Tetrachiral cylindrical metamaterials exhibit unique deformation characteristics arising from their rotational unit-cell topology and continuous ligament network. While their global mechanical behavior is strongly governed by geometric parameters, the specific influence of ligament thickness on compressive performance and auxetic response remained unexplored. This study addresses this gap through a systematic numerical and experimental investigation. A series of five tetrachiral cylindrical structures sharing identical outer dimensions, unit-cell geometry, and lattice orientation were designed, with ligament thickness varied between 0.2 and 1.0 mm. Quasi-static compression was simulated using a finite element to capture the full deformation process, including nonlinear stiffness evolution, progressive instability, and pseudo-densification. The simulations reveal that ligament thickness plays a dominant role in controlling global stiffness, deformation stability, and energy dissipation capacity. Increasing ligament thickness led to an approximately five-fold increase in load-bearing capacity and a 2.4-fold improvement in specific energy absorption. In addition, ligament thickness significantly altered local deformation mechanisms, shifting the response from relatively homogeneous chiral rotation to pronounced strain localization in thicker-ligament designs. All configurations exhibited strong auxetic behavior, with negative Poisson’s ratios ranging from −2.8 to −2.2. To assess the predictive capability of the numerical model, a representative tetrachiral cylinder was fabricated using VAT photopolymerization-based additive manufacturing and tested under quasi-static compression. The experimentally measured response closely matched the numerical predictions, validating the modeling approach. The combined numerical–experimental results demonstrate that ligament thickness provides an effective and practical design lever for tailoring stiffness, energy absorption, and auxetic response, supporting their deployment in protective, and impact-mitigation applications.
Thermal errors in fused deposition modeling (FDM) machines can reduce dimensional accuracy by altering the nozzle position due to non-uniform heating of the nozzle and the build platform. While previous studies have mainly emphasized part distortion, the workspace-dependent thermal deformation of the printer structure has received less attention. This study develops a coupled thermo-mechanical framework to model, validate, and compensate position-dependent thermal errors in a gantry-type FDM 3D printer. A finite element model was used to predict the temperature field and the resulting Z-direction nozzle displacement at 27 locations distributed over three measurement planes within the build volume. Experimental validation was carried out using a laser displacement sensor. The simulated and experimental results agree well, with deviations generally within 3%–5%, confirming the reliability of the proposed model. The model achieved an MAE of 2.51 µm and an RMSE of 2.54 µm. Linear regression between the numerical predictions and experimental measurements yielded an R 2 value of 0.992, indicating excellent agreement between the simulation and experimental results. Furthermore, the 95% confidence interval of the mean prediction error was 2.34 to 2.67 µm, demonstrating the robustness and consistency of the proposed model. The sensitivity analysis reveals that the heat bed temperature is the dominant factor influencing thermal deformation, with a sensitivity of 4.091 µm/°C. In comparison, the nozzle temperature exhibits moderate sensitivity (0.85 µm/°C), while the convection coefficient (20–40 W/m 2 K) shows a limited influence within the investigated range. These findings highlight that precise control of the heated bed temperature is essential for minimizing thermal deformation and improving printing accuracy. Based on the validated deformation field, a position-dependent compensation strategy is proposed to improve dimensional accuracy and support future real-time thermal error correction in FDM systems.
Thin-walled ring gears are prone to hobbing-induced distortion because material removal redistributes body residual stress (BRS) and introduces machining-induced residual stress (MIRS). This study distinguishes BRS and MIRS and establishes a coupled finite element method (FEM) workflow for normalized 18CrNiMo7-6 ring gears by combining AdvantEdge cutting simulation with ANSYS material-removal analysis. Initial BRS was evaluated using strain energy density, and residual-stress evolution during hobbing was examined under different hobbing-cutter rotational speeds and axial feed rates. Cutting power was used to assess the cutting-load input, whereas post-hobbing deformation measurements were used to evaluate the endpoint prediction of the coupled model. The results show that residual stress is concentrated near the tooth root and that a moderate increase in rotational speed or axial feed rate can reduce tensile surface residual stress within the tested parameter range. The through-thickness machining position also strongly affects radial deformation. These findings provide a basis for controlling hobbing distortion in thin-walled ring gears through process-parameter selection and blank-position planning.
Sheets with sub-millimeter thicknesses are increasingly used in micro-forming processes. Micro deep drawing is a sheet metal forming process commonly applied in industries such as electrical component manufacturing. Copper is widely used in the electronics industry, and in many cases, thin sheets with thicknesses below 1 mm are required. However, reducing the thickness of copper sheets introduces challenges, such as increased result variability and decreased strength. In this study, an effort was made to enhance the mechanical properties of copper sheets by applying Constrained Groove Pressing (CGP) as a cold working method. The results show that CGP increases ultimate strength by up to 25% and yield strength by up to 114%. Additionally, it reduces maximum strain by up to 77% and increases hardness by up to 100%. A comparative analysis was then conducted to examine the effects of these changes on several Micro Deep Drawing parameters, including maximum punch stroke, maximum punch force, punch force at 2.5 mm stroke, wrinkling, and thickness uniformity across the cup’s cross section. The results indicate that applying CGP results in less than a 10% decrease in maximum punch stroke, a 7% increase in maximum punch force, and up to a 27% increase in punch force at 2.5 mm after two CGP passes. Moreover, CGP reduced the wrinkling of the blank. One particularly significant finding is that CGP meaningfully reduced data scatter and improved cross-sectional thickness uniformity, enhancing the predictability of material behavior.
Modern manufacturing is transitioning toward autonomous, unmanned, and flexible production environments, where industrial robots (IRs) must operate intelligently while remaining synchronized with other manufacturing assets. This article presents a comprehensive cloud-enabled Digital Twin (DT) architecture for IRs that enables remote task allocation, autonomous ROS-based execution, near real-time monitoring, and synchronized operation with physical manufacturing assets through a unified Cloud–Edge framework. A Unity-based dashboard, integrated with AWS cloud services and edge devices, allows operators to remotely assign manufacturing tasks with varying operation sequences, enabling flexible coordination among IRs, CNC machines, and 3D printers for smart and dark factory applications. To ensure sustained operational accuracy, the proposed architecture incorporates a Kalman filter-based joint deviation compensation framework that estimates and compensates joint deviations caused by wear, backlash, and material degradation. The proposed framework is experimentally validated using two IRs. Experimental results demonstrate autonomous task execution with RMSE values below the encoder resolution and residual deviations within ±0.2° after compensation. Furthermore, a GUM-based measurement uncertainty analysis validates the metrological reliability of the proposed framework, yielding expanded uncertainties between ±0.0362° and ±0.0460°. Overall, the proposed architecture provides a scalable foundation for autonomous, synchronized, and intelligent smart manufacturing systems.
Aluminum matrix silicon carbide (SiCp/Al) composite is a typical high brittle-hard material, which is prone to surface defects such as matrix tearing and particle breakage during the machining process. In this study, ultra-precision turning was carried out on SiCp/Al composite with a volume fraction of 35%. The influence laws of two machining conditions—conventional precision turning and ultrasonic elliptical vibration-assisted turning (UEVAT)—on the surface hardness, thickness of the metamorphic layer, morphology and microhardness of the metamorphic layer after machining were investigated. The results show that, compared with conventional precision turning, UEVAT can ensure the surface hardness of workpieces to a certain extent. Within the parameter range studied, the surface hardness is increased by approximately 9.5%–17.7% with the change of spindle speed, 3.5%–13.4% with the change of feed rate, and 6.2%–10.1% with the change of tool nose radius. Additionally, this method reduces the thickness of the metamorphic layer, ensures the morphology and microhardness of the metamorphic layer.
During the production process on the coating line, workpieces are often stacked disorderly, resulting in significant mutual obstruction. This creates considerable challenges for robotic loading and unloading operations. Conventional vision algorithms struggle to meet production requirements for recognition success rates and pose estimation accuracy when faced with weakly textured, reflective workpiece surfaces and complex occlusion environments. To address this issue, this paper proposes a robotic loading and unloading method that integrates Singular Value Decomposition (SVD) and Iterative Closest Point (ICP) algorithms. First, point clouds within the storage bin undergo preprocessing and segmentation. Subsequently, feature matching using Fast Point Feature Histograms (FPFH) and SVD decomposition is employed to compute a coarse pose estimation of the workpiece. This approach overcomes the ICP algorithm’s sensitivity to initial values and its tendency to converge to local optima. Finally, using this coarse pose as the initial estimate, a KD-Tree accelerated ICP algorithm performs fine registration, yielding high-precision six-degree-of-freedom pose estimation. Robotic simulations and physical experiments were conducted. In the simulated environment, the workpiece loading/unloading success rate reached 97%, while the experimental platform achieved an 85.41% success rate. The total runtime of the component recognition and pose estimation algorithms is less than 1.26 s, meeting the real-time requirements of industrial applications. This paper provides a reliable solution for robotic component handling in scenarios such as coating lines.
The dynamic accuracy of the feed system in CNC machine tools is determined by its dynamic design. Currently dominant frequency-domain analysis and design methods lack a direct correlation with user-concerned time-domain indices such as positioning accuracy and repeatability accuracy. The feed system is composed of four subsystems: motion planning, control system, servo motor, and mechanical components. These subsystems are mutually coupled and collectively affect its time-domain accuracy. Among them, motion planning is determined by user requirements, while the control system’s parameters are adjustable. Therefore, the core of dynamic design lies in eliminating the interference of the control system under specific motion planning to conduct electromechanical system design. To address this, this paper proposes a control system decoupling method. For different mechatronic matching combinations, intelligent optimization algorithms optimize control parameters. This separates control system interference on time-domain accuracy, highlighting mechatronic matching’s influence mechanism. This study verifies its effectiveness via feed system integration models and experiments. It provides theoretical and practical guidance for time-domain accuracy-oriented dynamic optimization of feed systems.
Lithium niobate crystals are typical soft-brittle optoelectronic materials widely used in optical modulation, communication, sensing, and precision optical devices. However, conventional grinding easily induces surface scratches, particle embedding, subsurface cracks, and severe processing damage, which restricts high-precision, low-damage manufacturing. To solve this problem, ultrasonic-assisted high-shear and low-pressure grinding (HSLPG) with ball-end body-armor-like abrasive tools (BAAT) was proposed. Considering the high-frequency, intermittent contact characteristics induced by ultrasonic vibration, the vector superposition of the ultrasonic tangential impact load and the normal external load was introduced. Based on ultrasonic vibration kinematics, elastohydrodynamic lubrication and Hertz contact theory, a theoretical grinding force prediction model for ultrasonic-assisted HSLPG was established. The spatial distribution characteristics of elastohydrodynamic pressure under different ultrasonic frequencies, amplitudes, normal loads and spindle rotational speeds were revealed, and the intrinsic regulation mechanism of process parameters on contact pressure and tangential grinding force was clarified. Single-factor grinding experiments on a lithium niobate crystal were conducted to verify the accuracy of the proposed model. The results show that the predicted grinding force values are in good agreement with the experimental values, with an average error of only 5.04%. Increasing the ultrasonic frequency and amplitude reduces the average tangential grinding force, whereas increasing the normal load and spindle speed significantly increases the grinding force. Compared the tangential grinding forces of conventional grinding and ultrasonic-assisted HSLPG.
To address the challenge of polishing air-film holes in aero-engine turbine blades, this paper proposes a three-phase foam cavitation jet polishing technique. The method utilizes the synergistic effect of abrasive erosion and cavitation to polish and improve hole quality. Computational fluid dynamics simulations using ANSYS Fluent analyzed the influence of jet pressure, abrasive concentration, and standoff distance (SOD) on wall shear stress and cavitation volume fraction. Experiments were conducted to evaluate these parameters’ effects on material removal rate and surface roughness, leading to the identification of an optimal combination: 0.8 MPa jet pressure, 7% abrasive concentration, and 10 mm standoff distance. Under these conditions, the surface roughness (Ra) of the polished holes was reduced to 4.45 ± 0.23 μm. This work provides a new approach and parameter guidance for the efficient, high-quality polishing of turbine blade air-film holes.
The Shared Factory (SharedF) paradigm represents a transformative approach to manufacturing that enhances operational flexibility, resource efficiency, and collaborative capacity within shared manufacturing. This systematic review synthesizes findings from 936 peer-reviewed studies (2015–2025) to achieve three core objectives: establishing conceptual consensus on SharedF’s defining characteristics, identifying four critical research domains and examining technology integration patterns through industrial cases. Collectively, these evidence-based analyses provide implementable pathways for sustainable SharedF development.
Friction stir welding (FSW) has emerged as a highly promising solid-state joining technique for polymeric materials, offering an effective alternative to conventional fusion welding, adhesive bonding, and mechanical fastening. This review critically analyzes recent progress in the friction stir welding of polymers, with emphasis on quantitative relationships between process parameters, tool design, thermal behavior, and joint performance. Reported studies indicate that optimized tool pin geometries, particularly threaded cylindrical and frustum profiles, can achieve joint efficiencies exceeding 85%–95% of the base material strength in polymers such as HDPE, PP, and PMMA. The weld-to-velocity (w/v) ratio is identified as a key governing parameter, with optimal ranges varying across polymers (e.g. ∼25–60 rev/mm for PP and ∼100–130 rev/mm for PE), directly influencing heat input, material flow, and defect formation. Due to the inherently low thermal conductivity of polymers, heat accumulation and dissipation play a decisive role in microstructural evolution, including crystallinity changes and zone formation within the weld region. Recent advances in tool tilt control, plunge depth optimization, and sensor-based monitoring have significantly improved process reliability and repeatability. By synthesizing the experimental, numerical, and monitoring-based studies published recently, this review provides a comprehensive understanding of polymer FSW mechanisms, performance limits, and industrial applicability, particularly for lightweight and hybrid structures in automotive and aerospace sectors.
Self-piercing riveting (SPR) is a method for joining sheets by forming a mechanical interlock between them. SPR is increasingly adopted in the automotive industry due to its effectiveness in joining lightweight, high-strength, and dissimilar materials. This paper focuses on the riveting of commonly used metals in automotive bodies and explores recent advancements in SPR for new material joining. The challenges of conventional SPR (C-SPR) techniques are analyzed, and several optimized SPR processes are discussed in detail. Finally, methods for evaluating SPR joint quality are examined, including traditional inspection and evaluation methods and non-destructive testing techniques based on artificial intelligence and deep learning.
Accurate rotor installation is essential in order to ensure the performance, reliability, and safety of aero-engines. However, manufacturing errors introduced during the assembly process have the potential to propagate and drive the entire assembly out of their desired specifications. According to the reviewed studies, advanced mathematical models show notable improvements in rotor assembly precision. Reported outcomes include reduction of maximum coaxiality errors from 48.8 μ m to 17.9 μ m, reduced fitting-axis offsets from 21.3 μ m to 8.5 μ m, docking error reductions of 50%–65%, and prediction deviations maintained below 14%. Additional findings show 22.5% reduction in vibration amplitude, 44.1% reduction in unbalance, and error reduction rates reaching 86% using intelligent optimization approaches. This review provides a systematic and comprehensive analysis of the state-of-the-art mathematical models employed for the error propagation analysis in aero-engine rotor assembly. These models are effectively used for evaluating and mitigating errors from the conceptual design to the final assembly. The primary objective of this review study is to identify the most widely adopted mathematical model used for error propagation analysis in aero-engine rotor assembly. Forty-five scholarly research papers, including journal articles, patents, and a conference paper have been thoroughly reviewed to provide key insights pertaining to error propagation rotor assembly. The findings of this study underscore the growing importance of mathematical models in improving aero-engine rotor assembly precision, particularly HMT based models thus paving the way for more efficient and robust aero-engine manufacturing processes. This review serves as a valuable source of knowledge for researchers and engineers, guiding them in selecting the most appropriate model to improve assembly accuracy and ensure the safe functioning of aero-engines.
Self-Driving Manufacturing Labs (SDMLs) are emerging as a transformative approach to experimental manufacturing research, offering the ability to automate and optimize complex workflows with minimal human intervention. This paper defines a novel conceptual framework for SDMLs, systematically distinguishing between automation—the coordinated execution of experimental tasks through integrated hardware and software—and autonomy, the system’s ability to make data-driven decisions using machine learning and optimization algorithms. We decompose automation into four core components: materials design or manufacturing, property characterization, materials handling, and inter-machine communication. Autonomy is structured around data collection, surrogate modeling, and Bayesian optimization, enabling systems to adaptively choose optimal experimental conditions. The primary contribution of this work is the structured definition of this framework illustrated by examples, which is shown to be generalizable across different manufacturing domains, providing a modular blueprint for the design and implementation of next-generation self-driving laboratories. The paper concludes with a discussion of future directions for advancing automation, autonomy, and scaling SDMLs across broader applications in intelligent manufacturing.
Warm roll bonding (WRB) is a promising solid-state joining technique for producing aluminium-steel laminates with high strength-to-weight ratios and corrosion resistance. Despite growing interest in roll bonding, understanding of WRB-specific mechanisms and process-structure-property relationships remains fragmented across the literature. This review critically examines the underlying bonding mechanisms, material combinations, surface engineering methods, interfacial microstructure evolution, and the role of processing parameters in WRB. WRB operates at intermediate temperatures (200°C–350°C), promoting oxide fracture, virgin metal extrusion, and diffusion bonding while minimising excessive intermetallic compound (IMC) growth. The use of steel and AA6xxx aluminium alloys, when combined with optimised surface treatments and interlayers (Zn, Ni, Cu), yields strong and ductile joints with thin IMCs such as Fe 2 Al 5 and FeAl 3 . Key challenges include controlling IMC thickness, managing thermal stresses, and achieving uniform strain during rolling and post forming. Advanced surface activation methods, such as plasma or laser structuring, and FEM-based thermomechanical models are being developed to improve bond quality and predict failure. Applications of WRB span automotive, energy, and structural sectors, particularly for electric vehicles and lightweight infrastructure. By consolidating recent experimental findings, this review identifies key research gaps and outlines strategies for achieving scalable, defect-free WRB joints. The insights presented aim to support the future development of WRB-based multilayer materials for high-performance, multifunctional applications.
Ceramic extrusion additive manufacturing (CEAM) uses ceramic pellets as feedstock instead of the usual filament. It is a widely adopted and prominent technology in the domain of additive manufacturing. This technique involves the sequential deposition of material in layers via a heated, movable bed. This material solidifies at a predetermined temperature as the base, and the dispensing head traverses a defined trajectory along the X , Y , and Z axes. Pellet-based 3D printing is more cost-effective than traditional filament printing. This process is ideal for large-scale projects, and offers a wide variety of ceramic material options, including alumina, zirconia, silica, and composites. Various factors, such as extrusion speed, nozzle diameter, nozzle and bed temperature, printing speed, orientation, layer thickness, infill density, and pattern, influence the quality of the final product in CEAM. Horizontal orientation enhances flexural strength, while vertical orientation improves hardness. Thinner layers and optimized flow rates result in higher density and superior mechanical properties. Ceramics are consolidated into dense, crack-free components by ultrafast high-temperature sintering and rapid radiation sintering. Technique such as extrusion and spark plasma sintering improves the flexibility of materials. Mechanical properties get enhanced by sintering additives. CEAM’s widespread appeal is due to its accessibility, material versatility, and suitability for rapid prototyping, custom manufacturing, and educational applications. Although this process provides certain benefits, it faces challenges related to dimensional accuracy, surface finish, the physical and mechanical properties of the printed object. The extrudability and shape fidelity, along with material compatibility, and the optimization of production speed are also significant challenges. These concerns can be fixed through the optimization of paste’s rheology and solids loading, the adoption of controlled drying and debinding schedules, and proper adjustment of printing parameters, including nozzle size, printing speed, and temperature, to minimize defects and improve density.
With the continuous development of Industry 4.0 and Industry 5.0, the manufacturing industry has an increasing demand for intelligent, human-robot collaboration and flexible production systems. In order to improve flexibility and accuracy in the production process, human-robot collaboration technology has emerged as a core component of smart manufacturing. Although human-robot collaboration technology has been widely used, its performance is still constrained by the frequency of production switching, the lack of collaboration accuracy, and the untimely response to unexpected situations. In order to solve these problems, this paper proposes an all-element virtual modeling and real-time reconfiguration method for flexible human-robot assembly units. It aims to enhance the efficiency and precision of human-robot collaboration through accurate virtual reality technology, and to realize high-precision simulation and optimization of assembly cells by integrating the perception and modeling of people, robots, tasks, and environmental factors. For efficient data collection, format conversion and integration. Firstly, we use the multivariate heterogeneous data fusion technique to construct a unified data fusion channel. Secondly, we design high-fidelity models suitable for human-robot task environments. The model is “form-substance-consistent” and “form-nature-consistent,” and integrates geometric, physical, behavioral, and operational characteristics to capture the full range of human-robot processes. On this basis, the precise mapping of operational data to real-time interactions of these object models is realized through a real-time data-driven interface. This optimizes the accuracy and efficiency of the collaborative human-robot assembly process. Finally, the real-virtual mapping accuracy is verified under experimental conditions at two levels: data transmission and reception accuracy and real-time real-virtual mapping. Provide a new method and technical support for human-robot assembly.
Increased demand of high-strength, low-weight components in aerospace and automobile sectors has strengthened the focus on joining dissimilar aluminum alloys. Friction stir welding (FSW) is a solid-state process overcomes the thermal defects inherent in fusion welding. Nevertheless, FSW joints involving dissimilar materials tend to experience poor mechanical properties because of limited material flow. In the current research work, the mechanical strength of similar and dissimilar AA6061–AA6063 friction stir lap welds was improved by including B 4 C particles within the nugget region. Experimental process parameters were tool tilt angle (TTA), traverse speed (TS), and rotational speed (RS) for optimization based on response surface methodology using central composite design (CCD). Twenty experimentation trials were followed by regression analysis modeling and statistical ANOVA testing. Optimized parameters (RS: 1106.3 rpm, TS: 31.26 mm/min, TTA: 1.526°) achieved a maximum UTS of 281.17 MPa. X-ray diffraction analysis (JCPDS: 04-0787 for α-Al, 35-0798 for boron carbide (B 4 C)) ensured the retention of B 4 C without unwanted phase formation. Grain refinement and distribution of particles homogeneously were observed through microstructural and scanning electron microscope analysis. This work proves that controlled reinforcement with optimized FSW parameters can noticeably enhance joint strength, providing an effective pathway toward advanced structural applications.
In recent years, industry-related research has proposed to improve personnel efficiency using augmented reality (AR). However, with the increasing complexity of the production line in the industrial field, the problem of insufficient computing performance of AR hardware for applications arises. Therefore, this study deployed the image processing operation of the AR system to the fog nodes and used the 5G network to ensure the image transmission delay between AR devices and fog nodes to resolve the problem of insufficient AR hardware performance and improve the scalability of the AR system. On this basis, the AR monitoring and remote maintenance system is developed. The fog node is used as the relay station to integrate data from CNC-machines and remote maintenance platforms into the AR system. Field personnel can monitor the CNC status and receive remote expert maintenance instructions through AR devices and can immediately detect and resolve CNC anomalies to reduce the additional cost caused by temporary shutdown. In this study, 5G is used for image transmission to ensure that the delay is less than 20 ms. Compared with local image processing on AR devices, the performance is improved by 28%. On this basis, the monitoring system is developed to update AR visualization data with a frequency of 209 ms. This system is used for maintenance assistance. The test results indicate that 30% maintenance efficiency improvement is achieved for users unfamiliar with CNC maintenance tasks. The aforementioned results prove the efficiency and feasibility of the AR remote maintenance system architecture in the industrial field and provide a solution for the AR application in the industrial field.
