MCC of 99.01%, 0.99%, 98.89%, 99.86%, 95.85%, 99.37%, and 97.02%, outperforming some previous studies based on mammography images. It can be shown from the trials that the inclusion of the Fog computing concepts empowers the system by reducing the load on centralized servers, increasing productivity, and maintaining the security and integrity of patient data. Full article

The gear manufacturing method is an important determinant of their performance and service life. Surface hardness and dimensional accuracy play a significant influence in determining wear and contact fatigue in gears. This study’s goal was to measure the gear profile dimensions and surface behavior of nodular cast iron made using the chill casting technique. Chill plates made of 304 stainless steel with thicknesses of 0.2, 0.4, and 0.6 mm were used to provide good surface cooling rates during the chill casting of gears performed using open molds of silica sand. Chill plates are plated onto the walls of the mold, and then the molten material is poured at 1400 °C. The obtained gears were tested using photographs, microstructures, SEM-EDX, microhardness, wear, and dimensional measurements. The thickness of the chill plate can affect the hardening process of the gear surface. Thicker chill plates result in slower cooling rates, resulting in a more homogeneous microstructure and increasing the hardness level of the hardened layer. Whereas thinner chill plates result in a faster cooling rate, which results in a higher hardness and wear resistance of the hardened layer. Reducing the thickness of the chill plate from 0.6 mm to 0.2 mm increases the cooling rate and increases the amount of diffusion that can occur. The results showed that M₇C₃ and the (FeCrC)₇C₃ matrices were formed, with an average hardness within a range of 700–994.96 HV. A chill plate with a thickness of 0.4 mm produces gear with the best accuracy and precision. Full article

This work intends to perform technical and 2E (economic & environmental) analysis for the proposed hybrid energy generating system for a part load at SRM IST at the Delhi-NCR campus, India. The investigation has been done for electricity generation and hydrogen production through renewable energy sources, mainly solar energy. It is in line with the Indian Government’s initiatives. The proposed hybrid system has to meet the electric load demand of 400 kWh/day with a peak load of 74.27 kW and hydrogen load demand of 10 kg/day with a peak demand of 1.86 kg/h. The analysis has been performed for both on-grid and off-grid conditions. As a result, optimum results have been obtained off-grid condition, with $0.408 per kWh cost of energy, $16.6 per kg cost of hydrogen, low O&M cost ($21,955 per year), a high renewable fraction (99.8%), and low greenhouse emissions (247 kg/year). In addition, sensitivity analysis has been performed between—(1) the solar PV array size & the number of battery strings, with NPC, renewable fraction & CO₂ emissions as sensitivity variables, and (2) reformer capacity & hydrogen tank capacity, with NPC as sensitivity variable. Full article

The use of stress–strain analysis in structural design or mechanical components is critical for avoiding or investigating structural failures. In the case of complicated designs, mathematical full-field stress modeling produces imprecise predictions. Experimental analysis can be used as a replacement for mathematical modeling, but with the use of currently available strain gauges, it is cumbersome and impossible in the case of moving parts. Mechanoluminescent materials transform mechanical energy into visible light and can be used as a replacement for strain gauges to monitor strain/stress. Three-dimensional printing technology has made major advances in terms of additive manufacturing. In this article, we describe a method to produce an ML 3D print. The fabricated samples are precise and versatile and satisfy the need for easy and non-destructible spatial stress analysis. A 3D printed photopolymer sample with SrAl₂O₄: Eu, Dy particle addition only to the final layers was tested, and the number of layers was optimized. It was determined that the optimal number of layers for easy detection is in the range of 10 to 20 layers. It opens the possibility for the real-time evaluation of complex uneven forces on complex parts, thus having a good potential for commercialization. Full article

While much of additive manufacturing (AM) research is focused on microstructure, material properties, and defects, there is much less research in regards to understanding how well the part coming out of the machine matches the 3D model it is based on, as well as what are the key process parameters an engineer needs to care about when they are optimizing for AM. The purpose of this study was to understand the dimensional accuracy of the electron beam powder bed fusion (EB-PBF) process using specimens of different length scales from Ti-6Al-4V. Metrology of the specimens produced was performed using fringe projection, or laser scanning, to characterize the as-built geometry. At the meso-scale, specimen geometry and hatching history play a critical role in dimensional deviation. The effect of hatching history was further witnessed at the macro-scale while also demonstrating the effects of thermal expansion in EB-PBF. These results make the case for further process optimization in terms of dimensional accuracy in order to reduce post-processing costs and flow time. Full article

Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environmentally friendly and more affordable solutions for consumers. This article discusses the integration of PV and EV in a residential system to meet the requirements of residential loads taking into account the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control model has been proposed to control the residential system. The combined PI-Fuzzy logic controller is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A rule-based energy management strategy has been proposed to control the flow of energy between the components of the residential system. The suggested energy management system (EMS) covers every scenario that might occur. Whether the EV is linked to the home or not, and also takes into account the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle (H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the conditions related to the EV. All possible scenarios are tested and validated. The simulation results show that the proposed EMS is a reliable solution that can reduce the power grid intervention. Full article

This paper discusses the interaction between students (humans) and an interaction response system (IRS) app (machine) in the teaching context and explores the interface usability and interactive experience design through the experimental method. The experiment mainly explored the differences in the use of the IRS app by learners with different learning styles. A total of 72 subjects were recruited for the experiment, of which the four learning styles (diverger, assimilator, converger, and accommodator) and the two kinds of information architecture (deep/shallow) are discussed respectively. With operating time performance and use experience as dependent variables, the relationship between variables was explored. The results of this study are as follows: in the learning style factor, subjects of the reflection and observation type responded faster to vibration; in the information architecture factor, with the deep information architecture, it took longer for page switching as more pages needed to be switched, and thus the operation performance was poor. According to the results of the two-stage experiment, the following design suggestions are proposed. It is expected that the research results can contribute to the fields of interactive experience design and teaching technology. Full article

Deep-tech startups have enormous potential to solve major societal challenges, but their failure rates are quite high (above 90%). In this respect, deep-tech systems and products have long development times and thus require substantial amounts of investment capital long before the first customer can be served. Moreover, potential investors increasingly expect that the value proposition of a deep-tech venture has a clear sustainability dimension. We therefore designed a tool that serves to develop a convincing value proposition for investors, one that is explicitly connected to the Sustainable Development Goals (SDGs) of the United Nations. We adopted a design science approach to develop and test this tool in the context of a deep-tech venture builder located in the Netherlands. The final tool arising from this study extends and integrates various existing tools with an explicit connection to the SDGs. As such, this tool enables deep-tech entrepreneurs to develop a value proposition that is more likely to attract early-stage investors. Full article

Materials based on photocurable resins and pharmaceutically active agents (APIs) are gaining interest as a composite drug delivery system. In this study, a composite of caffeine with acrylic resin was obtained using an additive manufacturing method of digital light processing (DLP) as a potential material for transdermal drug delivery. The mechanical properties of the composites and the ability to release caffeine from the resin volume in an aqueous environment were investigated. The amount of caffeine in the resulting samples before and after release was evaluated using a gravimetric method. The global thresholding method was also evaluated for its applicability in examining caffeine release from the composite. It was shown that as the caffeine content increased, the strength properties worsened and the ability to release the drug from the composite increased, which was caused by negligible interfacial interactions between the hydrophilic filler and the hydrophobic matrix. The global thresholding method resulted in similar caffeine release rate values compared to the gravimetric method but only for samples in which the caffeine was mainly located near the sample surface. The distribution of caffeine throughout the sample volume made it impossible to assess the caffeine content of the sample using global thresholding. Full article

AI text-to-image generated images have revolutionized the design process and its rapid development since 2022. Generating various iterations of perfect renders in few seconds by textually expressing the design concept. This high-potential tool has opened wide possibilities for biomaterials research-driven design. That is based on developing biomaterials for multi-scale applications in the design realm and built environment. From furniture to architectural elements to architecture. This approach to the design process has been augmented by the massive capacity of AI text-to-image models to visualize high-fidelity and innovative renders that reflect very detailed physical characteristics of the proposed biomaterials from micro to macro. However, this biomaterials research-driven design approach aided by AI text-to-image models requires criteria for evaluating the role and efficiency of employing AI image generation models in this design process. Furthermore, since biomaterials research-driven design is focused not only on design studies but also the biomaterials engineering research and process, it requires a sufficient method for protecting its novelty and copyrights. Since their emergence in late 2022, AI text-to-image models have been raising alarming ethical concerns about design authorship and designer copyrights. This requires the establishment of a referencing method to protect the copyrights of the designers of these generated renders as well as the copyrights of the authors of their training data referencing by proposing an auxiliary AI model for automatic referencing of these AI-generated images and their training data as well. Thus, the current work assesses the role of AI text-to-image models in the biomaterials research-driven design process and their methodology of operation by analyzing two case studies of biomaterials research-driven design projects performed by the authors aided by AI text-to-image models. Based on the results of this analysis, design criteria will be presented for a fair practice of AI-aided biomaterials research-driven process. Full article

The optimal design of packed columns for separation processes is strongly dependent on an accurate prediction of the fluid flows in the packing. Insufficient knowledge about the complex factors and mechanisms governing hydrodynamic effects is compensated for by empirical information. The present study fills the gap in experimental data about the liquid phase distribution in plastic Raschig Super-Ring (RSRP) packing and plastic Ralu–Flow (RF) packing. These belong to the family of widely used random packings with an open lattice structure characterized by high mass transfer efficiency and a low pressure drop. The study was performed using the liquid collection method with a device with concentric annular collection sections at the packing outlet. Large-scale liquid maldistribution in the central and peripheral zones of the packed bed were evaluated in comparison data on competing random and structured packings. The effects of the packing size and the liquid load on the radial distribution of the superficial liquid velocity, wall flow formation and the maldistribution factor were investigated and analyzed. The results contribute to deepening the knowledge about the phenomenon of large-scale liquid flow maldistribution in packed columns, as well as to design enhancement. Full article

In this paper, four machine learning algorithms are examined regarding their effectiveness in dealing with a complete lack of sensor drift values for a crucial parameter for ship performance evaluation, such as a ship’s speed through water (STW). A basic Linear Regression algorithm, a more sophisticated ensemble model (Random Forest) and two modern Recurrent Neural Networks i.e., Long Short-Term Memory (LSTM) and Neural Basis Expansion Analysis for Time Series (N-Beats) are evaluated. A computational algorithm written in python language with the use of the Darts library was developed for this scope. The results regarding the selected parameter (STW) are provided on a real- or near-to-real-time basis. The algorithms were able to estimate the speed through water in a progressive manner, with no initial values needed, making it possible to replace the complete missingness of the label data. A physical model developed with the simulation platform of Siemens Simcenter Amesim is used to calculate the ship STW under the real operating conditions of a banker ship type during a period of six months. These theoretically obtained values are used as reference values (“ground-truth” values) to evaluate the performance of each of the four machine learning algorithms examined. Full article

This paper describes a numerical study of the optimal distribution of energy between fuel cells and auxiliary energy storages in the hybrid train. Internal combustion engines (ICEs) are currently under pressure from environmental agencies due to their harmful gas emissions, and pure battery vehicles have a short range; a hybrid train powered by fuel cells, batteries, and supercapacitors can provide a viable propulsion solution. In this study, special energy management on the mountain railway with optimal power distribution and minimum hydrogen consumption is proposed. Considering the characteristics of the mountain railway, the vehicle uses recuperation of regenerative braking energy and thus charges additional power devices, and hybridization optimization gives favorable power to each power source device with a minimum consumption of hydrogen in the fuel cell. In this study, a simulation model was created in a Matlab/Simulink environment for the optimization of hybridized power systems on trains, and it can be easily modified for the hybridization of any type of train. Optimization was performed by using Sequential quadratic programming (SQP). The results show that this hybrid train topology has the ability to recover battery and supercapacitor state of charge (SOC) while meeting vehicle speed and propulsion power requirements. The effect of battery and supercapacitor parameters on power distribution and fuel consumption was also simulated. Full article

We present the design of a universal gripper based on the principle of liquid jamming. The phase change behavior of a mineral oil subjected to cooling is used for adaptive gripping and release of objects. The mineral oil is enclosed in a soft, compliant membrane molded into a spherical shape. The membrane with the liquid easily adapts to the shape of the object to be picked up. A thermoelectric cooling system is designed with a conductive spreader immersed in the oil to quickly cool the liquid held in a membrane. Once the membrane gripper conforms to the shape of the object partially enclosing it, the liquid is cooled below the phase change temperature, thus freezing the liquid and hardening the gripper around the object and effectively gripping it. We describe the design methodology of the gripper, part selection, and mechanical design. A proprietary controller developed by Venture Corporation is used for controlling the TEC module and the controller is based on a simple PID optimized using Ziegler–Nichols tuning for the P, I, and D values. The hardware is evaluated and the basic gripping functions on different odd-shaped objects are demonstrated. An invention disclosure has been submitted to the NUS IP office (ILO). Full article

The paper describes the design and implementation of a cutting fluid monitoring system, as well as the design and development of an algorithm to increase the life of the cutting fluid in the machine tool reservoir. Cutting fluids are the most common type of coolant in machining. During its use, it becomes contaminated and gradually degrades until it needs to be replaced with fresh fluid. To increase its effective service life, its parameters should be monitored at regular intervals, and corrective measures such as topping up the fluid quantity and adding inhibitors and additives should be taken if necessary. For this purpose, a conceptual design of a monitoring device was developed, and a prototype device was subsequently manufactured. The device is designed as a floating probe in the storage tank. Therefore, its shape had to be designed to accommodate multiple sensors, batteries, and electronic components while remaining floating and watertight. The designed prototype was made by additive manufacturing and placed in a cutting fluid while being measured at regular intervals. In the event of non-compliant parameters, the algorithm generated corrective actions, and the machine operator could take the required steps to significantly increase the lifetime of the cutting fluid. Full article

Design for manufacturing, assembly, and disassembly is critical in manufacturing. Failing to consider this aspect can lead to inefficient performance and material overuse, which significantly impact cost and construction time. Production with a high capability for recycling is a method to help conserve natural resources. This article is compiled with a review method and has evaluated the recent and related articles that consider design for production, design for assembly and disassembly, design for recycling and reuse, and sustainable design. This review, moreover, aims to focus more on the relationship between using a design approach for production and assembly in the ease of recycling and preservation of raw materials and reuse of materials. The survey for the design methods conducive to achieving ease of recycling is one of the crucial issues that fill the gap in the literature in this respect. Google Scholar was selected as a database, and the keywords “DFMA”, “design”, “facility of recycling”, “recycling”, “EoL”, and “product design” were considered to collect related articles. At first, 115 articles were identified, and 26 articles with a high focus on the subject were selected. Finally, nine articles were considered for final evaluation, 33% of which focused on the design approach for assembly. Many of the issues evaluated are about reducing the number of components and reducing complexity in design, materials, environmental impact, manufacturing cost and time, repair, reuse, end-of-life, remanufacturing, recycling, and non-recyclable waste. According to the mentioned materials, compiling a category of crucial information along with sustainable design indicators and approaches, as well as identifying and explaining the strategic actions of the researchers in this field, will benefit the experts and help them to obtain better insight into environmentally friendly production. This, moreover, helps to substantiate a circular economy by increasing the percentage of recycling materials and parts with various methods and reducing costs and the use of raw materials. Full article