Manufacturing Applications Research Articles

Laser-directed energy deposition of low-carbon, low-temperature ultra-fine bainitic multi-physical modeling, microstructure and performance studies

An innovative iron-based alloy powder characterized by reduced carbon content and elevated Mn content was developed, leading to the fabrication of iron-based ultrafine bainitic steel with exceptional mechanical properties utilizing laser-directed energy deposition (L-DED). The layers' morphology, microstructure, phase composition, and elemental distribution were analyzed, indicating that the melting depth of the layer was deep and ultra-fine dendrite formed in the top region. A multi-physical powder and melt pool simulation was conducted to analyze the melt flow and pool solidification characteristics, showing that the strong downward flow inside the molten pool caused by the powder impingement increased the melting depth. The cooling rate G × R, where G was the temperature gradient and R was the solidification rate, was higher at the top pool boundary, facilitating the ultra-fine dendrite formation at this location. Following subsequent low-temperature isothermal heat treatment, a refined bainitic microstructure emerged within the deposited layer. The bainitic plates in the 573 K isothermally transformed bainite were interspersed with film-retained austenite (RA). Conversely, the bainitic plates formed at a lower temperature (523 K) exhibited a coalesced morphology stemming from the diminished stability of super-cooled austenite. The bainite plates underwent growth and coalescence, ultimately giving rise to the bainitic microstructure. The 523 K transformed coatings demonstrated superior hardness (509.2 HV), strength (1435 MPa), and elongation (11.5 %) compared to the 573 K transformed coatings (456.5 HV, 1241 MPa, 10.1 %). This research holds significant implications for advancing low-carbon bainitic materials with remarkable comprehensive mechanical properties in additive manufacturing applications.

Multi-factor coupling analysis of electric field distribution in electrochemical machining with electrolyte suction tool

Electrochemical machining (ECM) technology holds significant potential for applications in high accuracy engineering and manufacturing. To confine the electrolyte region during ECM and mitigate the effects of reaction products and heat generation, an electrolyte suction tool has been proposed. However, the complex electric field distribution on the anode surface during the application of pulse voltage, limits the further utilization of ECM with suction tool. A multi-physics simulation model is established to calculate the region of electrolyte confinement by the suction tool, current density distribution within the electrolyte region, and material removal. The model considers the combined effects of polarization and double layer, and all key parameters are calibrated through classical electrochemical testing experiments rather than fitted based on the predicted results. This study elucidates the transient response of current density on the anode surface during the application of pulse voltage. The simulation accuracy is validated by comparing experimental and simulated current waveforms, as well as material removal profiles under different pulse parameters and electrode shapes. This research is of great significance for improving surface precision and controllability of complex structures in ECM with suction tool, promoting its further application in the field of precision machining.

Fluid Ejections in Nature.

From microscopic fungi to colossal whales, fluid ejections are universal and intricate phenomena in biology, serving vital functions such as animal excretion, venom spraying, prey hunting, spore dispersal, and plant guttation. This review delves into the complex fluid physics of ejections across various scales, exploring both muscle-powered active systems and passive mechanisms driven by gravity or osmosis. It introduces a framework using dimensionless numbers to delineate transitions from dripping to jetting and elucidate the governing forces. Highlighting the understudied area of complex fluid ejections, this review not only rationalizes the biophysics involved but also uncovers potential engineering applications in soft robotics, additive manufacturing, and drug delivery. By bridging biomechanics, the physics of living systems, and fluid dynamics, this review offers valuable insights into the diverse world of fluid ejections and paves the way for future bioinspired research across the spectrum of life.

A‐prori layer height determination for wire arc additive manufacturing based on weld geometry

AbstractThe application of wire arc additive manufacturing (WAAM) for the production of large size components is currently limited, due to strong distortion and unprecise filling behavior. The resulting geometric and metallurgical irregularities pose a challenge to the process. The current approach of a layered structure cannot be adopted without adjustments when using wire arc additive manufacturing. Reasons include incompleteness, material accumulation and deformation. The combination of experimental weld geometry‐determination and its numerical estimation is presented here as solution to this challenge. The procedure is based on the measurement of a weld bead cross‐section‐area. By convolution of a path matrix with a weld‐geometry‐function, the planned path is filled with the seam geometry. Subsequent summation of multiple matrices results in a height profile showing discontinuities and accumulations. Further validation tests show a good agreement between the method and experimentally determined problem areas. The presented optimisation procedure can be extended with material parameters. A local compensation for deformation can be achieved.

Is fair carbon mitigation practicable in China? Insights from digital technology innovation and carbon inequality

The uneven matching of distribution and targets in practicing carbon emission reduction has led to emission reduction inequality, while digital technology innovation (DTI) is expected to reduce these gaps. Therefore, this study casts new insights into modeling the nexus between DTI and carbon inequality using China's 286 prefecture-level cities from 2000 to 2021. Remarkably, city-level DTI performance is innovatively proxied by the related patent application volume, while the carbon inequality index within the region is measured using the environmental Gini coefficient. The main findings of this study include: (1) DTI robustly mitigates China's carbon inequality. (2) The effect of DTI is asymmetrically more significant in regions with a higher carbon inequality index. (3) DTI heterogeneously reduces carbon inequality more obviously in the eastern and central regions, and technology innovation in digital product manufacturing and digital technology application industries reduces carbon inequality more significantly. (4) More precisely, DTI strengthens carbon justice through income equalization, spatial agglomeration, and digital inclusion effects. Specifically, DTI significantly reduces income inequality and the digital economy's spatial agglomeration level and enhances digital financial inclusion, thereby reducing carbon inequality. This study also provides the policy implications for China.

TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.

This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer's benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 μm for a 25.4 mm radius ball and ~6 μm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.

Effect of contact blast loading on the plastic deformation forming ability of large steel pipes

Plastic deformation forming with metal pipe blanks by contact blast loading inside pipes is an interesting moldless forming technique, also a complex and error-prone process. Some advantages are very characteristic of this forming technique such as no cost of mold, tooling and low energy consumption, no complicated control equipment compared to other forming techniques such as casting, rolling, tube hydrostatic forming, bending – welding. Up to now, the calculation and design of this forming technique mainly use some existing reference empirical formulas, so the experimental results are only suitable in the range of small pipe diameters, and still there are significant deviations for larger pipe diameters. In order to increase the predictability and accuracy of forming process by contact blast loading inside large pipes, this paper presents a study on the influence of the mass of highly explosive material – TNT to the forming ability of large steel pipes from API-5LX-42 mild steel materials by modern 3D numerical simulation – using Abaqus/Cae software. Four output criteria with maximum values are used to evaluate the efficiency of this forming process, including maximum diameter of the blast zone (Dmax£2D0), Von Mises stress (Smax£UTS), Hoop plastic strain component (PE22max£1), and Pipe wall thinning rate (eT-max£60 %). The results of this research on the plastic deformation forming process using numerical simulation can be used for the next experimental step to evaluate the difference between simulation and experiment, as well as use this data in the calculation and design of pipe products with circular or square cross-sections to save both time and money of trial and error before application in actual manufacturing

Development and Application of Modern Intelligent Control Theory

Modern intelligent control theory has experienced multi-stage development from classical control to fuzzy control, neural networks, and model predictive control, which not only improves the flexibility and adaptability of control systems but also promotes interdisciplinary research. With the advancement of science and technology and social development, intelligent control theory will be further combined with machine learning, big data, and other fields in the future to promote industrial automation and technological innovation. This article reviews the development history of modern intelligent control theory, its main theories, and its applications in robot manufacturing, power electronics, and other fields. It also discusses the current challenges and future development trends in the field of intelligent control.

Stimuli-responsive materials for 4D Printing: Mechanical, Manufacturing, and Biomedical Applications

Four-dimensional printing (4DP) is an interesting field of study that integrates intelligent materials into three-dimensional printing (3DP). Using this method, objects that can gradually change form in response to environmental stimuli, such as moisture, electric or magnetic fields, UV light, temperature swings, pH variations, or alterations in the composition of ions, can be created. Intelligent materials, such as shape memory polymers, alloys, hydrogels, ceramics, and composites, which can react to external stimuli, are essential for 4DP. Unlike traditional 3D printing, the ability of 4D printed materials to undergo structural and/or property alterations expands their applications across industries such as aerospace, biomedical, soft robotics, engineering, and fashion, potentially revolutionizing manufacturing. This review provides an overview of current advancements in 4D printing and discusses both established and emerging smart materials with shape-changing capabilities and their responses to stimuli. It explores the differences between 4D and 3D printing technologies, traces the origins of 4D printing, and examines the materials involved, with emphasis on shape-memory polymers. This paper also highlights the applications of 4D printing in pharmaceuticals and biomedicine, particularly in dynamic polymers, and in the development of drugs for genetic diseases. In addition, it discusses progress in creating implantable biomedical devices and pharmaceutical medications. Despite being in its early stages, 4D printing technology has immense potential and promising exciting possibilities in the future.

A proposal for an operational methodology to assist the ranking-aggregation problem in manufacturing

Ranking aggregation is an ancient problem with some characteristic elements: a number of experts, who individually rank a set of objects according to a certain (subjective) attribute, and the need to aggregate the resulting expert rankings into a collective judgment. Although this problem is traditionally very popular in fields such as social choice, psychometrics, and economics, it can also have several interesting applications in manufacturing, e.g., for customer-oriented design, reliability engineering, production management, etc. Through a case study related to a cobot-assisted manual (dis)assembly, the paper illustrates an operational methodology and various useful tools that assist in tackling the problem practically, effectively, and with a critical mind. Some of the proposed tools allow to estimate the degree of concordance among experts, and the collective judgment’s consistency and robustness. The paper is aimed at scientists and practitioners in manufacturing.

A study of the Performance of Haier Smart under Digital Transformation

In recent years, the digital boom has continued to hit, and global scientific and technological innovation has been unprecedentedly intensive and active. On the cusp of this 5G trend, emerging technologies such as artificial intelligence, cloud sharing, and big data are emerging in an endless stream, constantly impacting traditional technologies and the real economy. The fourth industrial revolution has quietly penetrated into every corner of society. Both people's lives and the economic environment have been unprecedentedly affected. As an important pillar industry in the national economic construction, how to meet the digital transformation, seize the opportunity of digital transformation, enhance product value, and create new quality productivity has become particularly important. This paper takes digital transformation as the starting point, explores the motivation and path of Haier's digital transformation in the past ten years, and evaluates its performance. The study found that Haier Smart Home's digital transformation has improved the long-term performance of enterprises; the first enlightenment is to accelerate the transformation and upgrading and improve the degree of digital attention ; second, adhere to innovation-driven, enhance digital R & D sales; third, create a digital platform to enhance the user's personalized experience. It is expected that the research in this paper can provide some reference for the digital transformation of other household appliance enterprises and bring some help to the digitization of China's household appliance manufacturing industry.

Wire-arc directed energy deposition of metal using a tendon-driven soft robotic gun: prototyping and conceptual validation

ABSTRACT Most directed energy deposition (DED) implementations employ a rigid end effector to deposit material layer-by-layer, which is insufficient in cases with space constraints. To enhance the flexibility, this paper proposes a novel soft-robotic gun (SRG) based DED technology. The ideology is to replace the rigid weld gun with a tendon-driven omnidirectional-steering continuum manipulator, which a computer can numerically control. To this end, both requirements and concepts of the SRG-DED are introduced. A prototype system is designed and implemented before dedicated kinematic modelling and trajectory planning mechanisms are presented. As demonstrated by the experimental results, the developed trajectory planning algorithm improves the computational efficiency of tendon variables and enhances the consistency of trajectory tracking. The prototype system can deposit a complex path on an inclined substrate and fabricate a thin-wall component within a pipeline. The validation work confirms the feasibility and applicability of the prototype system for metal additive manufacturing applications.

Toward Practical Applications of Engineered Living Materials with Advanced Fabrication Techniques.

Engineered Living Materials (ELMs) are materials composed of or incorporating living cells as essential functional units. These materials can be created using bottom-up approaches, where engineered cells spontaneously form well-defined aggregates. Alternatively, top-down methods employ advanced materials science techniques to integrate cells with various kinds of materials, creating hybrids where cells and materials are intricately combined. ELMs blend synthetic biology with materials science, allowing for dynamic responses to environmental stimuli such as stress, pH, humidity, temperature, and light. These materials exhibit unique "living" properties, including self-healing, self-replication, and environmental adaptability, making them highly suitable for a wide range of applications in medicine, environmental conservation, and manufacturing. Their inherent biocompatibility and ability to undergo genetic modifications allow for customized functionalities and prolonged sustainability. This review highlights the transformative impact of ELMs over recent decades, particularly in healthcare and environmental protection. We discuss current preparation methods, including the use of endogenous and exogenous scaffolds, living assembly, 3D bioprinting, and electrospinning. Emphasis is placed on ongoing research and technological advancements necessary to enhance the safety, functionality, and practical applicability of ELMs in real-world contexts.

Lean-and-Green Fractional Factorial Screening of 3D-Printed ABS Mechanical Properties Using a Gibbs Sampler and a Neutrosophic Profiler

The use of acrylonitrile butadiene styrene (ABS) in additive manufacturing applications constitutes an elucidating example of a promising match of a sustainable material to a sustainable production process. Lean-and-green datacentric-based techniques may enhance the sustainability of product-making and process-improvement efforts. The mechanical properties—the yield strength and the ultimate compression strength—of 3D-printed ABS product specimens are profiled by considering as many as eleven controlling factors at the process/product design stage. A fractional-factorial trial planner is used to sustainably suppress by three orders of magnitude the experimental needs for materials, machine time, and work hours. A Gibbs sampler and a neutrosophic profiler are employed to treat the complex production process by taking into account potential data uncertainty complications due to multiple distributions and indeterminacy issues due to inconsistencies owing to mechanical testing conditions. The small-data multifactorial screening outcomes appeared to steadily converge to three factors (the layer height, the infill pattern angle, and the outline overlap) with a couple of extra factors (the number of top/bottom layers and the infill density) to supplement the linear modeling effort and provide adequate predictions for maximizing the responses of the two examined mechanical properties. The performance of the optimal 3D-printed ABS specimens exhibited sustainably acceptable discrepancies, which were estimated at 3.5% for the confirmed mean yield strength of 51.70 MPa and at 5.5% for the confirmed mean ultimate compression strength of 53.58 MPa. The verified predictors that were optimally determined from this study were (1) the layer thickness—set at 0.1 mm; (2) the infill angle—set at 0°; (3) the outline overlap—set at 80%; (4) the number of top/bottom layers—set at 5; and (5) the infill density—set at 100%. The multifactorial datacentric approach composed of a fractional-factorial trial planner, a Gibbs sampler, and a neutrosophic profiler may be further tested on more intricate materials and composites while introducing additional product/process characteristics.

Investigation of oil palm fiber reinforced polylactic acid composite extruded filament quality

This study examines the quality of Polylactic Acid (PLA) filament reinforced with Oil Palm Fiber (OPF) for additive manufacturing applications. The research aims to create a composite filament that leverages the advantages of PLA, a biodegradable polymer, and OPF, a natural fiber from the oil palm tree, to enhance mechanical strength, dimensional stability, and printability. The methodology involves crushing the PLA filament and OPF to the desired size using a crusher machine, blending them in different ratios (e.g., 90:10 and 80:20 PLA to OPF), and using a hot-pressing process to bond the components. The resulting pelletized composites are then extruded into filaments using an extruder machine. The quality of the produced filament is assessed based on diameter consistency, surface smoothness, and printability, considering compatibility with 3D printers. The study reveals that composition ratios and processing parameters impact filament quality, leading to challenges such as diameter variations, rapid hardening, breakage, and extruder die clogs. Future recommendations were suggested to optimize compositions, refine processing, explore advanced extrusion, and investigate fiber distribution and bonding for improved filament properties. This research offers valuable insights for creating high-quality OPF-reinforced PLA filaments for additive manufacturing, advancing understanding of filament quality factors, and proposing ways to enhance composite filament performance across applications.

Advancements in Fused Deposition Modeling for Aerospace: Optimizing Lightweight and High- Strength Components

Fused Deposition Modeling (FDM) has emerged as a pivotal technology in aerospace manufacturing, enabling the creation of lightweight and high-strength components. Recent advancements in FDM materials, process optimization, and design methodologies have significantly enhanced its applicability in producing aerospace parts that meet stringent performance criteria. This paper reviews the latest developments in FDM technology, focusing on material innovations, structural optimization techniques, design for additive manufacturing and practical applications in the aerospace sector. Key advancements include the use of high-performance thermoplastics, carbon fiber composites, and hybrid materials, as well as improved printing techniques that reduce defects and enhance mechanical properties. The potential of FDM to revolutionize aerospace manufacturing through cost- effective and efficient production of complex geometries is explored, highlighting ongoing research and future directions in this dynamic field.

Evaluation of alternative construction methods for architectural additive manufacturing applications

Evaluation of alternative construction methods for architectural additive manufacturing applications

Bridging molecular dynamics and additive manufacturing: A study of Al-12 at% Si alloy solidification dynamics

This manuscript provides a detailed analysis of the molecular dynamics of the directional solidification process in Aluminum-Silicon (Al-12 % at Si) alloys, with a particular emphasis on applications in additive manufacturing. Utilizing molecular dynamics (MD) simulations, the study examines large systems containing 5–15 million atoms to investigate various facets of the solidification process. These include the dynamics of interface motion and the formation of Silicon (Si) precipitates. The research reveals significant variations in interface velocities and orientations across different thermal gradients and growth rates, underscoring the intricate interaction between thermal and solute flows in the microstructural development of Al-Si alloys. These findings are vital for comprehending and refining the solidification process in additive manufacturing, ultimately enhancing the mechanical strength and reliability of the final products. The paper highlights the critical role of microstructure control in printed metals and proposes future research directions, aiming to apply these insights to more complex alloy compositions and manufacturing contexts. In the concluding phase, the scikit-image library was employed to analyze dendritic formations in solidifying Al-Si alloys using MD simulations. This analysis delineates trends in dendritic count, spacing, perimeter, and area over time, offering essential perspectives on the solidification process and its implications for the alloy's microstructure and mechanical characteristics. The Si content in the Al-Si alloy used can be considered eutectic in the solidified structure. Si atoms are predominantly located at grain boundaries and precipitate regions. Coordination number analysis further confirms the presence of Si in these locations. This microstructure, characterized by a primary Al/Al-Si matrix and Si-rich eutectic regions, aligns with experimental observations in rapid solidification processes in additive manufacturing.

Generative Artificial Intelligence: Analyzing Its Future Applications in Additive Manufacturing

New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.

Antibacterial Janus cellulose/MXene paper with exceptional salt rejection for sustainable and durable solar-driven desalination

The scarcity of freshwater resources and increasing demand for drinking water have driven the development of durable and sustainable desalination technologies. Although MXene composites have shown promise due to their excellent photothermal conversion and high thermal conductivity, their high hydrophilicity often leads to salt precipitation and low durability. In this study, we present a novel Cellulose (CF)/MXene paper with a Janus hydrophobic/hydrophilic configuration for long-term and efficient solar-driven desalination. The paper features a dual-layer structure, with the upper hydrophobic layer composed of CF/MXene paper exhibiting convexness to serve as a photothermal layer with exceptional salt rejection properties. Simultaneously, the bottom porous layer made of CF acts as an efficient thermal insulation. This unique design effectively minimizes heat loss and facilitates efficient water transportation. The Janus CF/MXene paper demonstrates a high evaporation rate of 1.11 kg m−2h−1 and solar thermal conversion efficiency of 82.52 % under 1 sun irradiation. Importantly, even after 2500 h of operation in a simulated seawater environment, the paper maintains a stable evaporation rate without significant salt deposition and biodegradation due to an antibacterial rate exceeding 90 %. These findings highlight the potential of the Janus CF/MXene paper for scalable manufacturing and practical applications in solar-driven desalination.