Search Results (561)

As key equipment in the manufacturing industry, computer numerical control (CNC) machines need to meet the ever-increasing demands for high automation, intelligence, and integration. Since its introduction in 2003, digital twin (DT) has seen its broad applications in various areas, such as product design, process monitoring, quality control, and fault diagnosis. A DT creates a virtual replica of the physical system by integrating real-time data with simulation technologies, providing new possibilities to make CNC machining more intelligent. In the past decade, extensive research has been conducted on the implementation of CNC machining DTs (CNCDTs). This paper focuses specifically on multisensor data fusion-driven CNCDTs by introducing key technologies including sensors, data fusion, and CNCDT architecture. A comprehensive survey is conducted on existing studies of CNCDTs according to their application areas, followed by critical analysis on existing challenges. This review summarizes the current progress of CNCDTs and provides guidance for further development. Full article

The interlaminar drilling of CFRPs is a new machining method different from traditional drilling, in which the feed direction of the drill bit is parallel to the interlayer interface. To reasonably select tools for CFRP interlaminar drilling, four different types of tool structures, including twist drills, dagger drills, candlestick drills, and step drills, are employed to conduct interlaminar drilling. The axial force and the morphologies of material damage are extracted, the comprehensive damage factors are calculated, and the relation among tool structures, machining parameters, and outlet damage is analyzed. Results show that the peak axial force induced by the four types of tool structures reduces sequentially. The dagger drill and the candlestick drill tend to cause burrs and large-area surface tears, respectively, while the twist drill and the step drill will lead to more significant 3D tears. Among the four tools, the average comprehensive damage factor produced by twist drills is the smallest, making it more suitable for CFRP interlaminar drilling. In addition, this study establishes a mathematical prediction model for the peak axial force and the comprehensive damage factor and optimizes the process parameter combination of twist drills, with the spindle speed set to 4732.87 r/min and the feed speed to 0.137 mm/r. Full article

One of the key measures of cutting tool efficiency in machining processes is tool wear. In recent decades, numerical modeling of this phenomenon—primarily through finite element cutting models—has gained increasing importance. A crucial requirement for the reliable application of such models is the selection of an appropriate friction model, which strongly affects the accuracy of wear predictions. However, choosing the friction model type and its parameters remains a nontrivial challenge. This paper examines the effect of different friction model types and their parameters on the Archard and Usui wear model indicators, as well as on the main cutting process characteristics: cutting force components, temperature in the primary cutting zone, contact length between the tool rake face and the chip, shear angle, and chip compression ratio. To evaluate their impact on predicted tool wear—expressed qualitatively through the wear indicators of the aforementioned models—several widely used friction models implemented in commercial FEM software were applied: the shear friction model, Coulomb friction model, hybrid friction model, and constant tau model. The simulated values of these cutting process characteristics were then compared with experimental results. Full article

Precision manufacturing requires handling multi-physics coupling during processing, where digital twin and AI technologies enable rapid robot programming under customized requirements. However, heterogeneous data sources, diverse domain models, and rapidly changing demands pose significant challenges to digital twin system integration. To overcome these limitations, this paper proposes a digital twin modeling strategy based on a metamodel and a virtual–real fusion architecture, which unifies models between the virtual and physical domains. Within this framework, subsystems achieve rapid integration through ontology-driven knowledge configuration, while ROS provides the execution environment for establishing robot manufacturing digital twin scenarios. A case study of a robot shaping system demonstrates that the proposed architecture effectively addresses heterogeneous data association, model interaction, and application customization, thereby enhancing the adaptability and intelligence of precision manufacturing processes. Full article

The residual stresses induced by the curing process for carbon fiber-reinforced polymers (CFRPs) lead to inevitable deformation, which seriously affects manufacturing accuracy, especially for large-sized CFRP components. Furthermore, deformation may cause damage or failure of components during subsequent assembly. More attention needs to be paid to improving the curing accuracy for large-sized CFRP components. In this study, a normal direction compensation algorithm based on node deformation is proposed based on the mold profile. Firstly, a finite element model was constructed to simulate the curing process of CFRPs and validated with a small-sized test piece. The results showed the effectiveness of the simulated and compensation methods. Secondly, to meet the precision requirements of large-sized CFRP components, a neural network was used to establish a mapping between curing process parameters and curing deformation, and the parameters were then optimized with a genetic algorithm for subsequent analysis. Finaly, a 15 m CFRP component was used to explore the effect of compensation methods in reducing curing deformation of large-sized CFRP components. The verified results showed that the maximum deformation after compensation was 0.99 mm in the normal direction, which was superior to the 1.258 mm compensation in the connecting direction. Full article

Accurately identifying the load status of a ball mill is critical for optimizing grinding efficiency and ensuring operational stability. However, the one-dimensional vibration signals collected from ball mills exhibit strong non-stationarity and a high degree of entanglement between multi-scale local transient features and long-range temporal evolution patterns. To address this, rather than relying on a purely black-box approach, this paper introduces a novel Deep Multi-scale Spatial–Temporal Feature Decoupling Network (DMSTFD-Net) guided by a clear feature decoupling philosophy to enhance model interpretability. The core of DMSTFD-Net lies in its hierarchical collaborative feature refinement mechanism. It first utilizes a one-dimensional residual network (ResNet) to adaptively capture and preliminarily decouple multi-scale spatial characteristics from the raw signal. Subsequently, the extracted high-level feature sequences are fed into a bidirectional gated recurrent unit (Bi-GRU) to decouple high-order temporal dynamic patterns. Experiments on a multi-condition dataset demonstrate that the proposed network achieves a state-of-the-art accuracy of 97.65%. Furthermore, dedicated cross-condition experiments and t-SNE visualizations validate the framework’s effectiveness. The results confirm that DMSTFD-Net provides a powerful, robust, and more interpretable solution for ball mill load identification. Full article

SiCp/Al composites (Silicon Carbide Particle-Reinforced Aluminum Matrix Composites), due to their light weight, high strength, and superior wear resistance, are extensively utilized in aerospace and other sectors; nonetheless, they are susceptible to tool wear and surface imperfections during machining, which negatively impact overall machining performance. Supercritical carbon dioxide minimal quantity lubrication (SCCO₂-MQL) is an environmentally friendly and efficient lubrication method that significantly improves interfacial lubricity and thermal stability. Nonetheless, current lubrication models are predominantly constrained to gas–liquid two-phase scenarios, hindering the characterization of the three-phase lubrication mechanism influenced by the combined impacts of SCCO₂ phase transition and ultrasonic vibration. This study formulates a lubricant film thickness model that incorporates droplet atomization, capillary permeation, shear spreading, and three-phase modulation while introducing a pseudophase enhancement factor β_ps(p,T) to characterize the phase fluctuation effect of CO₂ in the critical region. Simulation analysis indicates that, with an ultrasonic vibration factor Af = 1200 μm·kHz, a lubricant flow rate Q_f = 16 mL/h, and a pressure gradient Δp_tot = 6.0 × 10⁵ Pa/m, the lubricant film thickness attains its optimal value, with Δp_tot having the most pronounced effect on the film thickness (normalized sensitivity S = 0.488). The model results align with the experimental trends, validating its accuracy and further elucidating the nonlinear regulation of the film-forming process by various parameters within the three-phase synergistic lubrication mechanism. This research offers theoretical backing for the enhancement of performance and the expansion of modeling in SCCO₂-MQL lubrication systems. Full article

The tolerance of critical components in five-axis machine tools directly impacts the overall machining accuracy of the entire system. This paper presents a tolerance optimization method for machine tools that is grounded in sensitivity theory and the NSGA-II algorithm. First, a mapping model is established to relate tolerance parameters to geometric and spatial motion errors. Second, a gradient-based sensitivity index, which has a clear physical interpretation and high computational efficiency, is defined to quantify the influence of individual tolerances on the spatial motion errors. Recognizing the limitations of existing tolerance allocation methods, this study introduces the innovative concept of tolerance control cost (the sum of the products of tolerance sensitivity and tolerance value for each parameter), and an optimization model is formulated to minimize this while ensuring the spatial motion error meets the requirement. The NSGA-II algorithm is employed to solve this model. Simulation results demonstrate that the tolerances of components can be significantly relaxed (thereby indirectly reducing manufacturing costs) while still ensuring the desired spatial motion error of the entire machine, validating the feasibility and effectiveness of the proposed method. Full article

There is a lack of information in the literature on the influence of technological heredity on surface integrity characteristics after diamond burnishing (DB). The present study fills this gap. Here, we present the effects of DB on the roughness parameters and surface microhardness of heat-treated C45 steel under conditions of changing initial roughness (Ra^init) due to wear on the cutting insert in the previous turning. The aim was to quantitatively assess the ability of DB to maintain sustainable surface integrity characteristics. We found that the service life of the cutting insert up to complete wear or fracture when operating at an optimal feed rate and cutting velocity was 163 min, at which point the roughness changed unevenly from an average roughness (Ra) value of 0.38 to 1.31 μm and an average height of the profile microroughness (Rz) value of 2.21 to 6.13 μm. Under conditions of an artificially created Ra^init (through different combinations of feed rate and cutting velocity) of 0.308 to 10.688 μm, DB provided Ra values in the range of 0.042 to 0.316 μm, with the surface microhardness varying from 461 to 568 HV. Stable Ra values were maintained from 0.042 μm to 0.089 μm, after which the Ra^init increased to 3.379 μm. Under production conditions, where the previous turning was performed at an optimal feed rate of 0.05 mm/rev and a cutting velocity of 180 m/min, DB provided a stable Ra of ≤0.059 μm of a resulting mirror-like surface during the first 90 min of operation of a new (unused) cutting insert, after which the Ra values increased linearly from 0.059 to 0.133 μm in the 150th minute. After 30 min of operation, until the cutting insert was completely worn, the microhardness after DB varied from 676 to 795 HV, the high surface microhardness resulting from a complex process of surface thermo-mechanical strengthening (including strain and transformation hardening) in the previous turning due to wear on the cutting insert. Full article

Modern production systems face the challenges of increasing personalization of products, growing structural complexity, and the need for sustainability. In this context, it is necessary to include the human dimension in the optimization of production processes, especially in line with the principles of Industry 5.0 and the circular economy. In this paper, a complexity index is proposed that integrates the objective characteristics of the product and the subjectively perceived workload of the operator during assembly. The proposed index was used in the assembly line optimization process using linear programming to find a compromise solution between two often-conflicting objectives: maximizing output and minimizing complexity. In the analysis, two approaches to the initial balance of the assembly line were considered—by assembly time and by complexity of work elements—which were used as inputs to the optimization model. The results show that an approach that considers complexity from the operator’s point of view contributes to a more even load distribution but also can lead to higher overall performance. Such an approach confirms the importance of integrating the human factor into optimization processes and thus contributes to the creation of efficient, sustainable, and human-centric production systems of the future. Full article

This study examines the digital maturity of small- and medium-sized enterprises (SMEs) in the context of Industry 4.0. Despite growing awareness of the importance of digital transformation, many SMEs encounter structural and strategic challenges that impede their progress. Among their obstacles is the inadequacy of digital maturity models used to diagnose digital maturity levels in SMEs due to their typological, sectoral, geographical, and other specific characteristics. Using a constructivist and qualitative approach, we have developed a simplified, inclusive, and holistic assessment framework comprising six key dimensions (technology, culture, organization, people and human resources, strategic planning), associated with six progressive maturity levels. Our findings reveal that most SMEs studied in 2023 exhibit a beginner level of digital maturity. These enterprises are characterized by small-scale digital initiatives, often lacking a clear strategy, with limited or partial digitization of processes and heterogeneous technology adoption. The resulting self-assessment tool provides SMEs with practical guidance to launch, evaluate, and accelerate their digital transformation. This study contributes theoretically by proposing a practical digital maturity model and offering a tool to support SMEs and public policy. It highlights the need for tailored support, strategic alignment, and continuous training to unlock the full potential of Industry 4.0 in less urbanized and resource-constrained areas. Full article

As Industry 5.0 emerges as a human-centric evolution of industrial systems, this study investigates the effectiveness of training interventions in companies aimed at supporting the transition to Industry 5.0, emphasizing human-centric and resilient skill development. Drawing from multiple case studies involving engineers and operators, the research applies both meta-analysis and meta-regression to assess the added value of experiential learning approaches such as Teaching and Learning Factories. In addition, a novel methodology combining quantitative analyses with qualitative interpretation of emerging competences is presented. Principal Component Analysis and classification frameworks are employed to identify and organize key competence clusters along technological, organizational, and social dimensions. Special attention is given to the emergence of human-centered competences such as decision empowerment, which are shown to complement traditional operational capabilities. The findings confirm that experiential training interventions enhance both self-efficacy and adaptive operational readiness, while the use of fusion techniques enables the generalization of results across heterogeneous corporate settings. This work contributes to ongoing discourse on Industry 5.0 readiness by linking training design to strategic company incentives and highlights the role of structured evaluation in informing future policy and implementation pathways. Full article

Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%. Full article

Chatter vibration in machining operations has been identified as one of the major obstacles to improving surface quality and productivity. Therefore, efficiently and accurately predicting stable cutting regions is becoming increasingly important, especially in high-speed milling processes. In this study, on the basis of a predictor–corrector scheme, the following three error correction methods are developed for milling stability analysis: the Correction Hamming–Milne-based method (CHM), the Correction Adams–Milne-based method (CAM) and the Predictor–Corrector Hamming–Adams–Milne-based method (PCHAM). Firstly, we employ the periodic delay differential equations (DDEs), which are usually adopted to describe mathematical models of milling dynamics, and the time period of the coefficient matrix is divided into two unequal subintervals based on an analysis of the vibration modes. Then, the Hamming method and the fourth-order implicit Adams–Moulton method are separately utilized to predict the state term, and the Milne method is adopted to correct the state term. Based on local truncation error, combining the Hamming and Milne methods creates a CHM that can more precisely approximate the state term. Similarly, combining the fourth-order implicit Adams–Moulton method and the Milne method creates a CAM that can more accurately approximate the state term. More importantly, the CHM and the CAM are employed together to acquire the state transition matrix. Thereafter, the effectiveness and applicability of the three error correction methods are verified by comparing them with three existing methods. The results demonstrate that the three error correction methods achieve higher prediction accuracy without sacrificing computational efficiency. Compared with the 2nd SDM, the calculation times of the CHM, CAM and PCHAM are reduced by around 56%, 56% and 58%, respectively. Finally, verification experiments are carried out using a CNC machine (EMV650) to further validate the reliability of the proposed methods, where ten groups of cutting tests illustrate that the stability lobes predicted by the three error correction methods exhibit better agreement with the experimental results. Full article

Additive manufacturing (AM), often referred to as 3D printing, has seen significant advances over the last few years. Through extensive research covering a wide range of industries from automotive and aerospace to healthcare, AM comes with the advantage of reduced manufacturing costs and ease of transition from design to real prototype. This review paper navigates the landscape of the AM process to highlight the latest findings in terms of process, materials, and applications by analyzing publications between 2022 and 2025. A particular focus is given to the integration of new materials including high-performance polymers and bio-based composites, types of printing materials that can enhance the performance and durability of 3D printing processes. In addition, the paper examines advances in printing technologies, including multi-material and large-format printing, as well as the integration of artificial intelligence for process optimization and quality control. Considering these advances, critical challenges such as the productivity, high cost, limited material options, and ethical concerns over intellectual property are also addressed. By synthesizing current trends and assessing future directions, while considering a critical view, this study aims to inform researchers and industry stockholders about the evolving additive manufacturing landscape and the opportunities and obstacles on the horizon. Full article