Search Results (13,944)

Three-dimensional trench electrode silicon detectors play an important role in particle physics research, nuclear radiation detection, and other fields. A novel polysilicon-fill-strengthened etch-through 3D trench electrode detector is proposed to address the shortcomings of traditional 3D trench electrode silicon detectors; for example, the distribution of non-uniform electric fields, asymmetric electric potential, and dead zone. The physical properties of the detector have been extensively and systematically studied. This study simulated the electric field, potential, electron concentration distribution, complete depletion voltage, leakage current, capacitance, transient current induced by incident particles, and weighting field distribution of the detector. It also systematically studied and analyzed the electrical characteristics of the detector. Compared to traditional 3D trench electrode silicon detectors, this new detector adopts a manufacturing process of double-side etching technology and double-side filling technology, which results in a more sensitive detector volume and higher electric field uniformity. In addition, the size of the detector unit is 120 µm × 120 µm × 340 µm; the structure has a small fully depleted voltage, reaching a fully depleted state at around 1.4 V, with a saturation leakage current of approximately $4.8\times {10}^{-10}\mathrm{A}$, and a geometric capacitance of about 99 fF. Full article

To ensure the sustainability of alloy-based strategies, both compositional design and processing routes must be simplified. Metal additive manufacturing (AM), with its exceptionally rapid, non-equilibrium solidification, offers a unique platform to produce tailored microstructures in simple alloys that deliver superior mechanical properties. In this study, we employ laser powder bed fusion (LPBF) to fabricate 1080 plain carbon steel, a binary alloy comprising only iron and carbon. Deviating from conventional process optimization focusing primarily on density, we optimize LPBF parameters for mechanical performance. We systematically varied key parameters (laser power and scan speed) to produce batches of tensile specimens, which were then evaluated on a high-throughput mechanical testing platform (HTP). Using response surface methodology (RSM), we developed predictive models correlating these parameters with yield strength (YS) and elongation. The RSM models identified optimal and suboptimal parameter sets. Specimens printed under the predicted optimal conditions achieved YS of 1543.5 MPa and elongation of 7.58%, closely matching RSM predictions (1595.3 MPa and 8.32%) with deviations of −3.25% and −8.89% for YS and elongation, respectively, thus validating model accuracy. Comprehensive microstructural characterization, including metallographic analysis and fracture surface examination, revealed the microstructural origins of performance differences and the underlying strengthening mechanisms. This methodology enables rapid evaluation and optimization of LPBF parameters for 1080 carbon steel and can be generalized as an efficient framework for robust LPBF process development. Full article

Organoids allow to model healthy and diseased human tissues. and have applications in developmental biology, drug discovery, and cell therapy. Traditionally cultured in immersion/suspension, organoids face issues like lack of standardization, fusion, hypoxia-induced necrosis, continuous agitation, and high media volume requirements. To address these issues, we developed an air–liquid interface (ALi) technology for culturing organoids, termed AirLiwell. It uses non-adhesive microwells for generating and maintaining individualized organoids on an air–liquid interface. This method ensures high standardization, prevents organoid fusion, eliminates the need for agitation, simplifies media changes, reduces media volume, and is compatible with Good Manufacturing Practices. We compared the ALi method to standard immersion culture for midbrain organoids, detailing the process from human pluripotent stem cell (hPSC) culture to organoid maturation and analysis. Air–liquid interface organoids (3D-ALi) showed optimized size and shape standardization. RNA sequencing and immunostaining confirmed neural/dopaminergic specification. Single-cell RNA sequencing revealed that immersion organoids (3D-i) contained 16% fibroblast-like, 23% myeloid-like, and 61% neural cells (49% neurons), whereas 3D-ALi organoids comprised 99% neural cells (86% neurons). Functionally, 3D-ALi organoids showed a striking electrophysiological synchronization, unlike the heterogeneous activity of 3D-i organoids. This standardized organoid platform improves reproducibility and scalability, demonstrated here with midbrain organoids. The use of midbrain organoids is particularly relevant for neuroscience and neurodegenerative diseases, such as Parkinson’s disease, due to their high incidence, opening new perspectives in disease modeling and cell therapy. In addition to hPSC-derived organoids, the method’s versatility extends to cancer organoids and 3D cultures from primary human cells. Full article

During large-scale pavement construction, the preparation of SBS-modified asphalt typically produces large amounts of harmful fumes. The emergence of deodorants can effectively alleviate the problem of smoke emissions during the asphalt manufacturing process. On the basis of ensuring the original road performance, exploring more suitable dosages and types of deodorant is urgently needed. Five commercial deodorants were evaluated using an asphalt smoke collection system, and UV-visible spectrophotometry (UV) was employed to screen the deodorants based on smoke concentration. Gas chromatography–mass spectrometry (GC-MS) was used to quantitatively analyze changes in harmful smoke components before and after adding two deodorants. Subsequently, the mechanisms of action of the two different types of deodorants were analyzed microscopically using fluorescence microscopy. Finally, the performance of bitumen and asphalt mixtures after adding deodorants was evaluated. The results showed that deodorant A (reactive type) and D (adsorption type) exhibited the best smoke suppression effects, with optimal addition rates of 0.6% and 0.5%, respectively. Deodorant A reduced benzene homologues by nearly 50% and esters by approximately 40%, while deodorant D reduced benzene homologues by approximately 70% and esters by approximately 60%, without producing new toxic gases. Both deodorants had a minimal impact on the basic properties of bitumen and the road performance of asphalt mixtures, with all indicators meeting technical specifications. This research provides a theoretical basis for the effective application of deodorants in the future, truly enabling a transition from laboratory research to large-scale engineering applications in the construction of environmentally friendly roads. Full article

This article presents the results of the tribological performance of 20MnCr5 (1.7147) tool steel countersamples produced by Direct Metal Laser Sintering (DMLS), as a potential material for inserts or working layers of sheet metal forming tools. Tribological tests were performed using a roller-block tribotester. The samples were sheet metals made of materials with significantly different properties: Inconel 625, titanium-stabilised stainless steel 321, EN AW-6061 T0 aluminium alloy, and pure copper. The samples and countersamples were analysed in terms of their wear resistance, coefficient of friction (COF), changes in friction force during testing, and surface morphology after tribological contact under dry friction conditions. The tests were performed on DMLSed countersamples in the as-received state. The largest gain of countersample mass was observed for the 20MnCr5/EN AW-6061 T0 friction pair. The sample mass loss in this combination was also the largest, amounting to 19.96% of the initial mass. On the other hand, in the 20MnCr5/Inconel 625 friction pair, no significant changes in the mass of materials were recorded. For the Inconel 625 sample, a mass loss of 0.04% was observed. The basic wear mechanism of the samples was identified as abrasive wear. The highest friction forces were observed in the 20MnCr5/Cu friction pair (COF = 0.913) and 20MnCr5/EN AW-6061 T0 friction pair (COF = 1.234). The other two samples (Inconel 625, 321 steel) showed a very stable value of the friction force during the roller-block test resulting in a COF between 0.194 and 0.213. Based on the changes in friction force, COFs, and mass changes in friction pair components during wear tests, it can be concluded that potential tools in the form of inserts or working layers manufactured using 3D printing technology, the DMLS method, without additional surface treatment can be successfully used for forming sheets of 321 steel and Inconel 625. Full article

Recently, diffractive optics systems have garnered increasing attention due to their myriad benefits in various applications, such as creating vortex beams, Bessel beams, or optical traps, while refractive optics systems still exhibit some disadvantages related to materials, substrates, and intensity shapes. The manufacturing of diffractive optical elements has become easier due to the development of lithography techniques such as direct laser writing, photo lithography, and electron beam lithography. In this paper, we improve the results from previous research and propose a new methodology to design and fabricate advanced binary diffractive optical elements that achieve a square focal spot independently, reducing reliance on additional components. By integrating a binary square zone plate with an axicon zone plate of the same scale, we employ machine learning for laser path optimization and direct laser lithography for manufacturing. This streamlined approach enhances simplicity, accuracy, efficiency, and cost effectiveness. Our upgraded binary diffractive optical elements are ready for real-world applications, marking a significant improvement in optical capabilities. Full article

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article

Over the last decade, polymers have attracted increasing attention for the fabrication of electronic devices due to the innovative results that can be achieved using additive manufacturing (AM) processes. Intrinsically conductive polymers are commonly used to obtain flexible and stretchable devices. They also enable the customisation of electronic devices when processed through AM. However, their main limitation is the reduction in electrical conductivity under mechanical deformation, such as bending. Extrinsically conductive nanocomposites, incorporating conductive fillers into polymer matrices, demonstrate the ability to retain electrical conductivity even following repeated bending, presenting a promising solution to the limitations of intrinsically conductive polymers. However, a gap remains in optimising their processing conditions for diverse 3D printing technologies. Moreover, fillers should be carefully selected according to the application’s specific needs. Dielectric polymers are also very promising for various electronic applications, but they are less investigated and have lower visibility than their conductive counterparts. This review presents three classes of polymer materials, i.e., intrinsically and extrinsically conductive polymers and insulators, discussing their advantages, drawbacks, and applications for 3D printing in electronics. This overview concludes with assessing future investigation areas needed to unlock the possibilities of 3D-printed polymers in electronics. Full article

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized into three thematic groups, followed by an analysis of key findings. Finally, the study identifies research gaps and proposes detailed recommendations along with a framework for future research. The study reveals that traditional AM processes have undergone significant transformations driven by digital threads, digital threads (DTs), and AI. However, this digitalization introduces vulnerabilities, leaving AM systems prone to cyber-physical attacks. Emerging advancements in AI, Machine Learning (ML), and Blockchain present promising solutions to mitigate these challenges. This paper is among the first to comprehensively summarize and evaluate the advancements in AM, emphasizing the integration of DTs, Modular AI, and cybersecurity strategies. Full article

Additive manufacturing (AM) techniques, especially fused deposition modeling (FDM), offer significant advantages in terms of cost, material efficiency, and design flexibility. In this study, the mechanical performance of biodegradable PLA/PHA composite samples produced via FDM was optimized by evaluating the influence of key printing parameters—layer height, printing orientation, and printing speed—on both the tensile and compressive strength. A full factorial design (3 × 3 × 3) was employed, and all of the samples were triplicated to ensure the consistency of the results. Grey relational analysis (GRA) was used as a multi-objective optimization method to determine the optimal parameter combinations. An analysis of variance (ANOVA) was also conducted to assess the statistical significance of each parameter. The ANOVA results revealed that printing orientation is the most significant parameter for both tensile and compression strength. The optimal parameter combination for maximizing mechanical properties was a layer height of 0.1 mm, an X printing orientation, and a printing speed of 50 mm/s. This study demonstrates the effectiveness of GRA in optimizing the mechanical properties of biodegradable composites and provides practical guidelines to produce environmentally sustainable polymer parts. Full article

As artificial intelligence (AI) becomes increasingly embedded in organizational development, understanding how leadership shapes employee responses to AI is critical for fostering workplace innovation. Drawing on trait activation theory, this study develops a theoretical model in which employee AI trust enhances innovative performance through AI crafting. Paternalistic leadership serves as a situational moderator, while the leader’s AI opportunity perception functions as a higher-order moderator. A three-wave survey was conducted with 523 employees from 14 AI-intensive manufacturing firms in China. Results show that the interaction between AI trust and paternalistic leadership positively predicts both AI crafting and innovative performance. In addition, AI crafting mediates the effect of the interaction term on innovative performance. Furthermore, the leader’s AI opportunity perception moderates this interactive effect: when this perception is high, the positive impact of AI trust and paternalistic leadership on AI crafting is significantly stronger; when it is low, the effect weakens. These findings contribute to the literature by clarifying the situational and cognitive conditions under which AI trust promotes innovation, thereby extending trait activation theory to AI-enabled workplaces and offering actionable insights for leadership development in the intelligent era. Full article

Directed Energy Deposition is an additive manufacturing subgroup that uses a laser beam to melt the wire or powder to create a melt pool. In the current study, a scientometric analysis has been carried out to analyze the contribution of countries, publication type analysis, distribution of publications over the years, keywords analysis, author analysis, cited journal, categories, institutes of publication, and report the practical implications. Firstly, the database was extracted from the Web of Science and then post-processed with CiteSpace 6.2.R4 and VOSviewer 1.6.20 software. Afterward, the associated results had been extracted and reported. It was found that China is the leader according to publication, followed by the USA and Germany, which mostly published their achievements in article and proceeding paper formats, which are increasing annually. According to the keywords, additive manufacturing, Laser Metal Deposition, and fabrication are the most commonly used. Based on the CiteSapce and VOSviewer results, Lin, Xin and Huang, Weidong are the authors with the highest publication rates. In addition, Additive Manufacturing, Materials & Design, and Materials Science and Engineering: A are the most cited journals, and regarding the categories, materials science, multidisciplinary, applied physics, and manufacturing engineering are the most commonly used DED processes. Northwestern Polytechnical University, Fraunhofer Gesellschaft, and the United States Department of Energy (DOE) have performed the most research in the field of DED. Full article

Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach struggles to meet the processing constraints of workpieces with higher production difficulty, while the second approach requires the development of suitable scheduling schemes to balance mold changes and continuous processing. Therefore, under the second approach, developing an excellent scheduling scheme is a challenging problem. This study addresses the mixed-flow assembly shop scheduling problem, considering continuous processing and mold-changing constraints, by developing a multi-objective optimization model to minimize additional production time and customer waiting time. As this NP-hard problem poses significant challenges in solution space exploration, the conventional NSGA-II algorithm suffers from limited local search capability. To address this, we propose an enhanced NSGA-II algorithm (RLVNS-NSGA-II) integrating deep reinforcement learning. Our approach combines multiple neighborhood search operators with deep reinforcement learning, which dynamically utilizes population diversity and objective function data to guide and strengthen local search. Simulation experiments confirm that the proposed algorithm surpasses existing methods in local search performance. Compared to VNS-NSGA-II and SVNS-NSGA-II, the RLVNS-NSGA-II algorithm achieved hypervolume improvements ranging from 19.72% to 42.88% and 12.63% to 31.19%, respectively. Full article

The dispersion behavior of ceramic particles in aluminum alloys during rapid solidification critically affects the resulting microstructure and mechanical performance. In this study, we investigated the nucleation and growth of Al₃(Sc,Zr) on TiB₂ surfaces in a 2TiB₂/Al–8Ni–0.6Sc–0.1Zr alloy, fabricated via wedge-shaped copper mold casting and laser surface remelting. Thermodynamic calculations were employed to optimize alloy composition, ensuring sufficient nucleation driving force under rapid solidification conditions. The results show that the formation of Al₃(Sc,Zr)/TiB₂ composite interfaces is highly dependent on cooling rate and plays a pivotal role in promoting uniform TiB₂ dispersion. At an optimal cooling rate (~1200 °C/s), Al₃(Sc,Zr) nucleates heterogeneously on TiB₂, forming core–shell structures and enhancing particle engulfment into the α-Al matrix. Orientation relationship analysis reveals a preferred (111)α-Al//(0001)TiB₂ alignment in Sc/Zr-containing samples. A classical nucleation model quantitatively explains the observed trends and reveals the critical cooling-rate window for composite interface formation. This work provides a mechanistic foundation for designing high-performance aluminum-based composites with uniformly dispersed reinforcements for additive manufacturing applications. Full article

As the use of data models and data science techniques in industrial processes grows exponentially, the question arises: to what extent can these techniques impact the future of manufacturing processes? This article examines the potential future impacts of these models based on an assessment of existing trends and practices. The drive towards digital-oriented manufacturing and cyber-based process optimization and control has brought many opportunities and challenges. On one hand, issues of data acquisition, handling, and quality for proper database building have become important subjects. On the other hand, the reliable utilization of this available data for optimization and control has inspired much research. This research work discusses the fundamental question of how far these models can help design and/or improve existing processes, highlighting their limitations and challenges. Furthermore, it reviews state-of-the-art practices and their successes and failures in material process applications, including casting, extrusion, and additive manufacturing (AM), and presents some quantitative indications. Full article