Search Results (187)

In conventional CNC machining, the workpiece clamping pose is registered with a preset CAD model under multiple geometric constraints to establish the machining reference frame. The tool path, generated from this model, is subsequently used to produce components of identical geometry. However, this paradigm proves inadequate when a final shape must accommodate morphological variations specific to each individual blank. Manual grinding, as an alternative, is not only inefficient and hazardous but also relies heavily on subjective quality assessment. To address these challenges, we propose an adaptive local-region milling strategy tailored for blanks with similar yet non-identical surface morphologies, enabling the finished geometry to adjust dynamically to each workpiece. Under conditions of under-constrained clamping, visual positioning is first employed to automatically locate the target regions. Line laser scanning is then conducted over the planned area to acquire high-density point clouds. Through segmentation, points lying outside the region to be machined are extracted, from which a theoretical post-machining surface is reconstructed. Milling toolpaths are subsequently planned based on this reconstructed model to compensate for surface variations across different blanks. Experimental validation on a three-axis CNC milling machine demonstrates that the proposed adaptive strategy effectively replaces manual grinding by removing the bulk of the machining allowance from locally variant surfaces. With the reconstructed model serving as the reference, 77.1 percent of the machining errors fall below 0.055 mm. These results confirm that the method yields a smooth and level surface finish, thereby meeting the fundamental requirements for such adaptive machining tasks. Full article

Opuntia ficus-indica (L.) Mill. is a crassulacean acid metabolism (CAM) xerophyte whose intraspecific morphological diversity remains poorly documented in Andean agroecosystems. This study characterized the phenotypic diversity of 55 accessions collected along an altitudinal gradient in the dry inter-Andean valleys of northern Ecuador. Twenty morphological descriptors were used to analyse the data. Quantitative descriptors showed moderate to high variability, with reproductive and defensive characters exhibiting greater variation than vegetative descriptors. MCA revealed substantial chromatic diversity, with fruit peel and pulp color emerging as the strongest qualitative discriminators among morphotypes. Cluster analysis identified three statistically distinct groups: a small-fruited, highly spinescent morphotype restricted to the most arid sites; an intermediate and phenotypically diverse morphotype distributed across all provinces; and a large-fruited, low-spinescence morphotype with the highest seed numbers, consistent with a more advanced domestication trajectory. The primary differentiation axis reflected a trade-off between spine investment and reproductive output. Five discriminant descriptors—fruit weight, spine length, seed number, peel color, and pulp color—provide a robust baseline for germplasm characterization and support conservation, breeding, and nutraceutical valorization in Andean drylands. The findings of this research contribute to filling a critical knowledge gap regarding Andean cactus pear germplasm, providing a scientific basis for conservation, sustainable management, and future breeding and agro-industrial initiatives. Full article

The end-effector accuracy of a multi-axis milling system is primarily affected by assembly errors and deformation errors induced by low structural stiffness. This accuracy exhibits spatially nonlinear and non-uniform variations with changes in system pose, especially in robotic milling systems. Therefore, full-space accuracy modeling that accounts for manufacturing and assembly processes is crucial, particularly for machining workspace optimization. However, existing assembly deviation models are generally limited to error fluctuation simulations under fixed poses and lack the capability to analyze accuracy variations across the entire motion space of kinematic mechanisms, often requiring remodeling for different poses. To address this issue, this paper proposes a full-space geometric variation propagation modeling method for multi-axis robotic machining systems, considering local parallel chains. In the proposed model, the effects of manufacturing tolerances of multiple axes and their propagation on the geometric accuracy of a multi-axis milling system are considered in the spatial domain during the milling motion process. Firstly, three-dimensional tolerance expressions of joint and shaft-hole features are defined using small displacement Torsors, which can represent small feature variations within their tolerance ranges. Then, feature-to-feature Jacobian matrices are defined to characterize geometric variation propagation in multi-axis assemblies. Consequently, an overall Jacobian–Torsor-based expression model can be generated through the construction of a dimensional chain diagram. Based on the proposed model, case studies are conducted on a multi-axis robotic milling system to validate its effectiveness in modeling geometric variation propagation. The proposed method provides a comprehensive understanding of the mechanism of geometric variation propagation in robotic milling processes. Full article

In the current context of the manufacturing industry, optimizing the cutting parameters to achieve a controlled surface roughness involves costly and time-consuming experimental efforts. The present study addresses this challenge by developing robust machine learning-based approximate functions for predicting surface roughness (Ra) resulting from toroidal milling on a five-axis CNC. The research includes an experimental design conducted under real production conditions on C45 steel. The relatively small experimental dataset was augmented, normalized, and then scripts were written for four prediction models: two artificial neural network architectures and two models based on decision trees. Their performance was analyzed based on MSE, RMSE, R², and MRA metrics. The results obtained reveal significant differences between the models, highlighting solutions with high accuracy, excellent robustness, and superior generalization capacity for new data. The study highlights the high potential of prediction models in optimizing machining processes, providing an effective way to reduce costly physical experiments and increase productivity in industrial environments. Full article

To address the pervasive engineering challenges of rigid interference and subpar machining efficiency encountered during the complex freeform surface polishing of P20 mold steel, this study proposes and fabricates a structurally designed, five-petal composite compliant polishing tool via fused granulation fabrication (FGF). The tool structurally integrates a passive thermoplastic polyurethane (TPU) compliant buffer layer with an active PA66/diamond micro-cutting functional layer, achieving monolithic precision assembly through dual-temperature-zone 3D printing. Tensile mechanical characterization (n = 6) reveals that the composite interface attains an average ultimate tensile strength (UTS) of 59.39 ± 15.41 MPa (with a peak of 78.90 MPa) and an average elongation at break of 27.42 ± 7.41%, demonstrating exceptional structural robustness and fracture toughness under heavy-load abrasive machining conditions. During adaptive polishing validations on complex convex topographies and deep concave mold cavities, the compliant tool effectively compensated for normal vector spatial errors intrinsic to three-axis CNC machining via passive geometric adaptation. Topographical evaluations suggest a ductile-regime, differential asperity planarization material removal paradigm, which is attributed to the macroscopic 3D elastic deformation of the tool synergized with the proposed compliance of the polymer matrix. Following high-intensity sequential polishing regimens, the original macroscopic milling striations were substantially reduced. Quantitative profilometric analysis reveals that the average surface roughness of the convex profiles decreased from an initial 13.33 µm to 7.42 µm, while that of the restrictive deep concave features was reduced from 10.84 µm to 4.11 µm. Ultimately, this technological framework circumvents the traditional reliance on capital-intensive, six-degree-of-freedom robotic platforms, providing a scalable automated polishing protocol compatible with standard CNC systems for the cost-effective surface planarization of precision molds. Full article

Smart manufacturing requires real-time monitoring of multidimensional forces at the interface between the tool and workpiece in computer numerical control (CNC) machining. In this study, an innovative iKIT wireless extrasensory toolholder is introduced that is capable of high-fidelity, in situ, high-frequency sensing and monitoring of the cutting force, torque, and two-way bending moments. The hardware design of the system is outlined, highlighting a high-bandwidth miniature wireless transmission method and noncontact power supply and energy storage solution suitable for rotating machining environments. To assess the system performance, comprehensive milling tests were performed on aluminum alloy materials, and the relationship between the process parameters and changes in multidimensional mechanical loads was thoroughly examined. The experimental findings demonstrate that the smart toolholder detects precisely how parameter variations affect the loads. Multidimensional mechanical signals (torque and two-way bending moments) show a strong positive correlation with the feed rate and axial depth of cut, confirming the impact of the material removal rate on the system loads. Conversely, these signals are negatively correlated with spindle speed, accurately reflecting the effects of thermal softening and a reduced friction coefficient in aluminum alloys during high-speed cutting. This study not only offers a dependable hardware framework for integrating miniaturized sensors into toolholders, but also delivers accurate data to support digital twin models and adaptive control in machining processes. Full article

Spindle-current-based force estimation provides a nonintrusive alternative to dynamometer-based milling-force measurement, but its accuracy is limited by the nonlinear and time-dependent relationship between spindle current and cutting force. This study proposes a CNN–ResNet–RF model for instantaneous milling-force prediction using only spindle current signals as input. In the proposed architecture, CNN layers extract local temporal features from windowed current sequences, residual blocks refine multiscale force-related representations, and a random-forest regressor performs nonlinear force regression. Milling experiments were conducted on 7075 aluminum alloy and steel 45 using a five-axis machining center. To prevent temporal data leakage, the synchronized and preprocessed current–force data were divided at the continuous cutting trial level into training, validation, and independent test subsets. On the independent test subset, the proposed model achieved an R² value of 0.952, an MAE of 2.793 N, and an RMSE of 4.301 N, outperforming the CNN, CNN–ResNet, and RF baseline models in terms of prediction accuracy and error reduction. These results demonstrate that the CNN–ResNet–RF framework improves test-set milling-force prediction within the tested machining range. Full article

Five-axis CNC machining involves strong coupling among machining quality, material removal efficiency, and operational safety, making it difficult to obtain adaptive and feasible process parameters using conventional scalar-objective optimization methods. To address this problem, this study proposes a physics-constrained multi-objective multi-agent deep deterministic policy gradient framework, termed MOMADDPG, for Pareto-oriented optimization of five-axis machining parameters. A data-calibrated digital twin simulation environment is constructed to model five-axis kinematics, tool-workpiece engagement, cutting force, chatter tendency, spindle power, tool wear, actuator bounds, and collision risk. The PHM Society 2010 milling dataset is used to calibrate the cutting force and tool wear sub-models, while five-axis motion, tool orientation variation, and engagement conditions are generated within the digital twin environment. In the proposed framework, three heterogeneous agents are assigned to quality preservation, efficiency improvement, and safety assurance, respectively. A hierarchical attention Actor is designed to enhance feature extraction under partially observable machining conditions, while vector-valued dual Critics preserve objective-specific value information. Physical constraints are handled using adaptive Lagrangian multipliers, and a Pareto archive-guided preference curriculum is introduced to improve the diversity of feasible non-dominated solutions. Simulation results show that MOMADDPG achieves a task success rate of 98% and a hypervolume value of 0.674 after training. Compared with representative baselines, including DQN, MADDPG, MAAC, and MAPPO, the proposed method provides better Pareto-front approximation, higher task feasibility, and stronger robustness under process perturbations in the data-calibrated five-axis simulation environment. The results demonstrate the potential of combining digital twins and multi-objective multi-agent reinforcement learning for safe and adaptive parameter optimization in five-axis machining simulations. Further validation on physical five-axis CNC systems is still required before industrial deployment. Full article

This work presents a systematic investigation on dry milling of TC18 forged alloy using longitudinal ultrasonic vibration assistance. The effects of key parameters (cutting speed, feed per tooth, cutting depth and ultrasonic amplitude) on three-axis cutting forces, cutting temperature and surface quality are explored, and orthogonal experiments are conducted to determine the optimal parameter combination. Results reveal that increasing ultrasonic amplitude reduces cutting temperature by 31.8% and suppresses cutting forces effectively. Cutting depth and feed per tooth act as major influencing factors; the three-directional cutting forces drop by 31.1%, 56.7% and 22.9%, respectively. Surface roughness rises to 0.435 μm and 0.29 μm with growing feed per tooth and cutting depth, and decreases to 0.24 μm at higher cutting speeds. Under ultrasonic assistance, roughness increases slightly first and then declines remarkably. A threshold value exists for ultrasonic amplitude, and periodic tool–workpiece contact transforms strip textures into fish-scale morphologies. Proper parameter matching for ultrasonic milling lowers cutting forces and temperature, and improves surface quality of TC18 alloy. This study offers experimental data and theoretical references for relevant machining research. Full article

As a prominent high-efficiency metal cutting process, plunge milling has found increasing applications in the rough machining of integral impellers. However, challenges arise due to the time-consuming process of avoiding interference-induced overcutting at the blade-root fillet, leading to excessive residual material. Consequently, the full potential of plunge milling’s high-efficiency advantages is constrained. To address these issues, a method to avoid overcutting caused by cutter interference at the blade-root fillet in integral impeller plunge milling is proposed. First, a parameterized model of the blade-root fillet is established using a rolling ball model. Second, a semi-analytical model for identifying cutter interference at the blade-root fillet is established through micro-element discretization. Lastly, the cutter position is adjusted along the direction of the vertical cutter axis vector to avoid overcutting. The modeling error of the blade-root fillet remains within 0.1%, ensuring high accuracy in overcut detection. Furthermore, the identification process is completed in less than 1s, demonstrating its computational efficiency. Compared with the conventional depth-reduction method, the proposed interference elimination strategy reduces the excessive residual material volume by 66% while avoiding overcutting, with only a 26% increase in plunge roughing time. Simulation and experimental validation on an 820 mm diameter impeller confirm the method’s effectiveness in balancing interference avoidance and material removal efficiency. Full article

Objectives: Existing nursing competency instruments are generally designed for broad nursing populations and may not fully capture the observable, task-linked, and developmental requirements of newly recruited nurses in maternal and child health (MCH) hospitals. This study developed the Newly recruited nurses’ Competency Framework for Maternal and Child Health Hospitals (NCF-MCH) and preliminarily validated its corresponding self-assessment tool. Methods: Guided by Benner’s novice-to-expert perspective and Mills’ reconceptualised competency terminology, the framework was developed through a structured literature and framework review, item mapping, two-round Delphi consultation, and pilot testing. A cross-sectional survey was conducted among newly recruited nurses from four MCH hospitals. Exploratory factor analysis, confirmatory factor analysis, and reliability and validity analyses were performed. Results: The final framework included six domains and 70 items. The scale showed high internal consistency (Cronbach’s α = 0.944), but weak total split-half reliability (0.585). Exploratory factor analysis using principal axis factoring with Promax rotation suggested a six-factor solution explaining 59.01% of the variance. Confirmatory factor analysis showed acceptable absolute fit (χ²/df = 1.675, SRMR = 0.0595, RMSEA = 0.055), whereas incremental fit was marginal (TLI = 0.861, CFI = 0.866). Convergent and discriminant validity analyses provided preliminary support for the multidimensional structure. Conclusions: The NCF-MCH provides a context-specific framework and corresponding self-assessment tool for describing self-perceived competency among newly recruited nurses in MCH hospitals. It may inform professional transition programs and competency-based training, but further external, longitudinal, and objective validation is required. Full article

With the rapid development of high-end equipment manufacturing, the number and size of complex surfaces continue to increase. Five-axis machining has become the dominant machining method. Effective prediction of cutting force and stability is of great significance for improving machining efficiency and quality. However, due to the complex and time-varying cutting geometry in five-axis machining of complex surfaces, low prediction efficiency has become a key issue restricting the research and engineering application of cutting force and stability. To address this issue, this study introduces the concept of dimensional compression and establishes an efficient prediction model for cutting force and stability. Each tool position along the tool path is discretized into inclined plane milling based on finite difference, thereby simplifying the research object. The tool twist angle and feed deflection angle are defined to describe the spatial relationship in five-axis machining. Using these two angles as new basis variables, a compressed space is constructed, and a mapping relationship between tool position and spatial point sets is established, further reducing the dimensionality of the research object. The cutting edge contact interval is determined using the spatial constraint method. Based on the full discretization method, the cutting force and stability of inclined plane milling are predicted, and the results are uniformly stored in the compressed space to form a sample point library. Consequently, the prediction process of complex surface five-axis machining is transformed into a process of sample point retrieval, significantly improving computational efficiency. Cutting force and vibration experiments in five-axis machining of complex surfaces are conducted. The results show that the predicted results are in good agreement with the experimental measurements, validating the accuracy of the proposed model and demonstrating its capability to guide practical machining. Full article

In the measurement of volumetric errors in CNC machine tools using multi-station laser tracking technology, the coordinate calibration accuracy of external measurement base stations is a key factor determining the system’s final accuracy. Traditional calibration approaches typically use the commanded positions of the machine tool directly to inversely determine base station coordinates, which results in strong coupling between inherent geometric errors and base station parameters. Consequently, the measurement accuracy cannot be properly evaluated, and metrological traceability of the results remains difficult to achieve. To address this issue, this paper proposes a novel calibration principle based on an independent external physical standard and develops a base station calibrator independently. This device employs a precision turntable, G5-grade precision spheres, and electromagnet groups to construct an equivalent target with four feature points at the spindle end. Verified by a high-precision coordinate measuring machine (CMM), the maximum difference in repeated calibrations of the device is 1.4 µm, indicating its excellent positioning repeatability. The calibrator was further applied to measure the positioning errors of a CNC milling machine, and comparative experiments were performed with a Renishaw XL-80 laser interferometer. The results indicate that the error variation trends obtained from the two measurement principles are highly consistent. In both the X-axis and Y-axis directions, the maximum deviations of linear errors are controlled within 3.7 µm, while the maximum deviations of angular errors remain within 4.6 µrad. Furthermore, the reliability of the system data was confirmed through an uncertainty analysis. The external physical standard developed in this study ensures that base station calibration accuracy is not affected by the inherent errors of the machine tool, providing a novel and reliable scheme for high-precision calibration and metrological traceability of machine tool spatial errors. Full article

Hybrid CNC and additive manufacturing platforms often rely on host-assisted or otherwise overdimensioned control architectures to achieve deterministic multi-axis motion, increasing system cost and complexity. This paper presents a fully microcontroller-based timer–DMA motion execution architecture that eliminates the need for external processors or FPGA-based execution, enabling deterministic multi-axis synchronization under the tested conditions in a simpler, more cost-effective way. The proposed framework integrates motion planning, precise step-time computation, and hardware-assisted pulse generation within a unified embedded control architecture. The main novelty lies in the systematic use of timer and DMA peripherals to offload time-critical pulse execution from the microcontroller core, allowing it to focus on motion planning and precise step-time computation. Unlike segmentation-based approaches, the duration of each individual step is calculated directly without fixed-interval segmentation, enabling high motion resolution while avoiding per-step interrupts that introduce jitter at high motion speeds. The architecture was validated on a hybrid platform capable of both milling and material extrusion. Experimental results confirmed real-time feasibility within practical on-chip memory limits and demonstrated very small interpolation errors caused mainly by timer quantization, comparable to those observed in host-processor-based motion systems. Machining and additive-manufacturing experiments further confirmed stable execution and accurate trajectory tracking under real operating conditions. Full article

Surface quality of machined wood-based panels plays a key role in subsequent processing and product performance; however, its formation during CNC edge milling remains insufficiently understood, particularly for materials with different structural characteristics, including recycled content. This study investigates the influence of feed per tooth, milling strategy, and material structure on surface quality during CNC edge milling of particleboards manufactured from alternative lignocellulosic resources. Six board variants were experimentally produced and machined on a five-axis CNC machining center Morbidelli m100 using a single-edge milling cutter, with feed per tooth varied at three levels and both climb and conventional milling strategies applied. Surface quality was evaluated using a non-contact 3D optical profilometer Keyence VR-6000, and roughness (R_a) and waviness (W_z) parameters were analyzed. The results showed that surface roughness increased with increasing feed per tooth for all materials, with an increase of approximately 30%–70%. Statistical analysis confirmed a significant effect of feed per tooth and material type, while milling strategy and its interaction with material were not statistically significant. Materials with higher surface heterogeneity (CVR_a) showed increased roughness and greater sensitivity to feed. A statistically significant positive relationship was found between surface heterogeneity (CVR_a) and roughness sensitivity (ΔR_a), indicating that materials with higher surface heterogeneity (CVR_a), which likely reflects variability in their internal structure, are more sensitive to changes in feed per tooth. Full article