Search Results (47)

Electrochemical discharge machining (ECDM) with low machining reaction forces is useful for machining hard and brittle materials, which are required in precision equipment. Lathe-type ECD machines have been proposed to machine axisymmetric shapes while reducing cracks caused by thermal expansion, and they are suitable for thin workpiece machining due to the small reaction force. This paper demonstrates the micromachining of non-circular cross-sections using a lathe-type ECD machine equipped with an improved force-controlled tool electrode holder. The tool electrode holder combining a voice coil motor (VCM) with leaf springs arranged in parallel was built. This holder achieves both flexibility in the longitudinal direction of the tool electrode and high rigidity in the lateral direction. The relationship between the VCM current, tool electrode shift within the tool electrode holder, and thrust force was approximated using a polynomial. Consequently, this device allows for the stable, small contact force required in micromachining. An on-machine shape measurement method was also carried out by combining the tool electrode shift with the motion of an XZ stage. As a demonstration for non-circular cross-section machining, a square cross-section was grooved from a cylindrical glass rod. The removal and measurement processes were alternately repeated to achieve precision. During ECDM, the on/off of the DC power supply for ECDM was synchronized with the rotation of the workpiece. The measurement results indicated some dimensional errors, including bulging at the middle of sides and excessive removal at corners. The bulging was mainly caused by drift due to thermal expansion of the stage, as well as tool electrode wear. Since the tool electrode comes into close proximity to with the machined surface, the discharge from the side surface of the tool electrode caused excessive removal at the corners. Full article

The on-machine measurement (OMM) of aero-engine blades is a critical technology for enabling closed-loop manufacturing. However, when using line laser sensors with a fixed scanning pose to measure free-form surfaces, the variation in surface geometry leads to changing incident angles, which in turn induce non-stationary noise. To address this issue, this paper proposes a multi-sensor fusion method utilizing Adaptive Heteroscedastic Gaussian Process Regression (AHGPR) based on a Spatial-Angle-Balanced Sampling (S-ABS) strategy. The AHGPR explicitly integrates the physical mapping of incident angle errors into its covariance structure, thereby automatically adjusting observation weights according to the local geometric posture. Concurrently, the S-ABS strategy captures the high-error characteristic points with large incident angles while maintaining a globally uniform spatial distribution. The experimental data indicate that this approach addresses the sampling deficiency encountered at the leading and trailing edges and in areas with large incident angles. The proposed approach reduced the impact of optical deviations on measurement accuracy and improved the precision of the process. Full article

With the rapid development of Head-Up Display (HUD) technology for vehicles, optical freeform mirrors, as its core optical components, are crucial for achieving system compactness and high imaging quality. However, their complex surface shapes and large-aperture characteristics pose significant challenges to ultra-precision manufacturing. This study presents a systematic optimization framework for the ultra-precision machining of HUD optical freeform mold cores, integrating surface design, tool path planning, vibration analysis, and process parameter optimization. Firstly, based on the XY polynomial freeform surface model, an off-axis three-mirror HUD system was designed, and the surface parameters and machining dimensions of the mold core were determined. For the Single-Point Diamond Turning (SPDT) Slow Tool Servo (STS) process, a hybrid trajectory planning method combining equidistant projection and cubic spline interpolation was proposed to ensure the smoothness and accuracy of the tool path. Through theoretical analysis and experimental verification, the selection criteria for tool parameters such as tool nose radius and effective cutting angle were clarified, and the mechanistic impact of Z-axis vibration on surface roughness and waviness was quantitatively revealed. Finally, through ultra-precision turning experiments and on-machine measurement, a high-precision freeform surface mold core was successfully fabricated. This validates the effectiveness and feasibility of the proposed process solution and provides technical support for the high-quality manufacturing of HUD optical elements. Full article

On-machine measurement (OMM) enables real-time dimensional feedback in production, yet accuracy is often degraded by thermal drift, sensor noise, and environmental disturbances. This motivates intelligent error-prediction methods to ensure reliable, high-precision machining. This study proposes a hybrid deep learning model integrating a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (Bi-LSTM) network, and a Whale Optimization Algorithm (WOA) for precise OMM error prediction. Initially, raw measurement data underwent preprocessing to remove noise and outliers. Subsequently, we use a CNN to extract features and a Bi-LSTM to model time-dependent patterns. Finally, WOA optimizes model hyperparameters globally, further boosting predictive accuracy. Comparative experiments show that the proposed model reduces RMSE, MAE, and MAPE by approximately 53.58%, 54.96%, and 57.65%, respectively, while improving the R² score by about 11.17% over baseline methods. Results confirm the method’s superior nonlinear prediction capabilities, significantly enhancing machining accuracy and production efficiency, and demonstrating promising industrial application potential. Full article

This study compares one traditional prediction model—the conventional single-variable interpolation method—with two newly developed models: an improved multi-variable interpolation model extended from the single-variable formulation, and a more advanced NURBS-based multi-variable interpolation model. All models are integrated into a real-time volumetric error compensation framework embedded within a CNC controller, enabling in-kernel correction without external hardware. Accuracy verification is carried out using planar body diagonal measurements obtained from a dense on-machine PDGE data grid across the coupling plane. Quantitatively, the improved multi-variable interpolation model reduces diagonal errors by 71%–74%, while the NURBS-based model achieves 82% (T1) and 84% (T2) reductions, delivering an additional 18%–19% improvement relative to the single-variable baseline. The in-kernel evaluation satisfies 2–4 ms interpolation cycles, confirming real-time feasibility. The proposed framework provides a compact, data-driven solution for predicting and compensating cross-talk-induced PDGEs in precision machine tools. Full article

Indexable face milling tools are often used to machine workpieces with large axial and radial depth of cuts, and thus, the inserts quickly wear out in machining. A kernel technique of smart machining is tool wear compensation, which is to regularly and automatically measure the insert radius/length with a laser tool setter on the machine table during machining, and compensate them in the subsequent machining. Another technique is tool condition monitoring, which is to calculate the insert flank wear width for tool condition and compare with its threshold. When it is less than but close to its threshold of invalid inserts, the cutting tool is automatically changed right before it becomes invalid. On-machine laser tool setters have been equipped in CNC machine tools for several years; however, they cannot conduct cutting tool condition monitoring. The main reason is that the insert flank wear width cannot be measured on the on-machine laser tool setter, and the status quo is that the cutting tool is replaced either too early or too late. To address this problem, a novel tool condition monitoring technique of determining the insert flank wear width of indexable face milling tool using on-machine laser tool setters is proposed. According to the insert geometry, the worn cutting edge and a new workpiece milling mechanism proposed in this work, the insert flank wear width can be calculated. In machining, the insert radius wear is measured on the on-machine laser tool setter, and the insert flank wear width is calculated to evaluate whether it is invalid soon. The results indicate that the optimal height for radius measurement is located near the intersection of the corner and side edges point MR₃, and close to the cutting depth point MR₅. A wear land width threshold of 0.10 mm is established to define tool failure. The proposed calculation method achieves high accuracy, maintaining calculation errors within 14.00%. The inserts can be used in good condition with the maximum lifespan. This method has been verified in machining applications and can be directly applied in industry. Full article

Transmission error (TE) is an important parameter in gear dynamics that has a direct impact on the vibration and noise of gears. Under quasi-static conditions, gear elastic deformation and assembly errors amplify with increasing load, potentially contributing to noise and vibration. This paper presents a novel method for measuring the quasi-static transmission error (QSTE) of spur gears under quasi-static conditions. In particular, the study investigates the relationship between quasi-static transmission error, elastic deformation transmission error, and gear tangential acceleration. Gear elastic deformation transmission error was calculated from experimental data obtained with single-point, symmetrical dual-point, and orthogonal four-point configurations of tangential acceleration sensors. The orthogonal four-point sensor configuration greatly improves measurement accuracy when compared to theoretical values derived from material mechanics calculations. A dedicated on-machine acquisition system for spur gear tangential acceleration was constructed. Tangential acceleration tests were conducted across varying loads and rotational speeds. The acquired data underwent filtering and integration processing in order to obtain gear elastic deformation and quasi-static transmission error. The feasibility of the acceleration approach for measuring both gear elastic deformation and quasi-static transmission error is confirmed by a comparative analysis of the acceleration method results with transmission errors obtained via material mechanics calculations and magnetic grating detection. Full article

Large-scale, high-density flatness measurement is critical for manufacturing reference surfaces in ultra-precision machine tools. Traditional methods exhibit degradation in both accuracy and efficiency as measurement points and area size increase. In order to overcome these limitations to meet the requirements for integrated in-process measurement and machining of structural components in ultra-precision machine tools, this paper proposes a novel nine-probe third-order matrix system that integrates the Fine Sequential Three-Point (FSTRP) method with automated scanning path planning. The system utilizes a multi-probe error separation algorithm based on the FSTRP principle, combined with real-time adaptive sampling, to decouple machine tool motion errors from intrinsic workpiece flatness deviations. This system breaks through traditional multi-probe 1D straightness measurement limitations, enabling direct 2D flatness measurement (with X/Y error decoupling), higher sampling density, and a repeatability standard deviation of 0.32 μm for large precision machine tool components. This high-efficiency, high-precision solution is particularly suitable for automated flatness inspection of large-scale components, providing a reliable metrology solution for integrated measurement-machining of flatness on precision machine tool critical components. Full article

The cup-shaped grinding wheels with arc-shaped edges provide a satisfactory precision grinding solution for high-accuracy optical surfaces on hard and brittle materials. However, the complex profile of the arc-shaped edges of cup-shaped grinding wheels makes them challenging to truing. This paper proposes an on-machine truing technique targeting cup-shaped grinding wheels with arc-shaped cutting edge. First, a mathematical model was established to simulate the three-axis of on-machine truing the arc-shaped cutting edge using a diamond roller. Based on this model, a theoretical analysis is conducted to investigate the impact of tool setting errors, measurement errors of the diamond roller, and the pose error on truing accuracy. A compensation method was proposed, and experimental results validated its effectiveness. To investigate the grinding performance of cup-shaped grinding wheels after truing, a complex component is ground using a truing diamond grinding wheel. The experimental results demonstrate that this method enables precise on-machine truing of the arc-shaped edges of cup-shaped grinding wheels and is efficient. The average dimensional accuracy of the grinding wheel’s arc-shaped edge is reduced to 1.5 μm, with the profile accuracy (PV) of 0.89 μm. Full article

On-machine measurement is a highly effective approach for enhancing machining accuracy and efficiency. A critical factor influencing the accuracy of on-machine measurements is the straightness error of the linear guideway. However, this error is significantly affected by environmental factors such as temperature, vibration, and gravity deformation. To improve the measurement accuracy of machine tools, this study investigates the impacts of these factors on straightness errors and proposes an innovative separation and compensation model for linear guideway straightness. A thermo-mechanical coupling simulation is employed to establish a model that quantifies the influence of thermal errors on straightness. The results demonstrate that thermal gradients cause the straightness error to bend to varying degrees, depending on the temperature distribution. Furthermore, a vibration error model is developed, revealing that the vibration period is approximately twice the ball diameter. Notably, vibration errors can be effectively mitigated using a band-stop filter to eliminate the corresponding frequency components. The study also addresses the effect of gravity deformation, comparing the deformation under different support conditions, highlighting the significance of precise support positioning. Through experimental validation of the straightness error separation and compensation model, it is shown that the straightness error of a conventional linear guideway can be reduced by 95%, and the compensated straightness error is less than 0.2 μm. This novel approach not only improves the accuracy of on-machine measurement but also provides valuable insights for optimizing machine tool performance under dynamic operating conditions. Full article

This study developed a movable, high-precision laser measurement system for assessing wear on cup grinding wheels. The proposed setup employs a Keyence CL-P070 laser sensor with a resolution of 0.25 μm and has a simple installation process that supports flexible deployment on multiple workstations. Unlike traditional static configurations, the compact design requires minimal adjustment and enables versatile positioning across operational environments. An automated measurement procedure was developed that can capture changes in the grinding wheel profile between grinding cycles. The experimental results indicate that the proposed system has high repeatability and accuracy in detecting the subtle progression of wear in cup grinding wheels. The proposed approach provides a user-friendly on-machine measurement solution that can improve quality control and operational efficiency in industrial grinding processes. Full article

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques. Full article

The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. This study proposes an automatic on-machine measuring apparatus for assessing inner diameter, straightness, and roundness errors. Based on the axial-section measurement principle, an integrated measuring module was designed, including a self-centering mechanism, a diameter measuring sensor, and a positioning reference sensor, all embedded within a control system. On this basis, calculations of the inner diameter, and evaluations of the straightness and roundness errors are presented. Experimental verification is conducted on a blind deep hole with a nominal 100 mm inner diameter and 700 mm depth. Compared with measurements performed on a coordinate measuring machine (CMM), which is limited to a maximum hole depth of 300 mm, the proposed apparatus achieved full-depth on-machine measurements. Meanwhile, the measurement results were consistent with the data obtained by the CMM. The straightness error is considered less than 0.05 mm, and the roundness error is considered less than 0.015 mm. Ultimately, without requiring any additional reference platform, the proposed apparatus shows promise for measuring deep-hole parts on various machine tools, with diameters of no less than 80 mm and theoretically unlimited hole depth. Full article

In modern manufacturing, there is an increasing demand for reliable in-process measurement methods directly on large CNC machine tools, eliminating the need to transport workpieces to metrological laboratories. This study assesses the capability and applicability of an articulated arm coordinate measuring machine and a machine tool touch-trigger probe when measuring to a specified tolerance of 0.05 mm in a production environment. Experiments were conducted using the KOBA calibration standard and included measurements with and without applying the articulated arm coordinate measuring machine leapfrog method. The results were evaluated according to ISO 22514-7:2021 and ISO 14253-1:2017, which establish criteria for measurement system capability. The findings revealed that neither measurement system met the capability requirements of ISO 22514-7:2021, particularly due to unsatisfactory Q_MS and C_MS values. However, under ISO 14253-1:2017, both systems were deemed conditionally suitable for verifying conformity to the specifications, with the articulated arm coordinate measuring machine showing lower applicability when using the leapfrog method. This research supports the idea that unreasonable demands for compliance with current standards may lead to questioning of the systems that previously met older standards. The study contributes to the ongoing discussion on integrating advanced metrological tools into the manufacturing process and underscores the need for careful evaluation to ensure the capability and reliability of measurement systems in industrial practice. Full article

Metal mirrors will be widely used in the coming decades. Therefore, as one of the enabling technologies for metal optical freeform surface manufacturing, ultra-precision (UP) diamond turning error compensation has become a research hotspot. However, for the tool offset error and tool radius error, which are the main errors in UP diamond turning, no precise and efficient calculation method has been found in the literature. In this study, a more precise and efficient algorithm was developed and validated in three ways using on-machine measurement data and profilometer measurement data. After one compensation, the tool offset error can be reduced to below 0.1 μm, and the tool radius error can be reduced to below 1 micrometer, which will significantly improve the UP turning accuracy and efficiency of optical parts. Full article