Search Results (2,038)

The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and labor-intensive, hindering their large-scale production. Recent advances in 3D printing technology offer innovative pathways to address these challenges. By combining design flexibility with material optimization, 3D printing holds the potential to enhance battery performance and enable customized structures. This review systematically examines the application of 3D printing technology in fabricating key AZIB components, including electrodes, electrolytes, and integrated battery designs. We critically compare the advantages and disadvantages of different 3D printing techniques for these components, discuss the potential and mechanisms by which 3D-printed structures enhance ion transport and electrochemical stability, highlight critical existing scientific questions and research gaps, and explore potential strategies for optimizing the manufacturing process. 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

This narrative review explores the role of additive manufacturing (AM) technologies in the production of dental implants, focusing on materials and key AM methods. The study discusses several materials used in implant fabrication, including porous titanium, trabecular tantalum, zirconium dioxide, polymers, and composite materials. These materials are evaluated for their mechanical properties, biocompatibility, and suitability for AM processes. Additionally, the review examines the main AM technologies used in dental implant production, such as selective laser melting (SLM), electron beam melting (EBM), stereolithography (SLA), selective laser sintering (SLS), and direct metal laser sintering (DMLS). These technologies are compared based on their accuracy, material limitations, customization potential, and applicability in dental practice. The final section presents a data source analysis of the Web of Science and Scopus databases, based on keyword searches. The analysis evaluates the research trends using three criteria: publication category, document type, and year of publication. This provides an insight into the evolution and current trends in the field of additive manufacturing for dental implants. The findings highlight the growing importance of AM technologies in producing customized and efficient dental implants. Full article

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

The development of technology has driven a rising need for high-accuracy and high-efficiency manufacturing of low-volume products. Incremental forming technology, characterized by die-free flexibility and low production costs, can effectively replace stamping processes for manufacturing customized small-batch products. However, high-performance aluminum alloys generally exhibit poor room-temperature plasticity but excellent high-temperature plasticity, necessitating the integration of thermal-assisted methods for manufacturing such products. However, the temperature of the forming region will excessively rise without temperature control, which will affect the forming performance of the material in hot incremental sheet forming of AA2024-T4 aluminum alloy. This study focuses on AA2024-T4 aluminum alloy and proposes a uniform temperature control method for the electric hot tube-assisted incremental sheet forming process, incorporating an adaptive fuzzy PID algorithm. The temperature difference of the forming region is lower than 6% under the various temperatures. On this basis, the forming limit angle and the microstructure state of the material are analyzed, and the grain feature of the material exhibits significantly refined grains and the uniform fine grain distribution under 180 °C with the temperature control of the adaptive fuzzy PID algorithm. Full article

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in MTO settings across three manufacturing sectors: electrical equipment, steel, and office supplies. A cross-industry benchmark assesses models such as ARIMA, Holt–Winters, Random Forest, LSTM, and Facebook Prophet. The evaluation considers error metrics (MAE, RMSE, and sMAPE) as well as implementation aspects like computational demand and interpretability. Special attention is given to data sensitivity and technical limitations typical in SMEs. The findings show that ML models perform well under high volatility and when enriched with external indicators, but they require significant expertise and resources. In contrast, simpler statistical methods offer robust performance in more stable or seasonal demand contexts and are better suited in certain cases. The study emphasizes the importance of transparency, usability, and trust in forecasting tools and offers actionable recommendations for selecting a suitable forecasting configuration based on context. By aligning technical capabilities with operational needs, this research supports more effective decision-making in data-constrained MTO environments. Full article

Discontinuous fibers are commonly added to matrix materials in additive manufacturing to enhance properties, but such benefits may be constrained by print and fiber orientation. The additive processes of forming rasters and layers in powder bed fusion inherently cause anisotropy in printed parts. Many print parameters, such as laser, temperature, and hatch pattern, influence the anisotropy of tensile properties. This study characterizes fiber orientation attributed to recoating non-encapsulated fibers and the resulting anisotropic tensile properties. Tensile and fracture properties of polyamide 12 reinforced with 0%, 2.5%, 5%, and 10% discontinuous carbon fibers by volume were characterized in two primary print/tensile loading orientations: tensile loading parallel to the recoater (“horizontal specimens”) and tensile load along the build axis (“vertical specimens”). Density and fractographic analysis indicate a homogeneous mixture with low porosity and primary fiber orientation along the recoating direction for both print orientations. Neat specimens (zero fiber) loaded in either direction have similar tensile properties. However, fiber-reinforced vertical specimens have significantly reduced consistency and tensile strength as fiber content increased, while the opposite is true for horizontal specimens. These datasets and results provide a mechanism to tune material properties and improve the functionality of selectively laser-sintered fiber-reinforced parts through print orientation selection. These datasets could be used to customize functionally graded parts with multi-material selective laser-sintering manufacturing. Full article

This study explores the efficacy of magnetic abrasive finishing (MAF) with planetary kinematics for post-processing Inconel 939 components fabricated by laser powder bed fusion (LPBF). Given the critical limitations in surface quality of LPBF-produced parts—especially in hard-to-machine superalloys like Inconel 939—there is a pressing need for advanced, adaptable finishing techniques that can operate effectively on complex geometries. This research focuses on optimizing the process parameters—eccentricity, rotational speed, and machining time—to enhance surface integrity following preliminary vibratory machining. Custom-designed samples underwent sequential machining, including heat treatment and 4 h vibratory machining, before MAF was applied under controlled conditions using ferromagnetic Fe-Si abrasives. Surface roughness measurements demonstrated a significant reduction, achieving Ra values from 1.21 µm to below 0.8 µm in optimal conditions, representing more than a fivefold improvement compared to the as-printed state (5.6 µm). Scanning Electron Microscopy (SEM) revealed progressive surface refinement, with MAF effectively removing adhered particles left by prior processing. Statistical analysis confirmed the dominant influence of eccentricity on the surface profile parameters, particularly Rz. The findings validate the viability of MAF as a precise, controllable, and complementary finishing method for LPBF-manufactured Inconel 939 components, especially for geometrically complex or hard-to-reach surfaces. Full article

Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions. Full article

We present a neural-network-based approach to solve the image-to-image translation problem in microscale droplets squeeze flow. A residual convolutional neural network is proposed to address the inverse problem: reconstructing a low-resolution (LR) droplet pattern image from a high-resolution (HR) liquid film thickness imprint. This enables the prediction of initial droplet configurations that evolve into target HR imprints after a specified spreading time. The developed neural network architecture aims at learning to tune the refinement level of its residual convolutional blocks by using function approximators that are trained to map a given film thickness to an appropriate refinement level indicator. We use multiple stacks of convolutional layers, the output of which is translated according to the refinement level indicators provided by the directly connected function approximators. Together with a non-linear activation function, the translation mechanism enables the HR imprint image to be refined sequentially in multiple steps until the target LR droplet pattern image is revealed. We believe that this work holds value for the semiconductor manufacturing and packaging industry. Specifically, it enables desired layouts to be imprinted on a surface by squeezing strategically placed droplets with a blank surface, eliminating the need for customized templates and reducing manufacturing costs. Additionally, this approach has potential applications in data compression and encryption. Full article

Supervisory control theory (SCT), based on Petri nets, offers a robust framework for modeling and controlling discrete-event systems but faces significant challenges in scalability, expressiveness, and practical implementation. This paper introduces General-purpose Petri Net Simulator and Real-Time Controller (GPenSIM), a MATLAB version 24.1.0.2689473 (R2024a) Update 6-based modular Petri net framework, as a novel solution to these limitations. GPenSIM leverages modular decomposition to mitigate state-space explosion, enabling parallel execution of weakly coupled Petri modules on multi-core systems. Its programmable interfaces (pre-processors and post-processors) extend classical Petri nets’ expressiveness by enforcing nonlinear, temporal, and conditional constraints through custom MATLAB scripts, addressing the rigidity of traditional linear constraints. Furthermore, the integration of GPenSIM with MATLAB facilitates real-time control synthesis, performance optimization, and seamless interaction with external hardware and software, bridging the gap between theoretical models and industrial applications. Empirical studies demonstrate the efficacy of GPenSIM in reconfigurable manufacturing systems, where it reduced downtime by 30%, and in distributed control scenarios, where decentralized modules minimized synchronization delays. Grounded in systems theory principles of interconnectedness, GPenSIM emphasizes dynamic relationships between components, offering a scalable, adaptable, and practical tool for supervisory control. This work highlights the potential of GPenSIM to overcome longstanding limitations in SCT, providing a versatile platform for both academic research and industrial deployment. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

Objective: This article presents a step-by-step digital technique for fabricating a 3D-printed surgical guide to assist in alveoloplasty for immediate denture placement. Methods: The workflow integrates intraoral scanning, virtual tooth extraction, digital soft tissue modeling, and additive manufacturing to produce a customized guide with an occlusal window and buccal slot, along with a verification stent. Results: This method ensures precise ridge recontouring and verification, enhancing surgical predictability and prosthetic fit. Conclusions: Unlike traditional surgical guides based on conventional casts or manual fabrication, this fully digital approach offers a practical and replicable protocol that bridges digital planning and clinical execution. By improving surgical precision, reducing operative time, and ensuring optimal denture fit, this technique represents a significant advancement in guided pre-prosthetic surgery. Full article

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts while managing inventory efficiently. An excess of inventory can increase warehousing costs, while stock shortages can lead to supply delays. Accurate demand forecasting is essential to balance these factors, considering the changing demand characteristics over time, such as long-term trends, seasonal fluctuations, and irregular variations. This paper introduces a novel method for time series forecasting that employs Ensemble Empirical Mode Decomposition (EEMD) and Dynamic Mode Decomposition (DMD) to analyze service part demand. EEMD decomposes historical order data into multiple modes, and DMD is used to predict transitions within these modes. The proposed method demonstrated an approximately 30% reduction in forecasting error compared to comparative methods, showcasing its effectiveness in accurately predicting service parts demand across various patterns. Full article