Production Engineering Research Articles

Artificial Intelligence in the Sustainable Design and Manufacturing of Products in Civil Engineering in the Context of Industry 4.0

The implementation of smart technologies as well as artificial intelligence resulted in an increase in productivity and efficiency in production, optimization of costs, and automation of time-consuming processes. The situation is similar in the field of the production of construction products. Digitization and automation are challenges within Industry 4.0 (Construction 4.0), which are the subject and interest of several studies and discussions by experts in the field. The aim of the research is to analyze the relationships between digitization in the industry, artificial intelligence, and performance in the design, production, and use of construction products in the context of Industry 4.0 principles. The research uses primary and secondary data on the use of AI and its potential and impacts in the field of performance and efficiency in design, production, and use in civil engineering. Several statistical tools are used in the analysis, from descriptive statistics to the use of statistical tests and correlation and regression analysis. Spearman’s correlation coefficient was the primary tool for evaluating the dependence between variables. The research results point to the connection and dependence between the use of AI and digitization in individual design and production activities. Autonomous production machines, the production of ore products, and generative design represent areas of production in the construction industry, where the use of AI and digitization makes sense from the point of view of the performance of the results. Innovations and intelligent tools within the concept of Industry 4.0 (Construction 4.0) are, therefore, a prerequisite for an effective setting of design and production in this industry as well.

An elucidation of blended modeling from an industrial perspective

AbstractModel-Driven Engineering (MDE) has been widely adopted across various industrial sectors due to its ability to manage the complexity of modern engineering products. However, traditional modeling languages and tools are often limited to a single, specific concrete syntax, which poses challenges for the diverse stakeholders involved in the modeling process.. To address these limitations, the emerging field of blended modeling introduces the use of multiple concrete syntaxes, and in some cases, even multiple abstract syntaxes, for representing the same information. In this expert perspective, we present generalized, technology-agnostic concepts developed within a European research and development project focused on blended modeling. Specifically, we contribute a standardized terminology and ontology for blended modeling, along with a methodology for creating blended modeling environments. These concepts were developed through collaboration between academic and industrial partners, who aligned on the motivations and benefits of this approach. The insights gained from this project are not only relevant to blended MDE but also can be applied to traditional MDE practices.

Genome-wide association study of metabolic traits in the giant duckweed Spirodela polyrhiza.

The exceptionally high growth rate and high flavonoid content make the giant duckweed Spirodela polyrhiza (L.) Schleid. (Arales: Lemnaceae Martinov) an ideal organism for food production and metabolic engineering. To facilitate this, identification of the genetic basis underlying growth and metabolic traits is essential. Here, we analysed growth and content of 42 metabolites in 137 S. polyrhiza genotypes and characterized the genetics underpinning these traits using a genome-wide association (GWA) approach. We found that biomass positively correlated with the content of many free amino acids, including L-glutamine, L-tryptophan, and L-serine, but negatively correlated with specialized metabolites, such as flavonoids. GWA analysis showed that several candidate genes involved in processes such as photosynthesis, protein degradation, and organ development were jointly associated with multiple metabolic traits. The results suggest the above genes are suitable targets for simultaneous optimization of duckweed growth and metabolite levels. This study provides insights into the metabolic diversity of S. polyrhiza and its underlying genetic architecture, paving the way for industrial applications of this plant via targeted breeding or genetic engineering.

Metabolic Engineering for Squalene Production: Advances and Perspectives.

Squalene is a linear polyunsaturated triterpene which has multiple physiological functions including anticancer, antioxidant, and skin-care. It has been widely used in the food, medicine, and cosmetics sectors and also serves as a precursor of triterpenes and steroids. Recently, the production of squalene by microbial cell hosts has drawn much attention due to its sustainability, environmental friendliness, and great efficiency. In this review, we first introduce the recent developments in the production of squalene by employing microbial cell factories, especially yeasts. Next, we underscore the primary metabolic strategies, including the biosynthetic pathway engineering, precursor manipulation, cofactor engineering, and organelle engineering. In addition to traditional metabolic engineering strategies, we also discuss some prospective metabolic regulation approaches, including regulation of lipid synthesis, identifying and manipulating related transcription factors, dynamic regulation of the metabolic pathway, and secretion engineering of membrane-impermeable terpenoids. These approaches provide insights for further metabolic engineering of squalene and related terpenoids.

Systematic Engineering for High-level Production of β-nicotinamide mononucleotide from NAM and Ribose

Systematic Engineering for High-level Production of β-nicotinamide mononucleotide from NAM and Ribose

Scientometric Insights into Rechargeable Solid-State Battery Developments

Solid-state batteries (SSBs) offer significant improvements in safety, energy density, and cycle life over conventional lithium-ion batteries, with promising applications in electric vehicles and grid storage due to their non-flammable electrolytes and high-capacity lithium metal anodes. However, challenges such as interfacial resistance, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This study conducts a comprehensive scientometric analysis, examining 131 peer-reviewed SSB research articles from IEEE Xplore and Web of Science databases to identify key thematic areas and bibliometric patterns driving SSB advancements. Through a detailed analysis of thematic keywords and publication trends, this study uniquely identifies innovations in high-ionic-conductivity solid electrolytes and advanced cathode materials, providing actionable insights into the persistent challenges of interfacial engineering and scalable production, which are critical to SSB commercialization. The findings offer a roadmap for targeted research and strategic investments by researchers and industry stakeholders, addressing gaps in long-term stability, scalable production, and high-performance interface optimization that are currently hindering widespread SSB adoption. The study reveals key advances in electrolyte interface stability and ion transport mechanisms, identifying how solid-state electrolyte modifications and cathode coating methods improve charge cycling and reduce dendrite formation, particularly for high-energy-density applications. By mapping publication growth and clustering research themes, this study highlights high-impact areas such as cycling stability and ionic conductivity. The insights from this analysis guide researchers toward impactful areas, such as electrolyte optimization and scalable production, and provide industry leaders with strategies for accelerating SSB commercialization to extend electric vehicle range, enhance grid storage, and improve overall energy efficiency.

Integrating Robotics in Bioprinting: Advancements in 3D-Printed Tissue and Organ Engineering

The integration of robotics in bioprinting is revolutionizing the field of tissue and organ engineering, enabling unprecedented precision, scalability, and complexity in 3D-printed biological structures. This research explores the advancements brought about by robotic systems in bioprinting processes, focusing on their role in enhancing the fabrication of tissues and organs with intricate architectures and functional properties. Key areas of investigation include robotic-assisted multi-material deposition, real-time process monitoring, and adaptive printing techniques that ensure high fidelity and cell viability. The study also examines the incorporation of robotics into scalable bioprinting workflows for large-scale tissue engineering and transplantable organ production. Ethical considerations, such as regulatory challenges and equitable access, are addressed to highlight the societal implications of these innovations. By bridging robotics, bioengineering, and regenerative medicine, this research underscores the transformative potential of robotic-assisted bioprinting in addressing critical healthcare challenges, including organ shortages and personalized medicine. With advancements in precision and adaptability, robotic systems are poised to reshape the future of bioprinting, paving the way for breakthroughs in tissue engineering and regenerative therapies.

Advancements in Microbial Cell Engineering for Benzylisoquinoline Alkaloid Production.

Benzylisoquinoline alkaloids (BIAs) are a class of natural compounds found in plants of the Ranunculaceae family, known for their diverse pharmacological activities. However, the extraction yields of BIAs from plants are limited, and the cost of chemical synthesis is prohibitively high. Recent advancements in systems metabolic engineering and genomics have made it feasible to use microbes as bioreactors for BIAs production. This review explores recent progress in enhancing the production and yields of BIAs in two microbial systems: Escherichia coli and Saccharomyces cerevisiae. It covers various BIAs, including (S)-reticuline, morphinane, protoberberine, and aporphine alkaloids. The review provides strategies and technologies for BIAs synthesis, analyzes current challenges in BIAs research, and offers recommendations for future research directions.

Revolutionizing smart textiles: a comprehensive review of conductive ink printing for sustainable and adaptive wearable applications

Purpose The demand of species monitoring for the benefit of various sectors such as industrial, medicinal and ecological has surged rapidly in the recent past. Therefore, the purpose of this paper is to articulate the major developments in synthesizing conductive inks for the structurization of miniaturized as well as disposable or reusable electrochemical equipments. Design/methodology/approach This section is not applicable to a review paper. Findings Numerous times the need for the use point becomes significant for achieving accurate as well as swift quantification. As an alternate, the wearable and effectual reuseable electrochemical sensors are being practiced. The technique of fabricating devices using conductive inks encompasses novelty, as it provides flexibility in designing the electrodes. The increase in the popularity of inks development is governed by its features of simplicity, reduced cost and waste generation, high production and eco-friendly engineering procedures. Further, the electrochemistry aspects of conductive inks highlighting the importance of their compounds and binders has also been discussed emphasizing on the conductive materials. Originality/value This paper is an original review work. This paper will be helpful for manufacturers/researchers from smart wearable textile sector in envisaging innovative developing techniques of sensors as well as biosensors through conductive inks.

Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel

Abstract This article mainly scrutinizes the heat transfer and flow characteristics of a mixed convection ternary hybrid nanofluid in a porous microchannel considering the catalytic chemical reaction and nonuniform heat absorption/generation. Using appropriate similarity transformations, the modeled equations are converted into reduced ones and then solved via the Runge–Kutta–Fehlberg 4th/5th order method. To strengthen this analysis, the convection mechanism has been deployed. The effect of pertinent physical parameters on the fluid motion and thermal field is displayed, including some important engineering variables like the Nusselt number, Sherwood number, and drag force. The novel outcomes display that the flow reduces with porous permeability and nanoparticle volume fraction. The temperature of the nanofluid improves with nonuniform heat absorption/generation. The concentration decreases in the presence of both homogeneous and heterogeneous reaction intensities. The heat transfer rate enhances for the Eckert number, and a similar influence on the mass transfer rate is noticed for homogeneous reaction parameters. Further, the drag force declines for the Grashof number. The outcomes show that, in all cases, the ternary hybrid nanofluid shows a greater impact than the nanofluid. The attained findings represent applications in the era of cooling and heating systems, thermal engineering, and energy production.

Genetic Engineering in Bacteria, Fungi, and Oomycetes, Taking Advantage of CRISPR

Genetic engineering has revolutionized our ability to modify microorganisms for various applications in agriculture, medicine, and industry. This review examines recent advances in genetic engineering techniques for bacteria, fungi, and oomycetes, with a focus on CRISPR-Cas systems. In bacteria, CRISPR-Cas9 has enabled precise genome editing, enhancing applications in antibiotic production and metabolic engineering. For fungi, despite challenges associated with their complex cell structures, CRISPR/Cas9 has advanced the production of enzymes and secondary metabolites. In oomycetes, significant plant pathogens, modified Agrobacterium-mediated transformation, and CRISPR/Cas12a have contributed to developing disease-resistant crops. This review provides a comparative analysis of genetic engineering efficiencies across these microorganisms and addresses ethical and regulatory considerations. Future research directions include refining genetic tools to improve efficiency and expand applicability in non-model organisms. This comprehensive overview highlights the transformative potential of genetic engineering in microbiology and its implications for addressing global challenges in agriculture, medicine, and biotechnology.

Circinus: An AI-Based Technical Description Plotting

Abstract. The digitization of maps poses a significant challenge for GIS operators and engineers, particularly in encoding bearings and distances into GIS software. This study, titled “Circinus: An AI-Based Technical Description Plotting,” represents a substantial advancement in the digitization process of land records. Leveraging artificial intelligence, this application enhances the efficiency of digitizing land survey maps, potentially surpassing the laborious manual input required for geographic information. By expediting the encoding process of scanned maps, the application enhances the accessibility and availability of digital land records. This advancement holds considerable benefits for effective land asset planning and improved financial operations, particularly benefiting prospective homebuyers and landowners. The tool offers streamlined encoding through its semi-automated features, facilitating ease of use. Additionally, its optimization features have the potential to significantly enhance the productivity of engineers and GIS operators involved in encoding scanned land survey maps.

In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method

Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media.

Genetic Engineering Approaches for the Microbial Production of Vanillin.

Vanilla flavour is widely used in various industries and is the most broadly used flavouring agent in the food industry. The demand for this flavour is, therefore, extremely high, yet vanilla bean extracts can only meet about 1% of the overall demand. Vanillin, the main constituent of vanilla flavour, can easily be obtained through chemical synthesis. Nonetheless, consumer demands for natural products and environmentally friendly industrial processes drive the development of biotechnological approaches for its production. Some microorganisms can naturally produce vanillin when fed with various substrates, including eugenol, isoeugenol, and ferulic acid. The characterisation of the genes and enzymes involved in these bioconversion pathways, as well as progress in the understanding of vanillin biosynthesis in Vanilla orchids, allowed the development of genetic engineering and synthetic biology approaches to increase vanillin production in naturally vanillin-producing microorganisms, or to implement novel vanillin biosynthetic pathways in microbial chassis. This review summarises and discusses these genetic engineering and synthetic biology approaches for the microbial production of vanillin.

Natural Antimicrobial Agents from Algae: Current Advances and Future Directions.

Infectious diseases have significantly shaped human history, leading to significant advancements in medical science. The discovery and development of antibiotics represented a critical breakthrough, but the rise of antibiotic-resistant pathogens now presents a serious global health threat. Due to the limitations of current synthetic antimicrobials, such as toxicity and environmental concerns, it is essential to explore alternative solutions. Algae, particularly microalgae and cyanobacteria, have emerged as promising sources of bioactive antimicrobial compounds. This review provides a comprehensive analysis of the antimicrobial properties of algal-derived compounds, including polysaccharides, fatty acids, and phenols, which have shown effectiveness against multi-drug-resistant bacteria. A co-occurrence bibliometric analysis using VOSviewer highlighted five key research clusters: antibiotic resistance, algal extracts, biosynthesis, water treatment, and novel pharmacological compounds. Furthermore, the primary mechanisms of action of these bioactive compounds, such as the inhibition of protein synthesis and cell membrane disruption, were identified, demonstrating their potential against both common and multi-resistant pathogens. Future research should prioritize optimizing algal biomass production, utilizing genetic and metabolic engineering, and creating innovative delivery systems to enhance the efficient production of bioactive compounds.

Enhancing corrosion resistance of Al alloy sheets with potential gradient by arc-sprayed Zn coatings

To improve the corrosion resistance and wide the application of Al alloy sheet in the engineering production, a corrosion potential gradient along the thickness of the Al alloy sheet was fabricated using the sprayed Zn layer. The process of arc spraying was employed to obtain the Zn coating with optimizing the parameters. Electrochemical testing was conducted on the surface of Al alloy sprayed with Zn layers at different depths. As the depth in the thickness direction increases, the content of Zn alloy elements gradually decreases and the potential gradually increases. This exhibits a gradual decrease in corrosion tendency. The results of electrochemical impedance spectra (EIS) revealed the existence of pitting corrosion on the surface of the Al alloy. This even extended to the underlying substrate material. In contrast, the Zn coating, when exposed to a corrosive environment, demonstrated the beneficial cathodic effect as a sacrificial anode and the protective function of a passivating film, thus mitigating corrosion. The gradual increase in charge transfer resistance in the impedance spectrum suggested that the corrosion resistance of the sample is increasingly improved. Therefore, it exhibited remarkable resistance to corrosion. Additionally, the SEM surface morphology and XRD pattern were utilized to further explain the corrosion mechanism of the arc-sprayed Zn coating. The Zn coating can provide sacrificial protection to the Al layer due to its higher negative potential and localized corrosion reactivity. This is attributed to the micro-galvanic couples formed between the Zn and Al layer, thus increasing the corrosion resistance of the sheets.

Performance investigation on a downdraft gasifier using corn waste as a solid biomass and co-feeding with hand gloves and used facemask waste

In India, an abundant source of corn waste is scattered throughout the country, ensure a consistent feedstock supply that can be easily converted into valuable products. Corncob waste, as a solid biomass, has significant potential energy content and can be converted through thermo-chemical processes like gasification. The co-gasification process is a viable solution for managing non-decomposing wastes, such as hand gloves and used face masks, due to their widespread use in domestic and industrial applications. Since hand gloves and used face masks are also difficult to recycle, so that the present investigation aims to utilize the co-gasification process to convert these materials into syngas.A downdraft gasifier, a fixed-bed closed type with a capacity of 5 kW for power generation, was used for the experimental work. The experiment involved using 90% corncob waste co-fed with 5% hand gloves and 5% used face masks. The performance characteristics, including gas composition, product gas composition, char yield, and tar yield, were analyzed. Operating parameters, such as reaction temperature, were varied from 700 to 900 °C, with the equivalence ratio ranging from 0.07 to 0.27.Increasing the reaction temperature led to an increase in the CO and H₂ volume percentages of the producer gas, up to 13.1 vol%, while significantly decreasing the CO₂ amount from 24.6 vol% to 19.3 vol%. CH₄ gas slightly increased from 15.8 vol% at 700 °C to 19.8 vol% at 800 °C but decreased to 19.0 vol% when the reaction temperature was further increased to 900 °C. As a result of the increase in combustible gases, the higher heating value (HHV) of the producer gas also increased with temperature. It was concluded that carbon monoxide (CO) and hydrogen (H₂) yields in the syngas increased at 900 °C, while carbon dioxide (CO₂) and methane (CH₄) decreased. However, further research is needed, especially with pilot-scale autothermal gasifier plants, to investigate the impact of adding hand gloves and used masks on the operations of such gasifiers. The produced green H2 were used an alternate fuel in diesel engine for power production in a localized area such as hospitals, industries, etc., or an effective fuel for fuel cell too.

PdO-decorated ZnWO4 heterojunctions: one-pot approach and quick solar-light-driven photocatalytic reduction of nitrobenzene with excellent recyclability

Aniline (AN) is an omnipresent raw material used to make many products in chemical engineering. As a result, the photocatalytic reduction of nitrobenzene (NB) has gained attention as an eco-friendly technology for producing aniline. This paper presents an environmentally friendly (sol-gel/calcination) approach to developing mesoporous Zinc tungstate (ZnWO4) nanostructures modified with 0.5, 1.0, 1.5, and 2.0 wt % of narrow bandgap (Eg) PdO nanoparticles (NPs). Analyzing the produced heterostructures showed a mesoporous texture and improved its optical properties after adding PdO. Adding 1.5 wt% PdO to the ZnWO4 nanocrystals significantly reduced the Eg from 3.27 to 2.21 eV, indicating improved visible light harvesting. The produced nanocomposite was applied in the photoconversion of NB into useful AN as a model in chemical industries. The optimal dose of 1.5 wt% PdO-ZWO (1.6 g/L) led to a full photoreduction of NB to AN in 30 minutes, with high reusability and recycling due to the good mobility and efficient separation of generated charges. The photoconversion mechanism demonstrated that the heterojunction between PdO and ZWO permits Step (S)-scheme charge transfer. This study suggests the usage of sustainable photocatalysts for chemical industries.

Machine Learning-Driven Analysis of Heat Transfer in Chemically Reactive Fluid Flow Considering Soret-Dufour Effects

ObjectiveThe study investigates tangent hyperbolic nanofluid flow across an extended media, emphasizing magnetohydrodynamic influences, mixed convection, and nanoparticle transport with the help of an innovative and advanced technique. MethodologyIt employs a sophisticated Gaussian Neural Network and Hybrid Cuckoo Search to mimic complex fluid dynamics accurately, ensuring efficient computation and solution precision. By transforming the governing system of nonlinear partial differential equations into ordinary differential equations via similarity transformation, the methodology attains computational efficiency and solution precision. Core FindingsStatistical measures confirm the model's resilience, indicating absolute error variations from E−05 to E−12 and mean squared errors continuously between E−02 − E−07, highlighting the reliability of the model, while the Error in Nash-Sutcliffe Efficiency values fall within the interval E−05 − E−15. The Theil's Inequality Coefficient values lie in the range of E−01 − 10−07. Future Direction and ApplicationsThe findings underscore the model's capacity to improve thermal management, energy efficiency, and process dependability in sectors like chemical processing, environmental engineering, and sustainable energy production. This study positions the Gaussian Neural Networks framework as a robust computational instrument, providing a foundation for subsequent investigations into intricate geometries, multi-physics phenomena, and magnetohydrodynamic systems for novel technology applications.

Critical machining parameters for large aircraft casing production considering the influence of clamping systems

AbstractIn recent years, there has been a notable initiation or acceleration in the production of new aeronautical engines. This surge is coupled with a global commitment to reducing emissions and mitigating noise generated by gas turbines used in aeronautical applications. A critical component in this context is the aeronautical casing. The manufacturing process is intricate and time-consuming, compounded by the complexity of the clamping system, which poses a significant challenge to productivity. This study undertakes a comprehensive analysis of the critical variables essential for the optimal manufacturing of a zero-defects large-scale casing. The examination encompasses design criteria, raw materials, constraints related to clamping systems and tooling, and process monitoring. Ultimately, the exploration extends to the examination of more sustainable manufacturing techniques. To facilitate this, a case study involving the fabrication of a life-size aeronautical casing is undertaken.