Search Results (273)

Tool wear degrades machining quality and, if unchecked, can progress to breakage, causing workpiece defects, downtime, and spindle damage; automatic tool condition monitoring is therefore essential. In real production, new and reground tools are used interchangeably and differ slightly in geometry and material, so the “normal” baseline shifts from one tool and mounting to the next. This makes both global-baseline diagnostics and deep-learning methods that require large labeled fault datasets difficult to apply. This study proposes a lightweight, self-referencing framework—whose per-tool baseline is built from acceptable-state data alone—for diagnosing milling tool wear under tool-to-tool variability. Its novelty lies not in the individual techniques—self-referencing, order tracking, and the Mahalanobis distance, which are established—but in their integration into a single framework, designed for fault-label-free operation, that rebuilds a dedicated baseline for every newly mounted tool. Whenever a tool is mounted, its own acceptable data, a short initial segment of machining taken as healthy immediately after mounting, form the baseline; kinematics-based order-tracked features from a single spindle-bearing accelerometer are used to compute the Mahalanobis distance from the acceptable state, which serves as a continuous health index. A warning limit set statistically from the acceptable data alone, together with a defect limit set as a pragmatic engineering multiple of it, separates the acceptable, warning, and defect grades. On four end mills of identical specification, the primary full-baseline analysis yielded a warning-detection AUC of 0.986 and a defect-detection AUC of 0.936; with a persistence rule, defect-grade wear was detected in all four tools with no false alarms in the acceptable state. Because the baseline is built from only a short acceptable segment and the computation is inexpensive, the framework is, in principle, suited to shop floors with frequent tool changes and to on-machine or edge deployment (not yet benchmarked on an edge device); the present validation, however, is limited to four tools and a single workpiece material under fixed cutting conditions with accelerated wear, so broader verification remains necessary. Full article

Micro curved-surface parts are widely used in the aerospace, defense, biomedical, and automotive industries, and their growing adoption imposes increasingly stringent performance requirements. Five-axis micro-milling can achieve precision machining of parts with complex shapes. In the micro-milling process, the cutting force is a critical parameter, as it is the main factor causing machining deformation, vibration, and tool wear. Therefore, this study develops a prediction model for five-axis micro-milling forces in the machining of micro complex curved-surface parts. First, four coordinate systems were established for the five-axis milling process, and the transformation relationships among them were derived. A cutter–workpiece engagement (CWE) extraction method based on solid modeling was also introduced. Then, an instantaneous undeformed chip thickness (IUCT) model was established, taking into account tool runout, elastic recovery of the machined surface, minimum chip thickness, and the local radius of the micro ball-end mill. On this basis, a five-axis micro-milling force prediction model was developed. Finally, five-axis micro-milling experiments were conducted on a micro-impeller and a micro-spherical part, and the cutting forces at different cutter location (CL) points were measured. For the micro-impeller blade, the average percentage errors in the X, Y, and Z directions at all selected CL points were below 11.2%; for the micro-spherical part, the corresponding errors were below 14.4%. These results show good agreement between the predicted and measured values, verifying the effectiveness of the proposed model. Full article

Burr formation remains a major challenge in micro milling due to reduced tool dimensions and size-effect phenomena, particularly when machining difficult-to-cut materials such as Inconel 718. This study compares the effectiveness of two abrasive deburring processes, polishing and sanding, applied to micro-milled slots, focusing on burr removal and surface topography modification. Twelve 15 mm long slots were machined using a 400 µm diameter TiAlN-coated micro end mill under constant cutting conditions. Deburring was performed for 1, 2, and 4 min for polishing and for 30 s for sanding. Surface topography and burr height were evaluated before and after each process. The results revealed significant burr formation, with burr heights reaching the same order of magnitude as the axial depth of cut and higher values on the down-milling side. Polishing showed limited deburring capability, primarily promoting burr deformation while reducing surface roughness by up to 21%. In contrast, sanding reduced total height of the burrs by up to 99% and generated flatter edge morphologies, although surface roughness increased by up to 19%. These findings highlight the trade-off between burr control and surface quality in the abrasive post-processing of micro-milled Inconel 718 features. Full article

The large aspect ratio micro end mill is a critical tool for microstructure machining, and its performance directly determines processing quality and efficiency. Diamond coatings are commonly applied to cutting edges to enhance wear resistance and extend tool life. However, existing coating techniques often suffer from poor uniformity and inadequate consistency, limiting batch production and process stability. Aerosol jet printing (AJP) offers a cost-effective and highly controllable alternative for the efficient, large-scale deposition of diamond coatings on micro end mills, where precise control of line spacing is essential to achieving coating uniformity. In this study, a transient numerical model of droplet deposition in AJP is developed using computational fluid dynamics (CFD). The volume of fluid (VOF) method and the discrete phase model (DPM) are coupled to track liquid–gas interface deformation and diamond particle motion, enabling the dynamic evolution of droplet deposition to be captured. The effects of inter-droplet distance on deposition, spreading, coalescence, and line uniformity are systematically investigated. Droplet deposition mechanisms are analyzed under low-speed jetting conditions, while high-speed jetting simulations are conducted to reflect industrial processing scenarios. The results show that under low-speed jetting, droplets undergo spreading, contraction, and rebound, eventually forming a uniform cap-like structure. Under high-speed jetting, droplets exhibit a dispersed ring-shaped spreading pattern; although uniformity is slightly reduced, the spreading area and deposition efficiency are significantly increased. These findings provide a theoretical basis for optimizing AJP process parameters to achieve high-quality diamond coatings on micro end mills. Full article

Tool wear detection is essential for machining quality control and predictive maintenance, but conventional inspection is often manual, time-consuming, and operator-dependent. Existing learning-based visual methods still face challenges in jointly achieving reliable wear-type recognition, accurate wear-region localization, and quantitative wear-width measurement under shop-floor imaging conditions. To address these issues, this study proposes MaskLenNet, a query-based instance segmentation and length prediction network for solid carbide end-milling tool diagnosis. MaskLenNet combines a Swin Transformer backbone, query-based instance-mask prediction, wear-oriented attention, and a key-point head that directly estimates the maximum wear-land width (VB). Evaluation uses 234 images from 54 physical tools under a tool-disjoint split, so different rotations of one tool cannot occur in both training and evaluation sets. On the held-out test set, MaskLenNet achieves 96.52% matched-instance classification accuracy, 95.75% foreground instance mIoU, and a VB mean absolute error of 0.010214 mm. Relative to BEiT-Base, the gains are 3.04 and 3.60 percentage points in accuracy and mIoU, respectively. These results demonstrate promising performance within the evaluated acquisition system; they do not establish equivalence to microscopy or generalization to other machines, optics, workpiece materials, or sites. Full article

To reduce ram deflection and positional deviation at the boring spindle front end of a floor-type milling and boring machine under large extension, a ram-boring spindle assembly was investigated. A finite element model was established based on the actual structure, guideway support, and spindle assembly. Gravity-induced deformation and vertical displacement at the boring spindle front end under different extension conditions were analyzed to identify weak regions. The Solid Isotropic Material with Penalization method was then used to optimize the adjustable internal region of the ram with the objective of reducing structural compliance. The topology–density distribution and the deformation characteristics under different extension conditions were then used to guide an engineering reconstruction of the internal stiffeners, considering casting, assembly, internal space constraints, and engineering experience. Static analysis, displacement testing, and modal analysis were performed to verify the optimized structure. Results show that the maximum total deformation under simultaneous maximum extension decreased from 0.10268 mm to 0.097325 mm, while the mass decreased from 2424 kg to 2363 kg. The measured vertical displacement decreased from 0.114 mm to 0.108 mm. The first six natural frequencies increased by 1.48–5.91%. The proposed reconstruction improves deformation control and dynamic performance while reducing mass. Full article

Frozen sand molds exhibit broad application prospects in aerospace, large-scale complex castings, and high-end equipment manufacturing owing to their high-strength particle-bonding structure and excellent low-temperature stability. However, their brittle–plastic characteristics make them susceptible to collapse, spalling, and load fluctuations during conventional milling, resulting in nonlinear and unstable milling force behavior. To address this issue, a longitudinal–torsional resonant ultrasonic-assisted milling method was proposed, and an instantaneous milling force model incorporating the effective cutting time was established based on the elemental cutting theory and the oblique cutting force model. Through a series of milling experiments, the milling force coefficients at different spindle speeds were calibrated using the average milling force coefficient method. The identified milling force coefficient models exhibited high fitting accuracy, with coefficients of determination ($R^2$) exceeding 0.9. The developed model was then employed to investigate the effects of various machining conditions on the milling forces of frozen sand molds. The relative error between the predicted and experimentally measured average milling forces was calculated to evaluate the prediction accuracy. The results show that the relative errors between the predicted and experimental milling forces in the X-, Y-, and Z-directions were 9.76%, 8.43%, and 8.45%, respectively, all below 10%, demonstrating the reliability and accuracy of the proposed model. Cutting depth and cutting width were identified as the dominant factors affecting the milling force, whereas the ultrasonic-assisted milling process effectively reduced the milling force, with the most pronounced load-reduction effect observed for conventionally prepared frozen sand molds. This study provides a theoretical basis and practical guidance for process optimization and parameter selection for the efficient and low-load machining of frozen sand molds. 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

Reliable fault diagnosis of milling machines under varying operating conditions remains challenging due to distribution shifts caused by speed variations, nonstationary dynamics, and limited labeled data in target domains. Conventional domain adaptation methods often assume equal reliability across samples and neglect the varying physical consistency of signals collected under different conditions. To address this limitation, this study proposes trust-aware domain adaptation network for cross-domain fault diagnosis that integrates physics-guided reliability estimation with deep representation learning. In the proposed framework, physically interpretable global and local features are first extracted from multi-channel vibration signals using energy, spectral, nonlinear, and impulsiveness descriptors. A dedicated Physics Trust Network is then introduced to estimate per-sample trust scores that quantify the physical reliability of each signal based on its physics feature consistency. These trust scores are explicitly embedded into representation learning through a trust-weighted feature encoder, ensuring that physically reliable samples contribute more strongly to the learned latent space. To address distribution mismatch between source and target conditions, a trust-weighted covariance alignment strategy is introduced, enabling domain adaptation to be guided by reliable samples instead of treating all data equally. In this way, the model simultaneously learns discriminative, transferable, and physically consistent features. The entire framework is trained end-to-end using labeled source data and unlabeled target data, enabling effective knowledge transfer under cross-speed conditions. Extensive experiments on a real milling machine dataset collected at different spindle speeds demonstrate that the proposed framework achieves an average accuracy of 98.07%, performing better than two recent state-of-the-art domain adaptation approaches by a significant margin. Ablation experiments further confirm that reliability estimation, trust-weighted representation learning, and trust-guided alignment each contribute independently to performance improvement. Full article

This study investigates the critical relationship between machining-induced surface integrity and the effectiveness of subsequent anti-corrosion protection for high-strength Al 7136-T76511 aerospace alloy. Given the alloy’s susceptibility to exfoliation corrosion, ensuring high-quality surface substrates for protective coatings is paramount. The research aims to model the influence of end milling parameters—cutting speed, depth of cut, and feed per tooth— on surface roughness to establish a topographical risk prognosis framework for subsequent coating vulnerability. A comprehensive full-factorial experimental design involving 150 distinct cutting regimes was evaluated on a CNC machining center. Statistical analysis using ANOVA showed that cutting speed is the most significant factor, contributing 83.89% to the variance of longitudinal Ra. A critical resonance zone was identified between 570 and 610 m/min, where the model predicts high instability and surface integrity degradation. The developed mathematical models achieved high precision, with coefficients of determination (R²) ranging between 85% and 88%. The research identifies a critical “danger zone” of dynamic instability between 570 and 610 m/min, where resonance significantly increases data dispersion (standard deviation = 0.112 µm compared to 0.051 µm in stable regimes). Findings demonstrate that even when average R_a values remain within industrial limits, vibration-induced micro-cracks, and severe chatter marks function as geometric precursors that theoretically lower the structural barrier efficiency of subsequent protective films. This study establishes that prioritizing process stability over nominal roughness minimization is essential for the structural integrity of critical aerospace components. Full article

The cutting variables of end milling are positively correlated with the machined surface roughness, but their mechanism of action remains unclear. Since the cutting tool used in end milling is a three-dimensional solid, under the action of horizontal cutting force, there exist both axial tensile strain and axial compressive strain in the cutting tool at the same time, thus putting forward an assumption about the formation mechanism of machined surface roughness: cutting variables affect the axial tensile strain of the cutting edge through the horizontal cutting force, and cause the cutting-edge tip to vertically wedge into the machined surface, thereby influencing the unevenness of the processed surface. Based on the cutting-tool deflection model of a two-segment cantilever beam and the horizontal cutting force formula, the mathematical expression for the axial strain at the sharp tip of the cutting edge was derived. Groove milling and half-groove milling experiments were done, and the experimental results show that the roughness Ra value in the central area is significantly higher than that in the cut-in area, with its maximum average value being 1.32 times that of the cut-in area. The surface roughness rises following the rise in depth of machining and feed per cutting tooth, but this relationship is significant. The laboratory findings are consistent with the theoretical analysis results, indicating that the assumption about the formation mechanism of surface roughness should be reasonable, and the surface roughness in the central area is greater than that in the cutting tool cut-in area. The research results provide a kind of new insight into the formation mechanism of machined surface roughness, which can serve as a reference for relevant research and cutting practices. 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

The application of robotic milling manipulators demonstrates a promising method for the efficient manufacturing of large-scale structures. However, the cutting accuracy and efficiency of milling robots are predominantly subjected to their low stiffness, which may easily cause chatter during machining. Accurate prediction of chatter stability for robots is of practical importance and is challenging. This paper develops a dynamic model of flexible link elements by considering link flexibility and joint torsional deformation and then constructs a multi-link flexible coupled dynamic model using the receptance coupling substructure analysis (RCSA) method. Subsequently, the equivalent dynamic parameters are identified via the particle swarm optimization (PSO) algorithm. On this basis, the end-effector frequency response functions (FRFs) of the robot under different poses are predicted, and the stability lobe diagram (SLD) for milling is generated based on chatter theory. Finally, the predicted FRFs and stability regions are validated through modal tests and milling experiments. Experimental results demonstrate that the proposed model can predict the end-effector dynamic characteristics and chatter occurrence conditions under different poses, confirming its effectiveness in the analysis of milling chatter stability. Quantitative validation yields a maximum error of 3% for predicted first-order modal frequencies and relative modal amplitude errors below 10%, with experimentally confirmed critical depths of cut of 0.1–0.2 mm at 3000 rev/min and 0.5–0.6 mm at 5000 rev/min. Full article

Tool wear in CNC milling increases friction and torque demand at the tool-workpiece interface, which is reflected in spindle motor current. This study develops a non-intrusive tool wear condition classification method using spindle motor current monitoring during practical CNC milling of commercial medium-carbon steel workpieces (JIS S50C/AISI SAE 1050-equivalent; as-received and non-heat-treated; nominal laboratory hardness approximately 4.3 HRC). Experiments were performed on a Tongtai MDV-508 vertical machining center at fixed cutting conditions (3000 rpm spindle speed, 2 mm axial depth of cut, 5 mm cutting width, and 300 mm/min feed rate) using eight TiAlN-coated fine-grain WC–Co solid carbide end mills (10 mm diameter, four flutes; nominal Co binder approximately 10 wt%). An oil-based HS Highstart/HS-SSHS-BH10 cutting fluid was applied through the machine external coolant nozzle in flood mode at an estimated nominal flow rate of approximately 3 L/min and near-room coolant temperature (25 ± 2 °C), and was used as supplied without dilution. A clamp-type AC current sensor was installed on one phase line supplying the spindle motor, and current was acquired using an NI-9221 module at 20 kHz. Cutting intervals were isolated by envelope-based segmentation, concatenated, and divided into 1 s windows (0.5 s overlap) for feature extraction. Three feature sets were evaluated: time-domain statistics, frequency-domain statistics, and an FFT→PCA hybrid representation. Tool states (New, Mid-life, Old) were labeled using post-process surface roughness Ra thresholds supported by microscope observation. The PCA transformation was fitted only on training data and then applied to the held-out test data. A logistic regression classifier achieved 97.44% test accuracy (152/156 windows; 95% Wilson CI: 93.59–99.00%) with the PCA-hybrid features, outperforming time-domain (89.74%) and frequency-domain (94.87%) models. The results support spindle current monitoring as a low-cost approach for quality-aligned tool condition monitoring, while the external validity remains limited to the tested machine, material, tool, coolant, and cutting-parameter combination. 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