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In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

Aluminum foam-filled thin-walled unit structures have received much attention for their excellent energy absorption properties. To improve the energy absorption effect of car energy absorption box under axial compression, this paper optimizes the fiber lay-up sequence, fiber angle and aluminum foam density of aluminum foam filled carbon fiber reinforced plastic (CFRP) thin-walled square tubes.

Design of sample points required to construct the proxy model using design of experiments (DOE) method, and the data sample points of different models are obtained through Abaqus simulation and test. A double high-precision proxy model with the maximum specific energy absorption (SEA) and the minimum initial peak crash force (PCF) as the evaluation index is constructed based on the response surface function method. The NSGA-II multi-objective genetic algorithm was used to optimize the design parameters and obtain the optimal solution for the Pareto front, and the results were verified by using the multi-objective optimization toolbox in design-expert.

The results show that the optimal solution to the multi-objective optimization problem with the inclusion of the lay-up sequence is ρ = 0.5g/cm3 for a fiber lay-up angle varying in the range ±15–90° and an aluminum foam density varying in the range 0.2g/cm3-0.5g/cm3, with a lay-up method of [±87°/±16°/±15°/±89°]. The two optimization methods correspond to SEA and PCF errors of 2.109% and 4.1828%, respectively. The optimized SEA value is 18.2 J/g and the PCF value is 18,230 N. The optimized design reduces the peak impact force and increases the specific energy absorption, which improves the energy absorption effect of thin-walled energy-absorbing boxes for automobiles.

In this paper, the impact resistance of CFRP thin-walled square tubes filled with aluminum foam is optimized. Based on numerical simulations and experiments to obtain the sample point data for constructing the dual-agent model, we investigate the effect of filling with different densities of aluminum foam under the simultaneous change of fiber lay-up angle and order on its mechanical properties in this process, and carry out the multi-objective optimization design with NSGA-II algorithm.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

The additive manufacturing process, such as fused filament fabrication based on material extrusion, fabricates the samples layer-by-layer. The various parameters in the process significantly affect the dimensions, structure and mechanical properties of the fabricated parts. The purpose of this paper is to investigate the surface and mechanical properties that can affect the contact characteristics with other materials during tribological tests.

The investigation of 3D-printed Polyetheretherketone (PEEK) includes the measurement of dimensions, microhardness, surface roughness, surface energy and tensile strength to define material characteristics. The crystallinity is measured using an X-ray diffractometer to understand the hardness behaviour.

The printing parameters affect its surface roughness, hardness and crystallinity. This change in parameters such as layer thickness and infill density impacts mechanical properties such as hardness and surface roughness, which will influence the contact mechanism with the counter body during any tribological test. The change in a single parameter during the sample fabrication and the change in the surface and mechanical properties are observed.

The material cost plays an important role in conducting numerous destructive tests, which is a major limitation to conducting parameter optimisation by varying more parameters. The study is limited to the as-fabricated samples rather than finished samples and without any heat treatment. Achieving optimal parameters is integral to the success of additive manufacturing, ensuring the production of components with consistent performance.

The study aims at the application of 3D-printed PEEK for bush or journal bearings that can be directly used in practice. The mechanical properties discussed in this paper can fill the gap between theory and practice.

The research provides all fundamental properties, including the printing parameters and their effect on the dimensions and surface structure, which are required to understand the material and its use. The results are consistent as at least four samples were tested for tribological behaviour. The conclusion is updated as per suggestions.

The study outlines the relationship between the change in layer thickness and infill density with changes in surface energy, surface roughness, hardness and tensile strength. The deformation and adhesion during the friction test depend on these properties.

In recent decades, advancements in biosensors technology have made fluorescent biosensor pivotal for biomolecular recognition. This paper aims to provide an in-depth analysis of polymerase chain reaction (PCR) fluorescent biosensor detection technology for identifying Escherichia coli (E. coli), setting the stage for future developments in the field.

The review of literature on PCR fluorescent biosensor detection technology for E. coli over the past decades includes discussions on traditional biological fluorescent detection, quantitative PCR fluorescent detection and digital fluorescent detection technology.

Advancements in fluorescent biosensor technology enable precise measurement of fluorescent signals, and when integrated with microfluidic technology, produce compact, reagent-efficient digital sensor devices.

This paper provides a comprehensive review of recent fluorescent detection technology for pathogenic E. coli, assessing method efficiencies and offering insights to advance the field.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

This study aims to examine the cutting performance of a coated carbide tool during the boring of 1Cr17Ni2 martensitic stainless steel, with a focus on how the tool’s structural parameters, particularly the nose radius, affect the wear patterns, wear volume and lifetime of the cutting tool, and related mechanisms.

A full factorial boring experiment with three factors at two levels was conducted to analyze systematically the impact of cutting parameters on the tool wear behavior. The evolution of tool wear over the machining time was recorded, and the influences of the cutting parameters and nose radius on wear behavior of the tool were examined.

The results show that higher cutting parameters lead to significant wear or plastic deformation at the tool nose. When the cutting depth is less than the nose radius, the tool wear tends to be minimized. Larger nose radius tools have weaker chip-breaking but greater strength and wear resistance. Higher cutting parameters reduce wear for the tools with larger nose radius, maintaining their integrity. Wear mechanisms are primarily abrasive, adhesive and diffusion wear. Furthermore, the full-factorial analysis of variance revealed that for the tool with r_ε = 0.4 mm and 0.8 mm, the factors contributing the most to tool wear were cutting speed (38.76%) and cutting depth (86.43%), respectively.

This study is of great significance for selection of cutting tools and cutting parameters for boring 1Cr17Ni2 martensitic stainless-steel parts.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0266/

This study aims to investigate the impact of implementing various robotic systems in logistics and supply chain management on corporate performance in Jordanian manufacturing companies, focusing on environmental, operational, economic, and social dimensions.

Using a quantitative approach, data was collected through a survey questionnaire to measure the relationship between robotic systems and several performance dimensions. Various established constructs were employed, and the structural relationships were analyzed using partial least squares structural equation modeling (PLS-SEM) to assess the complex interdependencies within the model.

The results of this study indicate that the adoption of robotic systems has a positive influence on the environmental, operational, economic, and social performance of Jordanian manufacturing companies. In contrast to prior research that revealed inconsistencies in the social dimension, our findings align with previous studies highlighting the benefits of robotics in logistics and supply chain management. However, it’s worth noting that this study did not uncover similar inconsistencies, particularly in terms of the impact on social performance.

The study provides valuable insights for manufacturing companies considering the implementation of robotic systems, highlighting the need to evaluate the environmental, operational, social, and economic consequences. This understanding can help organizations make informed decisions to leverage the benefits of robotics for sustainable growth.

This study contributes to the growing literature on robotics in logistics and supply chain management, specifically focusing on the unique context of Jordanian manufacturing companies. By examining the multifaceted impact of robotic systems, this study extends the understanding of the role of technology in enhancing corporate performance.

This study aims to investigate the machining performance of CVD-coated carbide tools by considering most crucial machinability aspects: cutting force, tool life, surface roughness and chip morphology in high-speed hard turning of AISI 4340 alloy steel under a sustainable minimum quantity lubrication (MQL) environment.

The purpose of this study is to analyze the performance of coated carbide tools under MQL environment therefore, machining tests were performed in accordance with the Taguchi L9 orthogonal array, accommodating the three crucial machining parameters such as cutting speed (V = 300–400 m/min), feed rate (F = 0.1–0.2 mm/rev) and depth of cut (DOC = 0.2–0.4 mm). The measured or calculated values obtained in each experimental run were validated for normality assumptions before drawing any statistical inferences. Taguchi signal-to-noise (S/N) ratio and analysis of variance methodologies were used to examine the effect of machining variables on the performance outcomes.

The quantitative analysis revealed that the depth of cut exerted the most significant influence on cutting force, with a contributing rate of 60.72%. Cutting speed was identified as the primary variable affecting the tool life, exhibiting a 47.58% contribution, while feed rate had the most dominating impact on surface roughness, with an overall contributing rate of 89.95%. The lowest cutting force (184.55 N) and the longest tool life (7.10 min) were achieved with low machining parameters at V = 300 m/min, F = 0.1 mm/rev, DOC = 0.2 mm. Conversely, the lowest surface roughness (496 nm) was achieved with high cutting speed, low feed rate and moderate depth of cut at V = 400 m/min, F = 0.1 mm/rev and DOC = 0.3 mm. Moreover, the microscopic examination of the chips revealed a serrated shape formation under all machining conditions. However, the degree of serration increased with an incremental raise with cutting speed and feed rate.

The study is limited to study the effect of machining parameters within the stated range of cutting speed, feed rate and depth of cut as well as other parameters.

Practitioners may consider to adopt this machining technique to create more sustainable working environment as well as eliminate the disposal cost of the used metal cutting fluid.

By applying this machining technique, diseases caused by metal cutting fluid to the mechanist will be significantly reduced, therefore creating better lifestyles.

Hard turning is commonly carried out with advanced cutting tools such as ceramics, cubic boron nitride and polycrystalline cubic boron nitride to attain exceptional surface finish. However, the high cost of these tools necessitates exploration of alternative approaches. Therefore, this study investigates the potential of using cost-effective, multilayer-coated carbide tools under MQL conditions to achieve comparable surface quality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0013/

In this paper, the authors aim to study the relationship between hydrogen embrittlement (HE) susceptibility and cathodic current density applied on the X70 steel girth welds.

The HE susceptibility of X70 steel girth welds were investigated through slow strain rate tensile test and observed and analyzed by optical microscope and scanning electron microscope methods.

The results show that HE susceptibility of X70 girth weld was basically unchanged with increasing of ion concentration while gradually increased and maintain at a specific value with the increase of cathodic current density. As for same ion content, a dense calcareous deposit layer generated on the sample surface in soil simulation solution with Ca2+ and Mg2+ resulted a decreased HE susceptibility while the porous calcareous deposit layer resulted a increased HE susceptibility.

A logistic regression model was established to describe the correlation between HE index and the cathodic current density.