Search Results (149)

To overcome the issues of excessive cutting force, poor chip segmentation, and premature tool wear during the drilling of Ti-6Al-4V titanium alloy. This study established the cutting edge motion trajectory function and instantaneous dynamic cutting thickness equation for ultrasonic vibration-assisted drilling through kinematic analysis. Based on this, an analytical model of drilling force was formulated, integrating tool geometry, cutting radius scale effects, dynamic chip thickness, and drilling depth. In parallel, a finite element model was constructed to achieve visual simulation analysis of chip deformation and cutting force. Finally, the accuracy of the model was verified through experiments, with a comprehensive analysis performed on how cutting parameters affect thrust force. The findings indicate that the average absolute prediction errors of thrust force and torque between the analytical model and finite element simulations were 7.87% and 6.26%, respectively, confirming the model’s capability to accurately capture instantaneous force and torque variations. Compared to traditional drilling methods, the application of ultrasonic vibration assistance resulted in reductions of 40.8% in thrust force and 41.7% in torque. The drilling force exhibited nonlinear growth as the spindle speed and feed rate were elevated, while it declined with greater vibration frequency and amplitude as drilling depth increased. Furthermore, the combined effect of optimized vibration parameters enhanced chip fragmentation, producing short discontinuous chips and effectively preventing entanglement. Overall, this research provides a theoretical and practical foundation for optimizing ultrasonic vibration-assisted drilling and improving precision hole making in titanium alloys. Full article

This article addresses the monitoring and diagnosis of high-speed spindles (HSM) used in CNC grinding machines, emphasizing the importance of the real-time evaluation of their dynamic behavior during commissioning. Due to the complexity of these dynamic phenomena, especially at high speeds (up to 150,000 RPM), common faults such as bearing wear, imbalance, or misalignment can lead to catastrophic failures and high repair costs. An original method is proposed, based on synchronous envelope vibration analysis (SEVA) using the Hilbert transform, to detect mechanical defects in both low-frequency domains (imbalance, mechanical looseness) and high-frequency domains (bearing faults). The system includes vibration, temperature, and speed sensors, and the experimental protocol involves step-by-step monitoring from 10,000 to 90,000 RPM. Through synchronous FFT analysis and IFFT, critical frequencies and their impacts on machining quality are identified. The method enables the accurate fault diagnosis of new or refurbished spindles under real industrial conditions, reducing downtime and production losses. The method involves both local and remote real-time monitoring and diagnosis using a remote data center protocol. Full article

Titanium alloys (Ti-6Al-4V) are widely used in the aerospace field. However, as a typical difficult-to-machine material, titanium alloys have a low thermal conductivity, a high chemical activity, and a significant adiabatic shear effect. In conventional milling (CM), the temperature in the cutting zone rises sharply, leading to tool adhesion, rapid wear, and damage to the workpiece surface. This article systematically investigated the influence of process parameters on the surface roughness, cutting force, and cutting temperature in the ultrasonic-vibration-assisted milling (UAM) process of titanium alloys, based on which multi-objective optimization process of the milling process parameters was conducted, by utilizing the grey relational analysis method. An orthogonal experiment with four factors and four levels was conducted. The effects of various process parameters on the surface roughness, cutting force, and cutting temperature were systematically analyzed for both UAM and CM. The grey relational analysis method was employed to transform the optimization problem of multiple process target parameters into a single-objective grey relational degree optimization problem. The optimized parameter combination was as follows: an ultrasonic amplitude of 6 μm, a spindle speed of 6000 rpm, a cutting depth of 0.20 mm, and a feed rate of 200 mm/min. The experimental results indicated that the surface roughness Sa was 0.268 μm, the cutting temperature was 255.39 °C, the cutting force in the X direction (FX) was 5.2 N, the cutting force in the Y direction (FY) was 7.9 N, and the cutting force in the Z direction (FZ) was 6.4 N. The optimization scheme significantly improved the machining quality and reduced both the cutting forces and the cutting temperature. Full article

This study investigates the effect of ultrasonic vibration applied in the cutting speed direction on surface quality during face turning of C45 steel. The experiments were performed using an ultrasonic generator operating at a frequency of 20 kHz with an amplitude of approximately 10 µm. The cutting parameters used in the experiments included spindle speeds of 700, 1100, and 1300 rpm, feed rates of 0.1 and 0.15 mm/rev, while the depth of cut was fixed at 0.2 mm. Surface quality was evaluated based on the roughness parameters R_a and R_z, as well as surface topography was observed using a Keyence VHX-7000 digital microscope. The results show that ultrasonic-assisted face turning (UAFT) significantly improves surface finish, particularly in the central region of the workpiece where the cutting speed is lower and built-up edge (BUE) formation is more likely. The lowest R_a value recorded was 0.91 µm, representing a 71% reduction compared to conventional turning (CT). Furthermore, at the highest spindle speed (1300 rpm), the standard deviations of both R_a and R_z were minimal, indicating improved surface consistency due to the suppression of BUE by ultrasonic vibration. Topographical observations further confirmed that UAFT generated regular and periodic surface patterns, in contrast to the irregular textures observed in CT. Full article

In computer numeric controller (CNC) milling machining, air-cutting, where the tool moves without engaging the material, will reduce the machining efficiency. This study proposes a novel methodology to detect and minimize non-productive (air-cutting) time in real-time using spindle load monitoring, vibration signal analysis, and NC code tracking. A logic-based benders decomposition (LBBD) approach was used to optimize toolpath segments by analyzing air-cutting occurrences and dynamically modifying the NC code. Two optimization strategies were proposed: increasing the feedrate during short air-cutting segments and decomposing longer segments using G00 and G01 codes with positioning error compensation. A human–machine interface (HMI) developed in C# enables real-time monitoring, detection, and minimization of air-cutting. Experimental results demonstrate up to 73% reduction of air-cutting time and up to 42% savings in total machining time, validated across multiple scenarios with varying cutting parameters. The proposed methodology offers a practical and effective solution to enhance CNC milling productivity. Full article

This work solves the challenge of separating the eccentricity error of a workpiece installation from the first harmonic of radial runout error of the spindle, which has a crucial impact on improving the machining quality of the workpiece. Firstly, a mathematical model for the synthesized elliptical motion for spindle vibration and eccentricity error is established. Subsequently, a novel separation method combining Particle swarm optimization (PSO) and the least squares method (LSM) is proposed. PSO is applied to determine phase angles, and the least squares method is applied to determine amplitudes, achieving precise error separation. Then, numerical simulations were used to verify the effectiveness and reliability of the proposed method, producing a calculation error of less than 0.07% and high consistency (R² > 0.97). Finally, experimental tests at different spindle speeds, axial distances, and workpieces confirmed the robustness of the method, with a variation in eccentricity error calculation result of less than 0.6%. The results indicate that the installation eccentricity error of the experimental machine tool is independent of the spindle angular velocity and stems from the misalignment of the chuck. This method provides a reliable solution for accurately separating installation eccentricity errors in precision manufacturing. Full article

Titanium alloy thin-walled components are extensively used in aerospace engineering, yet their milling stability remains a persistent challenge due to vibration-induced surface anomalies. This study develops an advanced dynamic model for the face milling of titanium alloy thin-walled structures, systematically integrating axial cutting dynamics with regenerative chatter mechanisms and nonlinear process damping phenomena. The proposed framework crucially accounts for time-varying tool–workpiece interactions and damping characteristics, enabling precise characterization of stability transitions under dynamically varying axial immersion conditions. A novel extension of the semi-discretization method is implemented to resolve multi-parameter stability solutions, establishing a computational paradigm for generating three-dimensional stability lobe diagrams (3D SLDs) that concurrently evaluate spindle speed, cutting position, and the axial depth of a cut. Comprehensive experimental validation through time-domain chatter tests demonstrates remarkable consistency between theoretical predictions and empirical chatter thresholds. The results reveal that process damping significantly suppresses chatter at low spindle speeds, while regenerative effects dominate instability at higher speeds. This work provides a systematic framework for optimizing machining parameters in thin-walled component manufacturing, offering improved accuracy in stability prediction compared to traditional two-dimensional SLD methods. The proposed methodology bridges the gap between theoretical dynamics and industrial applications, facilitating efficient high-precision machining of titanium alloys. Full article

To effectively suppress spindle vibrations in rotating machinery, magnetorheological (MR) dampers, as an ideal vibration control device, have attracted attention. To enhance the vibration damping effect, in the paper, a MR damper vibration with a multi-layered permanent magnet as the magnetic source is designed, and the self-made magnetorheological fluid is used as the damping medium. The mechanical properties of the MR damper were obtained through testing and calculation. On this base, both simulation and experimental methods are used to demonstrate the effectiveness of the multi-layered permanent-magnet MR damper. The simulation results show that the critical speed increases greatly for the first four modes. The experimental results show that the Y-direction displacement decreases greatly, especially at 1800 rpm and at 3400 rpm, after applying the MR damper. The vibration displacement at 1× frequency shows a 69.74% reduction at 2600 rpm and a 65.69% reduction at 3200 rpm in the Y-direction after applying the MR damper. The effectiveness of the multi-layered permanent magnet MR damper in rotor vibration suppression was confirmed. Full article

Cutting force is a critical indicator reflecting the interaction between the cutting tool and the workpiece in machining processes. Conventional measurement methods using dynamometers are accurate but costly and challenging for real-time applications. This study proposes a novel transfer learning-based method for estimating milling cutting forces using only spindle vibration signals without direct force sensors. The proposed approach consists of two stages: First, an autoencoder is trained with measured cutting force data to construct a latent feature space. Second, a target encoder aligns spindle vibration signals to this latent space, allowing the decoder to reconstruct estimated cutting forces. To reflect machining parameters into the learning model, the input dataset was constructed by integrating material type, cutting speed, and cutting direction as additional inputs into each model’s inputs. Experiments were conducted on Ti-6Al-4V and STS316L workpieces under various machining conditions. Under normal conditions, the proposed method achieved an average Pearson correlation coefficient (PCC) of 0.9213 ($F_x$) and 0.9072 ($F_y$). Under abnormal transient conditions, robust performance was maintained, with PCC values of 0.8573 ($F_x$) and 0.9202 ($F_y$). The results demonstrate that the proposed method can effectively monitor cutting forces and reflect changes across a variety of machining environments using only vibration signals. Full article

Carbon fiber reinforced polymers (CFRPs) are significantly vital for industries. However, the drilling process of a CFRP is considered a challenge due to its nature, which causes delamination, fiber pull-out, peel-up, high friction, and a decrease in cutting tool life. Wet drilling is necessary for minimizing defects, and lubricants are very costly. In the current work, ultrasonic-assisted drilling (UAD) with a longitudinal vibration of 39.7 kHz was applied to the drill bit in the feed direction, used for CFRPs, and compared with conventional drilling (CD). Low spindle speeds under 5000 rpm were applied with different feed rates. The morphology, delamination factor, and cutting forces were investigated through the specific input machining parameters for CD and UAD. SEM was applied to study the morphology of the hole entrance and exit as well as the burr heights of evacuated chips. UAD with 39.7 kHz succeeded in minimizing the surface roughness by 50% compared with the surface roughness resulting from CD and could drill high-precision holes for CFRPs with a trade-off concept, besides achieving near-zero delamination (K ≃ 1) in the absence of a lubricant, which is being extended for industrial application. Full article

Sand mold milling plays a critical role in digital mold-free casting, but it is prone to damage such as corner collapse, collapse, and cracks during the machining process. To address this issue, ultrasonic vibration was used for sand mold milling in this study. By incorporating the solid–liquid transition model for sand mold cutting and considering the deformation characteristics of the shear zone, a prediction model for ultrasonic milling forces in sand mold was developed and experimentally validated. The results demonstrate that increasing the spindle speed and decreasing the feed rate lead to a decrease in cutting force. At high speeds, there is a 15% error between the dynamic milling force model and experimental values. Compared with conventional processing methods, ultrasonic processing reduces cutting force by 19.5% at a frequency of 25.8 kHz and amplitude of 2.97 μm, minimizes defects like sand particle detachment pits on the surface of sand mold, significantly improves surface quality, and enables precise, stable, high-precision, and efficient sand mold processing. Full article

In aerospace manufacturing, the reliability of machining equipment, particularly spindle bearings, is critical to maintaining productivity, as bearing health significantly constrains operational efficiency. Accurate prediction of the remaining useful life (RUL) of bearings can preempt failures, reduce downtime, and boost productivity. While conventional BiGRU-based models for bearing RUL prediction have shown promise, they often overlook handcrafted extracted time-series features that could enhance accuracy. This study introduces a novel model, BiGRU-ASSA-iTransformer, that integrates deep learning and handcrafted feature extraction to improve RUL prediction. The approach employs two parallel processes with a fusion step: First, a bi-directional gated recurrent unit (BiGRU) captures dynamic degradation features from raw vibration signals, with an adaptive sparse self-attention (ASSA) mechanism emphasizing short-term degradation cues. Second, 13 time-domain, frequency-domain, and statistical features, derived from traditional expertise, are processed using iTransformer to encode temporal correlations. These outputs are then fused via an attention mechanism. Experiments on the PHM 2012 and XJTU-SY datasets demonstrate that this model achieves the lowest prediction error and highest accuracy compared to existing methods, highlighting the value of combining handcrafted and deep learning approaches for robust RUL prediction in aerospace applications. Full article

Tool Chatter or Self-Excited Vibration is a common issue in machining processes. This phenomenon arises due to various factors, such as tool rigidity, depth of cut, spindle speed, etc., leading to poor surface finish, excessive tool wear, and premature deterioration of machine components. To prevent tool chatter, a real-time chatter detection algorithm was developed using a low-cost accelerometer in combination with internal machine variables. The algorithm operates without requiring a prior model of the specific tool characteristics, making it capable of detecting chatter by simply knowing the number of teeth of the active tool. Furthermore, the implementation of the detection algorithm meets the strict requirements of real-time embedded systems, ensuring high determinism, low latency, and minimal computational cost. This enables efficient and optimal integration into the machine. The developed chatter detection system was validated through machine-based experimental testing. Full article

In manufacturing automation, accurately determining the optimal tool replacement timing is critical yet challenging. Tool condition monitoring (TCM) has been widely studied to address this issue. Cutting force is a key parameter for evaluating tool wear, but conventional force sensors are costly and difficult to implement. This study proposes a cost-effective alternative by estimating cutting forces using spindle vibration data through a long short-term memory (LSTM)-based machine learning model. First, the correlation between cutting force and tool wear is analyzed to emphasize the need for accurate force estimation. Then, vibration data collected from the spindle are used to train an LSTM model, which is effective for time-series data processing. The model is trained with vibration signals from various machining positions, with structured time-series datasets improving performance and generalization. Experimental results show that the developed model accurately estimates cutting forces using short segments of vibration data from a single tool revolution. Additionally, the observed relationship between cutting force and tool wear remains consistent across different machining conditions. This study validates real-time cutting force estimation via spindle vibration monitoring and suggests its potential for tool wear prediction. The proposed method offers a practical, low-cost solution for improving tool condition monitoring in automated machining. Full article

The aim of this paper is to present an approach to condition monitoring of an actuated mechanical system operating in a steady-state regime. The state signals generated by the sensors placed on the mechanical system (a lathe headstock gearbox) operating in a steady-state regime contain a sum of periodic components, sometimes mixed with a small amount of noise. It is assumed that the state of a rotating part placed inside a mechanical system can be characterized by the shape of a periodic component within the state signal. This paper proposes a method to find the time domain description for the significant periodic components within these state signals, as patterns, based on the arithmetic averaging of signal samples selected at constant time regular intervals. This averaging has the same effect as a numerical filter with multiple narrow pass bands. The availability of this method for condition monitoring has been fully demonstrated experimentally. It has been applied to three different state signals: the active electrical power absorbed by an asynchronous AC electric motor driving a lathe headstock gearbox, the vibration of this gearbox, and the instantaneous angular speed of the output spindle. The paper presents some relevant patterns describing the behavior of different rotating parts within this gearbox, extracted from these state signals. Full article