Search Results (655)

In high-value precision machining, existing tool wear monitoring models often suffer from two major limitations: poor generalization under varying cutting conditions and heavy reliance on large amounts of labeled data for new operating scenarios. These limitations hinder the practical deployment of intelligent monitoring systems. To address these challenges, this paper proposes a few-shot warm-start framework based on model-agnostic meta-learning. The method consists of two stages. First, meta-training is performed on historical machining data to learn a task-sensitive parameter initialization that enables rapid adaptation. Second, under a new operating condition, the few-shot warm-start mechanism collects a minimal number (1 to 5) of samples through a targeted physical trial-cutting process for online fine-tuning, aligning the model with the current physical environment. Experiments on the PHM2010 dataset fully simulate varying cutting scenarios. The experimental results demonstrate that the proposed framework consistently outperforms traditional transfer learning, deep learning models, and existing meta-learning approaches, offering an effective solution for fast and accurate tool wear prediction under few-shot and varying cutting conditions. Full article

Ultra-high-speed cutting (UHSC) has emerged as a transformative manufacturing technology aimed at overcoming the long-standing machining challenges associated with high-performance difficult-to-machine composites (HPDMCs). These materials—comprising silicon-based, metal matrix, and carbon fiber-reinforced polymers—are critical to strategic sectors such as aerospace and high-end equipment. This review adopts a distinctive “material-tool-process-equipment” synergistic innovation framework as its core analytical lens. Within this framework, it systematically outlines advances in UHSC, including the fundamental mechanisms of damage suppression and surface integrity enhancement under ultra-high strain rates. Innovative process methods such as laser-assisted and ultrasonic-assisted machining are examined in detail. This review also provides a mechanistic analysis of two key enabling technologies—tool micro-texturing and functional coatings—highlighting their roles in interfacial tribological regulation and physicochemical protection. Furthermore, dedicated equipment systems and stability optimization strategies essential for technological implementation are presented and evaluated. By synthesizing the current state of the field, this review identifies persistent bottlenecks and, guided by the proposed framework, suggests targeted future research directions: deep integration of smart manufacturing technologies, development of synergistic multi-energy-field processing, and enhanced adaptability to extreme service environments. This work not only consolidates the current knowledge in UHSC but also outlines a clear pathway for its evolution into a fully autonomous, efficient, and reliable manufacturing paradigm. Full article

We report on tool wear and surface roughness for hybrid additive manufacturing of Inconel 718 components. The hybrid additive manufacturing comprises laser powder bed fusion (PBF-LB/M) and an in situ high-speed milling process, i.e., milling is performed within the powderbed, which deteriorates the surface quality by additionally occurring wear mechanisms. Therefore, in this comparative study milling path suction is used to improve tool wear characteristics and thus enhance surface quality. As a result, we quantify the improvement of the maximum tool life according to the flank wear, which is granted by the milling path suction. Additionally, the dominant wear mechanisms are investigated, revealing adherence and abrasion as the main contributing factors to wear. Furthermore, surface analysis shows an improvement of surface quality by the use of the milling path suction. Specifically, a reduction in surface roughness of hybrid manufactured Inconel 718 components down to a minimum of $Ra$ = 0.55 μm is highlighted. Full article

Cooperative 3D printing (C3DP) is an additive manufacturing paradigm where a swarm of robotic 3D printers work cooperatively in a shared environment to fabricate continuous parts. Reliable operation requires both accurate per-printer kinematic calibration and cross-printer spatial alignment. This paper presents an automatic vision-based XY calibration workflow for C3DP using ArUco fiducials and low-cost monocular cameras. The method performs intra-printer kinematic calibration and inter-printer alignment through peer-to-peer observations without fixed global infrastructure. In a two-printer Selective Compliance Assembly Robot Arm (SCARA) Fused Filament Fabrication (FFF) testbed, the automatic workflow reduced total calibration time from 157.19 min (manual) to 36.49 min while improving positional consistency and print accuracy. For individual-printer artifacts, the mean Euclidean error was 0.03 ± 0.02 mm, whereas cooperative artifacts exhibited a mean Euclidean error of 0.078 ± 0.002 mm. These results show that practical and repeatable C3DP calibration can be achieved with low-cost vision hardware. Full article

Mill relining manipulator is essential maintenance equipment used to replace liners in a grinding mill. However, its excessive structural weight significantly constrains maneuverability and operational efficiency. To address this problem, this paper proposed a lightweight design framework for the manipulator’s upper arm, integrating improved multiple operating conditions topology optimization with size optimization. Firstly, a finite element model of the manipulator was established in ANSYS Workbench 2022R2. The loads under the corresponding operating conditions were extracted and applied to the finite element model of the upper arm to perform multi-condition finite element simulations. Secondly, a mathematical model for multi-condition topology optimization was developed using the variable density method combined with the Analytic Hierarchy Process (AHP), and the weight coefficients for each operating condition were determined. Finally, a combined response surface methodology (RSM) and genetic algorithm (GA) approach was employed to optimize the structural parameters of the upper arm. A response surface model with maximum equivalent stress and maximum deformation as the response variables was constructed, and the Pareto optimal set was obtained using the non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal structural design. Quasi-static load tests were conducted on a scaled prototype to verify the reliability of the numerical optimization results. The results demonstrate that the optimized upper arm satisfies the strength and stiffness requirements while achieving a 12% mass reduction (2463 kg), confirming the effectiveness and engineering applicability of the proposed lightweight design methodology. Full article

As a solid-state welding technique, friction stir welding (FSW) has many advantages over conventional fusion welding. Its applications in the manufacturing and joining of parts in aerospace, automotive, and shipbuilding have significantly increased. Friction heat generation is the fundamental driver of the FSW process. It governs material flow, microstructural evolution, mechanical properties, and residual stresses. Understanding the effect of heat generated on the joint quality is essential for process parameter optimization, ensuring defect-free welds and high-quality joints. Thus, evaluating the thermal history of the FSW process is a key requirement for effective analysis. This comprehensive review critically discusses research studies published over the past three decades (1991–2025) that have examined different approaches to predict and measure heat generation in FSW. A total of 136 highly relevant articles were selected from the Scopus database and systematically analyzed. The effects of various welding parameters on heat generation, microstructural evolution, and joints’ mechanical properties have been reported. Different heat generation prediction and measurement techniques, such as analytical models, finite element models (FEM), and experimental methods have been discussed in terms of their feasibility, accuracy, advantages, disadvantages, and cost. The evolution, state of the art of analytical models and FEM over the last three decades are analyzed and future research directions are outlined. Finally, the correlation between process parameters, heat generated, microstructural development, and mechanical performance of the welded joints for various workpiece materials is investigated. This review provides a critical and comparative perspective that highlights the strengths and limitations of each method, offering practical guidance for researchers and industry practitioners. Full article

Digital twin (DT) technology is a key element of Industry 4.0. Despite its rapid development, current research is mainly focused on industrial optimisation and machine-level monitoring. However, its implementation in the educational process lags significantly behind practice. Moreover, existing DT implementations in education often emphasise visualisation or simulation, while neglecting synchronisation and verification of functional equivalence between the physical and virtual systems. This study presents the design, development and experimental verification of a digital twin of a laboratory material handling system. The virtual model created in Tecnomatix Plant Simulation is connected to the physical system controlled by a Siemens PLC SIMATIC S7-1200 and equipped with industrial sensors and an HMI interface. Real-time bidirectional communication is established via the OPC UA protocol using KEPServerEX, ensuring synchronisation between the physical and virtual systems. Experiments confirmed the functional synchronisation of both systems. Additionally, the study presents that DT technology can be adapted for educational purposes and implemented in engineering education. Full article

Seamless Copper Tube Manufacturing (SCTM) is a multi-stage manufacturing chain, typically comprising billet casting, hot extrusion, cold drawing or pilgering, intermediate annealing, and finishing operations. Despite the fact that quality control (QC) practices are implemented at individual stages, many product deviations originated from cumulative thermomechanical and metallurgical interactions across multiple processes. Thus, although the current stage-wise QC schema ensures compliance with quality standards, it’s questionable whether it can identify root causes or implement proactive QC at the production level. This study presents a narrative review of QC approaches in SCTM, examining the production chain across key quality domains, including billet integrity, extrusion tooling condition, dimensional control, surface and internal defect detection, annealing atmosphere monitoring, and inner-surface cleanliness. The industrial practices are critically compared with research approaches in numerical modelling, advanced sensing technologies, and data-driven monitoring methods. Results confirmed that dimensional instability, defect formation, surface contamination, and microstructural variation in the tube are influenced by interactions among factors such as billet quality, thermomechanical conditions during extrusion and drawing, annealing conditions, tooling conditions, lubrication regimes, and handling between processing steps. Their analysis indicated that the main limitation of current QC frameworks is not the lack of monitoring or modelling technologies but the limited integration of process data across the manufacturing chain. Full article

Accurate prediction of cutting force is essential for process optimization and intelligent control in CNC turning, yet cross-material performance comparisons of machine learning models remain limited. This study develops and applies a structured diagnostic benchmarking framework to evaluate ten supervised regression models for cutting force prediction across five engineering alloys: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Stainless Steel 304 (annealed), and Carbon Steel 1020 (annealed). The input space included material category together with machining descriptors (diameter, feed rate, and axial distance from the chuck). Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), cross-validated stability metrics, and pairwise dominance probability matrices derived from R² and CV(RMSE). Gradient Boosting achieved the highest overall accuracy and robustness, with a mean R² = 0.962 and RMSE = 18.03 N, followed by a feedforward neural network (R² = 0.953, RMSE = 19.96 N), while Support Vector Regression showed substantially lower performance (R² < 0.65; RMSE > 54 N). Residual diagnostics indicated that ensemble and neural models produced compact, near-homoscedastic error distributions, whereas linear and single-tree models exhibited systematic bias and heteroscedasticity. Principal Component Analysis revealed that the first two components captured 78.7% of the total variance, separating geometric and spatial effects from feed-driven variability. The proposed evaluation framework provides a unified methodology for accuracy, and multivariate interpretation in machining force prediction. These results offer practical guidance for selecting robust learning models in intelligent CNC systems and data-driven manufacturing environments. Full article

Wire Arc Additive Manufacturing (WAAM) has gained attention as a metal additive manufacturing process producing complex large-scale components with high deposition rates and lower costs. Cold Metal Transfer (CMT) offers reduced heat input and enhanced control of metal transfer, making it suitable for aluminium. This review analyses CMT-based WAAM with a focus on Al–Si alloys, providing a synthesis for this material system and establishing a structured comparison of representative studies on process fundamentals, arc mode variants, and key processing parameters. The influence of electrical and kinematic parameters and thermal management on process and geometrical stability, microstructural evolution, defect formation, and mechanical behaviour is discussed. Process behaviour is governed by the temporal distribution of heat input within the CMT cycle and thermal history. Control of heat input can reduce porosity, microstructural heterogeneity, and geometric instability, while advanced CMT modes can improve process stability and material efficiency under appropriate process configurations. Mechanical performance depends on the interaction between process parameters, microstructure, and defects, leading to variability and anisotropy. Despite progress, challenges related to process repeatability, narrow processing windows, defect susceptibility, and predictive capability remain. Future research should focus on parameter optimization, integrated modelling, real-time control, and WAAM-specific alloys to enable reliable industrial implementation. Full article

This work presents a case study detailing an end-to-end workflow for reconstructing a damaged plastic component when no original design data are available. The approach integrates microscopic inspection of fracture surfaces, selective enhancement of 3D scan data, CAD-based modification of geometrically and functionally critical features, and continuous fibre-reinforced additive manufacturing. The component examined functions as a structural mounting element in an automotive lighting module, where it maintains correct alignment and provides mechanical support in service. The study concentrates on the cost-effective replacement of unique parts produced in very small batches. The results indicate that this fracture-analysis-informed reverse engineering strategy offers a practical solution for reproducing low-volume, custom, or replacement components in situations where standard manufacturing methods are not economically viable. The reconstructed part matched the geometry necessary for installation in the original assembly and successfully passed initial functional checks; however, this study did not include quantitative measurements of mechanical performance. Full article

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic sampling of measurement poses, or computationally expensive global optimization procedures, which collectively limit their effectiveness in industrial environments. This study presents a unified identification framework that overcomes these limitations; it incorporates 3D offset parameters to enhance the decoupling of true geometric errors from non-PIGEs, an observability-driven measurement pose selection strategy to maximize the parameter sensitivity, and an ANN-surrogate model to accelerate high-dimensional global optimization. A genetic algorithm is used to optimize the measurement points based on the observability index of the machine tool. The ANN-surrogate model enhances the identification accuracy of error parameters (11 PIGEs + 3 offsets) through precise kinematic models, global exploration, and final refinement. Experimental validation on a five-axis machine tool demonstrates a volumetric error reduction of 88.615% after compensation, with RMSE decreasing to 0.4337 μm. Sensitivity analysis reveals that PIGEs contribute up to 75.26% of the total inaccuracy, while offset parameters capture 24.74% of the error from thermal and non-PIGE sources. The results confirm the method’s superiority over other techniques in terms of identification accuracy, efficiency, and robustness, providing a practical solution for high-precision applications in the manufacturing industries. Full article

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

In the fabrication process of pipelines for petrochemical and other industries, weld reinforcement is often excessive and adversely affects subsequent processes such as anticorrosion treatment and surface coating. Weld reinforcement must be removed through a grinding process. Welding deformation and fit-up errors often lead to highly irregular weld geometries, which makes robotic grinding difficult and causes the task to still heavily rely on manual operation. To address this issue, this study proposes an automatic weld recognition and grinding trajectory planning method based on 3D visualization and deep learning. A weld recognition network, termed WSR-Net, has been developed based on an improved PointNet++ architecture with a cross-attention mechanism, achieving a segmentation accuracy of 98.87% and a mean intersection over union of 90.71% on the test set. An intrinsic shape signature (ISS) key point selection algorithm with orthogonal slicing-based pruning optimization is developed to robustly extract key weld ridge points that characterize the weld trend on rugged weld surfaces. According to the height differences between the weld and the adjacent base metal surfaces, the grinding reference surface is fitted using the weld contour through the moving least-squares method. The ridge line points are projected onto the grinding reference surface along the local normal to generate the expected grinding trajectory points. The grinding trajectory that meets the process constraints is generated through reverse layer slicing. Grinding experiments demonstrate that the proposed WSR-Net achieves robust segmentation performance for both planar and curved surface welds. With the reverse layered trajectory planning method, the proposed method enables high-precision automatic grinding of complex spatially curved surface welds. The results show that the final grinding mean error is 0.316 mm, which satisfies the preprocessing requirements for subsequent processes. The proposed method provides a feasible technical method for the intelligent grinding of spatially curved surface welds. Full article

This study investigates the factors affecting surface integrity during the machining of near-β Ti-5553, a critical material in the aerospace and defense industries. Considering this alloy as a difficult-to-machine material, the turning process was examined by analyzing the effects of cutting speed, feed rate, and cooling strategy (dry, conventional, and 30 MPa/High-Pressure cooling) on cutting force, temperature, surface roughness, and residual stress. The primary novelty of this research lies in its integrated approach: rather than evaluating surface integrity metrics in isolation, it simultaneously models interrelated responses to residual stress, cutting temperature, cutting force, and surface roughness under high-pressure coolant (HPC) conditions. Furthermore, it introduces a robust machine learning framework that uniquely applies data augmentation (Gaussian jittering and interpolation) to overcome the conventional constraints of limited experimental machining data, providing a highly accurate predictive tool. The experimental data were expanded using data augmentation methods (Gaussian jittering and interpolation) and modeled using five different machine learning algorithms (Extra Trees, Random Forest, Gradient Boosting, KNN, and AdaBoost). The results revealed that cooling pressure plays a dominant role, particularly in residual stress (importance score: 0.926) and cutting temperature (0.657). It was observed that high-pressure cooling (HPC) reduces thermal gradients, thereby lowering tensile stresses and improving surface integrity. When algorithm performances were compared, the Extra Trees and Random Forest models achieved the most accurate predictions after hyperparameter optimization. Specifically, the optimized Extra Trees regressor demonstrated exceptional predictive capability for residual stress, achieving an accuracy of 98.47%, a remarkably high coefficient of determination (R² = 0.9997), and a minimal Mean Squared Error (MSE = 6.8289). These quantitative results confirm that the proposed machine learning framework provides a highly reliable and precise tool for controlling surface quality in HPC- assisted machining. Full article