On-machine Measurement Research Articles

AN UPDATE OF THERMAL ERROR COMPENSATION MODEL VIA ON-MACHINE MEASUREMENT

Software compensation is state-of-the-art technology used to reduce CNC machine tool thermal errors, and it belongs to a key intelligent functions of modern machine tools. However, a pretrained and nonadaptive model may not be accurate and robust enough for long-term application. This research presents a transfer function based thermal error compensation model updated via on-machine measurement. A mathematical model is implemented into the machine management software of a large horizontal machining centre to compensate for thermal errors in real time using C#/C++ programming language. The results show that after the thermal error compensation model is updated via on-machine measurement, the prediction accuracy, measured as peak-to-peak values, and the normalized root mean squared error are significantly improved. The prediction accuracy of the compensation model updated via on-machine measurement strongly depends on the sampling interval of the on machine measurements.

An image-based quantitative model of surface measurement complexity for on-machine measurement

An image-based quantitative model of surface measurement complexity for on-machine measurement

Machine Straightness Error Measurement Based on Optical Fiber Fabry–Pérot Interferometer Monitoring Technique

Abstract In this research, we propose an Error Separation Technique (EST) based on optical fiber sensors for on-machine straightness error measurement. Two fiber optic Fabry–Pérot interferometers have been developed serving as two displacement sensors. The displacement distance is computed according to the reflected spectrum from interferometers, which can achieve a sub-micrometer resolution. The two-point method has been employed to separate the straightness error of the slides and the profile error of a fine-polished standard block. The spacing distance between two interferometers is determined by the diameter of optical fibers so that the EST's resolution has the potential to reach the sub-millimeter scale. In the experiment, the straightness error has been measured on a commercially available computer numerical control machine tool, and the measurement has been conducted on its x-axis. The spacing distance between two optical fiber sensors is 1.5 mm which equals the EST's resolution along the machine tool's x-axis. The separated profile error of the measured standard block is around 30 µm which has been verified by a high precision Coordinate Measurement Machine (CMM). The magnitude of the separated straightness error is around 40 µm. This technique is flexible and simple to be conducted, which can contribute to the micro-machine tool calibration and other straightness error applications.

On-machine workpiece straightness profile measurement using a hybrid Fourier 3-sensor method

To compensate for the straightness error of the slide of a machine tool efficiently and precisely, on-machine self-calibrating measurement of the manufacturing error is critical. The Fourier 3-sensor (F3S) method proposed by Fung is promising in measuring the straightness profile of a workpiece accurately on a machine. However, it still suffers from two main challenges: the height difference between the second and the third probes and the stochastic uncertainty, both of which can significantly decrease the measurement precision. In this paper, we counter these two challenges, respectively, and propose the solutions accordingly. First of all, by resorting to the Laplace transform, an algorithm for the F3S method is proposed. Second, the adverse effect of the height difference between the second and the third probes is demonstrated. An approach is presented for estimating the height difference, and compensating for this. Third, to alleviate the stochastic uncertainty, a hybrid F3S method is developed: several F3S measurements are first performed under different probe spacings; then, the optimal Fourier coefficients of the straightness profile are individually selected from the candidate estimates in accordance to the determinant of the transfer matrix. Finally, practical straightness profile measurements were performed, respectively, on a grinding machine by adopting the hybrid F3S method and on a Taylor Hobson surface profiler. The results show that compared with the conventional F3S method, the hybrid F3S method reduced the measurement uncertainty significantly, and the straightness profiles estimated by the hybrid method and by the surface profiler were consistent with each other.

On-Machine Measurement of Profile and Concentricity for Ultra-Precision Grinding of Hemispherical Shells.

The profile and concentricity of hemispherical shells affect the frequency split and quality factor of hemispherical resonators. To compensate for machining errors caused by tool wear and tool setting, an on-machine measurement (OMM) method for the profile and concentricity of hemispherical shells in ultra-precision grinding was developed without the removal of workpieces from the machine tool. The OMM utilizes an inductive lever probe to test the inner and outer surfaces of the shell. A standard sphere is utilized to calibrate the relative position of the inductive lever probe at the two different work positions. To enhance the test accuracy of the OMM, a zero-position trigger-sampling method for the inductive lever probe was developed. It was verified to achieve a stable repeatability accuracy of 0.04 μm when using the OMM to realize a single-point sampling. Hemispherical shells were tested using the proposed OMM method. The concentricity test’s accuracy was verified to achieve accuracy better than 1 μm using a coordinate measuring machine and a standard sphere. The accuracy was 0.26 μm for testing the profiles of the hemispherical shell. The proposed OMM system was integrated with an ultra-precision machine tool. It is hoped that this method can help realize the integration function of machining-measurement-compensation.

Diamond microscraping for the fabrication of a trimmed radial Fresnel array on a roller mold.

Roll-to-roll (R2R) imprinting is a high-throughput and low-cost continuous manufacturing technique for the mass production of high-quality functional optical polymer films. In fabricating optical films, roll molds with high precision and surface finishs are key tooling components in the R2R imprinting process. However, the trimmed radial Fresnel array results in discontinuity of the machining trajectory. Therefore, direct diamond turning of trimmed radial Fresnel array on a roller mold was considered infeasible. We use the diamond microscraping method to manufacture the trimmed radial Fresnel array on the roller mold. The trimmed radial Fresnel array is divided into trimmed and the complete Fresnel structure to be machined separately. The trimmed part adopts the rounded corners method to avoid a damage machined surface. Due to the large size and heavy weight of the roller mold, conventional offline measurement methods cannot be used, so we measure and evaluate its cross-sectional profile by an on-machine measurement method to verify the feasibility of the proposed method. We provide a solution for surface machining of discontinuous and complex microstructures on the roller mold.

Geometric Error Analysis and Compensation in Spherical Generating Grinding of Hemispherical Shell Resonators.

The geometric accuracy of a hemispherical shell resonator (HSR) affects the assembly accuracy and final performance of a hemispherical resonant gyroscope in many ways. During the precision grinding of a resonator, the tool-setting error and wear error affect the form and positional accuracy of the inner and outer spherical surfaces. In this study, a compensation method for generating grinding of the HSR is proposed to address this problem. The geometric errors of the inner and outer spherical surfaces are systemically analyzed and a geometric model of the tool setting and wheel wear is established for generating grinding of the HSR. According to this model, a mapping relationship between the wheel pose and size, form, and positional error of the HSR was proposed. Experiments regarding machining, on-machine measurements, and error compensation were performed using the mapping relationship. The results demonstrate that the proposed method can reduce the radius error of the inner and outer spherical surfaces from 10 μm to 1 μm, sphericity from 5 μm to 1.5 μm, and concentricity from 15 μm to 3 μm following grinding. The form and positional errors are simultaneously improved, verifying the effectiveness of the proposed method.

A MEMS Device Integrating Multiple Cantilever Displacement Sensors to Evaluate Flat Machined Surfaces

Multi-point scanning measurement, which is effective in eliminating motion errors of the stage in on-machine profile measurement, requires multiple displacement sensors of equal pitch to measure displacements simultaneously. However, it is not easy to arrange small sensors with high alignment accuracy when applying the multi-point method at a narrow pitch. In addition, if many sensors can be arranged in parallel, improvement in measurement accuracy can be expected. Therefore, a new micro electro mechanical system (MEMS) device for straightness measurement, one that integrates 10 cantilever displacement sensors, has been proposed. This device can be expected to solve the problem involved in the multi-point method because of the characteristics of MEMS, as the semiconductor processing method can make mechanical structures with high accuracy and it can easily make the device with many identical structures. The device is designed to measure waviness less than 100 μm in height. Ten cantilevers of 11 mm length are fabricated in parallel with 1.8 mm pitch on a side of a base substrate 20 mm square. The strain induced by a displacement of the probe placed near the front edge of the cantilever is detected as a change in the resistance of the piezo resistor at the foot of the cantilever. In the fabrication process of this device, crystal anisotropic etching is performed for 12 hours to form probes 250 μm high. A new fabrication process is also proposed in which a protective process is added to prevent damage to the circuits already formed during the etching. A prototype is investigated, and it is found that the resistance value increases about 0.45% in proportion to the displacement of 100 μm. It is therefore confirmed that this device has the basic ability to detect displacement.

Adaptive sampling method for thin-walled parts based on on-machine measurement

Adaptive sampling method for thin-walled parts based on on-machine measurement

An action-oriented teaching approach for intelligent and energy efficient precision manufacturing

Thermal errors of machine tools are a major contributor to inaccuracies of produced workpieces. Especially, the reduction of thermal errors without increasing the energy consumption of the machine tool and the shop floor due to exact air conditioning has a great social, scientific, and industrial relevance. Therefore, the Institute of Machine Tools and Manufacturing (IWF) at ETH Zürich set up a series of three lab courses to increase the awareness of undergraduate students on this important topic. In the first lab course the students elaborate how the carbon footprint of machine tools can be minimized over the whole live cycle including the manufactured products. Specifically, the students measure the energy demand and analyze the energy efficiency of the most important components of a 5-axis machine tool in different operating conditions. The second lab course focuses on the thermal chain of causes, which describes the physical fundamentals leading to the thermal deformations of machine tools. Temperature and displacement measurements as well as finite-element simulations of a purpose-built test bench visualize the characteristics of the thermal chain of causes. The third lab course completes the topic by dealing with model-based thermal compensation strategies, which enables a shift from resource-based towards intelligence-based reduction strategies for thermal errors. The students evaluate the thermal behaviour of a 5-axis machine tool with an on-machine measurement cycle and learn how to create physical and data-based thermal error compensation models.

Development of an on-machine measurement system with chromatic confocal probe for measuring the profile error of off-axis biconical free-form optics in ultra-precision grinding

Ultra-precision grinding of large-scale free-form optics made of hard brittle material is faced with several thorny problems, such as the grinding process is complex, hard to estimate the wear of the grinding wheel and the on-machine measurement technique of profile error is immature, which seriously affects the rapid convergence of profile accuracy. The on-machine measurement of profile error can effectively avoid the re-installation error introduced into the compensation period, and then the effectiveness and machining efficiency of compensation grinding are enhanced. Therefore, we developed an on-machine measurement system with a chromatic confocal probe for measuring the profile error of off-axis biconical free-form optics in ultra-precision grinding. First, the on-machine measurement system was developed on a 4-axis ultra-precision machine tool, and the scanning principles and position calibration were studied. Then, the scanning strategy was systematically researched, which included the centering approach, scanning path planning, scanning parameter calculation, and the evaluation principle of profile error. Finally, the on-machine measurement experiments were carried out on the off-axis biconical free-form optics with a width of 220 mm and height of 100 mm. The sub-micron variation range of measurement results is achieved by measuring the full-aperture profile error under different sampling parameters. The developed on-machine measurement system and scanning strategy can significantly improve the profile accuracy and machining efficiency of the large-scale free-form optics made of hard brittle material, and reduce the production cost.

Reverse identification of dynamic and static motion errors for five-axis machine based on specimen feature decomposition

The dynamic and static error motions dramatically influence the accuracy of five-axis machine tools. We propose a new approach of reverse identification for dynamic and static motion error of five-axis machine tools through specimen cutting and feature decomposition. The error mapping between the workpiece features and the corresponding errors is established, and a feature specimen is designed accordingly. By measuring the specimen with the on-machine measurement and CMM, 15 static motion errors and dynamics-induced errors can be identified and separated. The error estimates are verified by the direct measurement via the interferometer, autocollimator and ballbar tests. These errors are compensated using a volumetric error model, and the specimen accuracy after compensation is effectively increased, which confirms the feasibility and accuracy of this method.

Ultraprecision machining and on-machine measurement of the radial Fresnel lens structure array of a large-scale roller mold.

Automation and on-machine manufacturing process measurement is essential in increasing product productivity. However, a significant challenge is the radial Fresnel lens structure-array machining and on-machine measurement research for a large-scale roller mold. We present a rotating A-axis machining method for manufacturing an array of a radial Fresnel lens structure, based on an ultraprecision four-axis drum roll lathe system developed by the team. Therefore, based on the principle of a virtual multiprobe, the traditional two-point method is improved and applied to the on-machine measurement of the radial Fresnel lens structure of the roll mold. Through the machining and on-machine measurement experiments of the radial Fresnel lens structure of the roller mold, the feasibility of the machining method and measurement method is verified.

Precision Milling of Integrated Turbine Based on a Non-Contact On-Machine Measurement System

Precision Milling of Integrated Turbine Based on a Non-Contact On-Machine Measurement System

Measurement and evaluation of wheel profiles of a rail vehicle on an underfloor wheelset lathe

Wheel profile form directly affects the dynamic behaviour of rail vehicles. Moreover, it is an important factor to ensure the safe running of a high-speed vehicle. As off-line measurements do not provide high-efficiency and high-accuracy wheel profile measurements, we focused on on-machine measurement and evaluation for obtaining the wheel profiles of a rail vehicle in this study. By combining the wheel machining with the wheel measurement, the measurement and evaluation of radial and axial wheel profiles on the underfloor wheelset lathe were proposed. The measurement of radial wheel profile based on two laser sensors was introduced, and the corresponding error models of on-machine measurement were established. The error models were used to separate the measurement errors from the measurement data. This can improve the accuracy of measuring the radial wheel profile effectively. For measuring the axial wheel profile, an adaptive correction method of profile error was proposed to suppress the measurement error, which was the basis for obtaining an accurate equivalent conicity. By measuring radial and axial wheel profiles, polygonisation evaluation and equivalent conicity were obtained to evaluate the radial and axial dynamic behaviours of a wheelset, respectively. Finally, the measurements were applied to an underfloor wheelset lathe to verify the effectiveness of measuring wheel profiles. In this study, we not only improved working efficiency for the machine tools, but also provided a high-accuracy measurement method for the wheel profiles of rail vehicle.

On-machine measurement method and geometrical error analysis in a multi-step processing system of an ultra-precision complex spherical surface

In the field of ultra-precision machining, the machining accuracy and the surface quality have increased with the demand. However, due to the varying needs of different fields, the requirements for the surface shapes of workpieces are complicated. Therefore, it is necessary to use multi-process processing methods to process complex curved surfaces. However, determining how to ensure the repeated positioning accuracy of multi-process machining has been an insurmountable problem in the field of ultra-precision machining. In this study, a repeated positioning method for the processing of complex spherical surfaces was designed, and the geometric errors were analyzed. This study was based on an ultra-precise five-axis turning and milling machine. First, the processed workpiece was repeatedly clamped and the workpiece was measured by an on-machine measurement method. By processing the detection data, the position error and the pose error after repeated clamping were obtained. Then, using the detection results of the various geometric errors of the ultra-precise five-axis machine tool, random simulation experiments were performed using the homogeneous transformation matrix (HTM) and multi-body system (MBS) methods. A large number of random simulation experiments predicted the theoretical range of errors in the repeated positioning process. Finally, the processing experiment was carried out according to the designed process. The results showed that this approach could effectively solve the problem of repeated positioning in ultra-precision machining. In addition, the results of the machining experiment were in the range of the calculated error theory, which proved that the calculation model was reliable.

On-machine measurement of tool nose radius and wear during precision/ultra-precision machining

On-machine measurement of tool nose radius and wear during precision/ultra-precision machining

Uncertainty Propagation and Assessment of Five-Axis On-Machine Measurement Error

Abstract As an important technology of adaptive machining, on-machine measurement (OMM) is always used to measure the shape of the to-be-cut surface. Due to the multi-source errors in an OMM system, the confidence of the measurement accuracy becomes particularly important before planning the next cutting procedure. OMM is implemented by multi-components involving machine tools, fixture, and on-machine probe. Each component introduces uncertainty of error into the measurement result. The positioning error, alignment error, and eccentricity error are investigated in terms of the expectation, uncertainty, and probability density function. By the kinematics of the components participated in the measurement, these errors are accumulated to affect the measurement results. Thus, the uncertainty of the final measurement result is determined by the accumulation of uncertainty of error sources. In order to evaluate the uncertainty of measurement error, an uncertainty propagation model involving positioning error of five-axis machine tools, alignment error of workpiece, and eccentricity error of the stylus tip is established in terms of the kinematic chain of OMM. Then, the uncertainty of the measurement result in vector form is obtained. Finally, the simulation and the experiment about the impeller are employed to validate the effectiveness and repeatability of the uncertainty propagation model for five-axis OMM.

On-Machine Measurements for Aircraft Gearbox Machining Process Assisted by Adaptive Neuro-Fuzzy Inference System

This paper deals with the development of dimensional control technology for the production of accessory drive train (ADT) gearbox housing, according to the closed door technology approach. The work presents the methodology of the final inspection of bearing seat position deviation by replacing the coordinate measuring machines (CMMs) with a computerized numerical control (CNC) machine and adaptive neuro-fuzzy inference system. The results of the work indicated that correct solutions were obtained. In addition, the technological process of manufacturing is fully automated and performed entirely on the production line.

Self-optimizing thermal error compensation models with adaptive inputs using Group-LASSO for ARX-models

Thermal errors have a significant impact on the machining accuracy of five-axis machine tools. The thermal adaptive learning control (TALC), which combines adaptable data-based models and on-machine measurements, realizes a precise and robust long-term reduction of thermal errors of machine tools. To further improve the precision and the robustness of data-based models for thermal error compensation, this publication introduces a new method to realize adaptive model inputs. This method combines the Group-LASSO (least absolute shrinkage and selection operator) for autoregressive models with exogenous inputs (ARX) and the particle swarm optimization to realize a simultaneous estimation of the optimal inputs, the model structure, and the model parameters. Additionally, the self-optimization ability of thermal error compensation models, based on the TALC, is increased by introducing error-specific action control limits to define the frequency of model updates. The newly developed methods are applied to compensate the thermal errors of a swiveling and a rotary axis of a five-axis machine tool during a long-term test series of 350 h. Randomly generated speed profiles of the linear and rotary axes as well as the spindle and changing ambient conditions ensure a high variety of thermal load cases within in the analyzed long-term test series. The results show that the prediction accuracy measured as peak-to-peak values and the robustness of the thermal error compensation models are improved by up to 36% and 58% respectively when adaptive instead of static model inputs are used. Furthermore, the compensation results of the new method outperform the previously used sequential input selection method regarding prediction accuracy and repeatability. The average peak-to-peak value of the compensated translational thermal errors is reduced by 23% and the repeatability of the corresponding compensation results is increased by 57%. Consequently, the consideration of the resulting model structure during the selection of the optimal model inputs significantly enhances the performance of the resulting data-based thermal error compensation models.