On-machine Measurement Research Articles

Development of an on-machine surface measurement system for an ultra-precision five-axis machine

Ever-increasing demand for complex freeform surfaces machined by ultra-precision five-axis machines necessitates efficient on-machine surface measurement (OMSM) systems. While OMSM offers significant advantages in metrology efficiency and machining accuracy, integrating it with these machines presents a challenge due to complex data synchronization and processing. This paper addresses this challenge by proposing a dedicated controller for synchronized acquisition of sensor measurement data and machine coordinate data. Additionally, a straightforward sensor alignment method is presented. The effectiveness of the proposed OMSM system is demonstrated through successful 2D and 3D measurements, significantly streamlining the traditional time-consuming offline coordinate measuring machine (CMM) and optical profilometer measurements.

Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements

Monitoring cutting forces for process control may be challenging because force measurements typically require invasive instrumentation. To remedy this situation, two new methods were recently developed to estimate cutting forces in real time based on the use of on-machine accelerometer measurements. One method uses machine learning, while another uses a physics-inspired data-driven approach, to generate a model that estimates cutting forces from on-machine accelerations. The estimated forces from both approaches were compared against cutting force data collected during various milling operations on several machine tools. The results reveal the advantages and disadvantages of each model to estimate real-time cutting forces.

Geometric Feature-Based Partial Retrieval of On-Machine Measurement Models

Geometric Feature-Based Partial Retrieval of On-Machine Measurement Models

Accuracy-Monitoring Path Optimization With Error Suppression in Laser Triangulation On-Machine Measurement

Accuracy-Monitoring Path Optimization With Error Suppression in Laser Triangulation On-Machine Measurement

Touch-trigger probe error compensation for nonvertical on-machine measurement of freeform surface workpieces

Touch-trigger probe error compensation for nonvertical on-machine measurement of freeform surface workpieces

Modeling and compensation of enhanced volumetric error of machine tools containing crosstalk errors

The error compensation method is an economical and effective method to improve the machining accuracy of machine tools. Conventional volumetric error modeling of machine tools is based on the rigid body kinematics model using 21 terms of geometric errors and assumes the geometric error of one axis is independent of other axes. However, if the error motion does not satisfy this assumption in serial machine tools, it may significantly influence the overall machine tool accuracy. This paper reveals the existence of mutual crosstalk of angular errors in coupling axes by experiments with equipping the five-degree-of-freedom (5-DOF) embedded sensor module in each axis. A new on-machine measurement method is proposed for detecting crosstalk errors of the coupling axes covering the whole coupling area. It is confirmed that the position-dependent geometric errors (PDGEs) of each axis should be modified by two-dimensional dependence. Therefore, extended from the normal volumetric error model (NVEM) that is commonly used nowadays, a novel enhanced volumetric error model (EVEM) that contains crosstalk's PDGEs is proposed. The real-time error compensation controller (RECC) embedded in CNC system kernel has been developed in collaboration with the vender by writing in the application function. The effectiveness and feasibility of the EVEM and RECC are verified by non-cutting and cutting experiments. Through comparison tests with laser interferometer on body diagonals, the accuracy improving rate of EVEM is about 10 % higher than NVEM. The real-cutting tests on the ISO10791 sample are verified by a CMM and the results show the accuracy improvement of EVEM is better than NVEM by about 20 %.

On-machine measurement method of hole parallelism error based on multi-sensor fusion

The hole parallelism error (HPE) is one of the key quality parameters for mechanical parts. However, HPE can usually be out-of-tolerance due to the coupling effect of residual stress, cutting stress, and geometric structure during its machining process. Thus, on-machine measurement and timely adjustment are important guarantees for HPE. This paper proposes an on-machine measurement method of HPE based on multi-sensor fusion that is low-cost, efficient, and suitable for mass production of parts. First, the axis of the cylindrical surface is directly extracted using five points according to the spatial line position relation. Then, the mathematical model of the HPE of two holes’ axes was established through the theory of spatial analytic geometry. The HPE is characterized using two parameters: parallelism magnitude and parallelism direction. Finally, the feasibility of the measurement method in principle and practical application is verified by simulation and experiment. The simulation tests show that the maximum value of the relative error of parallelism magnitude and parallelism direction is −1.38‱. The actual experiments show that the relative error of the measurement results compared with the coordinate measuring machine is 6.23%. None of the standard deviations exceeds the order of magnitude of 0.01 mm. The difference in measurement uncertainty is 0.0003 mm.

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

Identification and compensation of geometric errors in the machine tool are widely performed to increase machining accuracy. Periodic verification of the geometric errors, as introduced in ISO 230-2, helps monitor the machine tool accuracy during continued manufacturing operation and detect accuracy degradation early on. While interferometry-based error identification techniques are commonly applied, on-machine measurement (OMM), with its increasing availability to machine tools, can be used to identify geometric errors as an alternative to traditional interferometry-based techniques. As geometric errors also contribute to OMM error, assessment of the uncertainty of error identification is essential to ensure the reliability of the identification. This work presents a method to evaluate the uncertainty in the OMM-based geometric error identification process by Monte Carlo simulation. The error ellipse model, which represents OMM errors with better accuracy, is utilized to improve the identification uncertainty. The squareness errors, as position-independent geometric errors that contribute to machining inaccuracy, are taken as an example to demonstrate the presented method. The influence of the artifact setup errors on the error identification is also investigated. A series of experiments are conducted to evaluate the uncertainty of the OMM-based identification method. The geometric error obtained with the presented method is found to deviate by less than 10% from that obtained with an interferometry-based commercial instrument. The uncertainty obtained from the proposed Monte Carlo simulation method matches well with the uncertainty results obtained by the Guide to the Expression of Uncertainty in Measurement method and repeated measurements.

Promoting Machining Efficiency of 5-Axis Machine Tools with Non-Contact On-Machine Measurement Function

Promoting Machining Efficiency of 5-Axis Machine Tools with Non-Contact On-Machine Measurement Function

A novel IoT based machine vision system for on-machine diameter measurement and optimization

The use of machine vision systems has been made user-friendly, cost-effective, and flawless by the rapid development in the fields of advanced electro-optical and camera systems, electronics systems, and software systems. One such application of machine vision systems in the field of manufacturing is the inspection of a semi-finished or finished component during an ongoing manufacturing process. In this study, the camera’s intrinsic and extrinsic parameters were maintained constant, while red, green, and blue light sources were employed to measure the component diameter using pixel analysis. A novel approach was used in an IoT-based machine vision system where, on the same image, the smartphone camera was calibrated and the image diameter of the component under study was measured, which was found to be quite accurate. Four different cases were used in the error analysis of image diameter, in which experimental results show that under blue light, the percentage pixel error span is the largest at 0.2624% followed by 0.1422% under green light and 0.0903% under red light. Further, the use of four different cases was followed by the ‘Weighted Sum Model’, which optimized the percentage errors in estimated actual diameter precisely and effectively, where outcome results showed that the approximate percentage errors were determined within 0.8% for blue light, 0.5% for a red light, and 0.1% for a green light. The proposed IoT-based machine vision system was found to be robust and effective for on-machine measurement.

Design of an On-machine Measurement System Based on GNC62

Parts with high precision requirements in CNC machining often require manual intervention for repeated checking of critical dimensions and dimension compensation to meet machining accuracy requirements. This makes it difficult to establish a standardized inspection process. This paper proposes a design scheme for an on-machine measurement system based on domestically produced machine tools. Firstly, design measurement features are created through CAD model feature recognition and information extraction, establishing the foundation for measurement data. Next, a method is developed for creating measurement programs for the GNC62 numerical control system based on measurement features. Then, measurement accuracy is enhanced through the design of pre-travel error and probe radius compensation procedures. Finally, on-machine measurement experiments are conducted on typical parts processed with the KEDE GNC62 numerical control system, achieving measurement and calculation of machining dimensions and positional tolerances, and implementing numerical control system alarms based on measurement results. Experimental results demonstrate the successful implementation of on-machine measurement functionality on the GNC62 numerical control system, with measurement accuracy within 0.02mm.

Research on Geometric Constraint Strategies for Controlling the Diameter of Micro-Shafts Manufactured via Wire Electric Discharge Grinding.

Micro-tools comprising difficult-to-machine materials have seen widespread application in micro-manufacturing to satisfy the demands of micro-part processing and micro-device development. Taking micro-shafts as an example, the related developmental technology, based on wire electric discharge grinding (WEDG) as the core method, is one of the key technologies used to prepare high-precision micro-shafts. To enable efficient and high-precision machining of micro-shafts with target diameters, instead of performing multiple repeated on-machine measurements and reprocessing, a geometric constraint strategy is proposed based on the previously introduced twin-mirroring-wire tangential feed electrical discharge grinding (TMTF-WEDG). This strategy encompasses the tool setting method, tangential feed distance compensation, and an equation that establishes the relationship between tangential distance and diameter variation. These components are derived from a key points analysis of the geometric constraints. The micro-shafts with diameters of 50 µm and consistencies of ±1.5 µm are repeatedly processed. A series of micro-shafts with diameters ranging from 30 µm to 120 µm achieve geometric constraints with a diameter accuracy of ±2 µm, accompanied by the complete continuous automation of the entire process. Accordingly, it can be concluded that the geometric constraint strategy is flexible and stable and can be controlled with high precision in the TMTF-WEDG process.

Modeling and analysis of radial throw of micro-milling tool based on tool-attachment errors in high-speed rotating

In the process of micro-milling complex structures, radial throw is likely to cause lower dimensional/form accuracy, which will severely affect the performance of the components. Therefore, it is necessary to investigate the source contributing to the radial throw. According to the kinematic process of the radial throw arising from the attachment errors of the micro milling cutters, a mathematical model considered the attachment errors and the variable section geometry of the micro milling cutters was established to predict the radial throw on the basis of the principle of minimum total potential energy and the Timoshenko beam theory. In comparison to the mathematical model, the proposed finite element model (FEM) presetting the attachment errors shows the effect of modal vibration and the variation of radial throw with attachment errors. To further investigate the environmental factors influencing the radial throw, a measurement method based on the laser measurement system is presented to achieve the on-machine measurement of runout, and a specific extraction method of attachment errors was proposed to help validate the mathematical model. The difference between the results of the mathematical model, FEM, and the experiment is not exceeding 10 % within 3 μm of the maximum runout. Radial throw varies strongly with attachment errors, and the high rotation speed will aggravate the system instability caused by the attachment errors. The main contributor among experimental factors to the variation of runout is the form accuracy of micro milling cutters, accounting for 4.7 % at the highest. This method can provide a theoretical basis for the sources of unbalanced cutting force analysis and the improvement of dimensional/form accuracy.

Estimation of Shape Error with Monitoring Signals

Recently, extensive research has actively been conducted in relation to intelligent manufacturing systems. During the machining process, various factors, such as geometric errors, vibrations, and cutting force fluctuations, lead to shape errors. When a shape error exceeds the tolerance, it results in improper assembly or functionality issues in the assembled part. Predicting shape errors before or during the machining process helps increase production efficiency. In this paper, we propose a methodology that uses monitoring signals and on-machine measurement (OMM) results to predict machining quality in real time. We investigate the correlation between monitoring signals and OMM results and then construct a machine learning model for shape error estimation. The developed model implements a tool offset compensation strategy. The performance of the proposed method is evaluated under various sliding window sizes and the compensation weights. The experimental results confirmed that the proposed algorithm is effective for obtaining a uniform machining quality.

Online monitoring method of non-cylindrical wheel wear for gear grinding based on dynamic force model

Traditional monitoring methods of grinding wheel wear mainly take on-machine measurement systems or black-box algorithms. The first method has poor real-time performance for it can only be implemented after machining, making it difficult to adjust grinding processes in time. The second method has not revealed the mechanism of wear, resulting in poor model accuracy and flexibility. Besides, previous models are mainly for cylindrical wheels in surface grinding, while the properties of non-cylindrical wheel wear for gear grinding are different. To address this issue, a novel online monitoring method of non-cylindrical wheel wear for gear grinding is proposed. Firstly, a new parameter is defined based on grain height variation to describe the non-cylindrical wheel wear condition, avoiding the problem of underestimating the actual results due to a small measurement range. Then, a theoretical model for dynamic grinding forces is established considering more grain parameters and more complicated and realistic shape, so that on the basis of that model, the online monitoring method for wheel wear is proposed. Finally, experiments under different grinding conditions are conducted to verify the proposed models. The average calculation error of the grinding force model in different grinding parameters is 6.67%, and the maximum prediction error of the monitoring method under different wheel wear conditions is 8.2%.

New machining test for identifying geometric and thermal errors of rotary axes for five-axis machine tools

This paper presents a new machining test for identifying the geometric and thermal errors of rotary axes for a five-axis machine tool. A disc-shaped test piece containing 12 rectangular grooves is designed and processed. A coordinate-measuring machine is used to measure the rectangular grooves to identify four position-independent geometric errors (PIGEs) and six position-dependent geometric errors (PDGEs) of the rotary axis. On-machine measurements are performed under heating and cooling conditions to identify the six thermal errors. The identification principle and analytical solution of each geometric and thermal error are provided, and an uncertainty analysis is performed. Compared with two classical machining tests, the results of the proposed method are in good agreement; the agreement degrees of the PIGE, PDGE, and thermal error are 90.9%, 91.3%, and 85.1%, respectively. The identification principle and analytical solution of each error are simple and not affected by linear axes, thereby improving accuracy.

Workshop-suited identification of thermo-elastic errors of three-axis machine tools using on-machine measurement

This paper presents a method for workshop-suited identification of thermo-elastic errors of three-axis machine tools (MTs) using on-machine measurement. For this purpose, a new design for a three dimensional artifact is presented. Using the artifact, it is possible to identify all relevant geometric errors of three-axis portal milling machines in table design. The method presented is therefore suitable for the one-time identification of geometric errors caused by manufacturing inaccuracies, assembly-related deviations and wear, and for the identification of thermally induced geometry errors over a long-term period. Since the artifact is scalable in size, it can be easily adapted to different machine sizes. The presented method thus represents a universal and inexpensive possibility to measure geometric and especially thermo-elastic errors.Within the scope of this paper, an error model was established for the investigated machine type and an artifact design was derived on the basis of this model. The suitability of the artifact for identifying thermo-elastic errors has also been investigated experimentally. For this purpose, an artifact made of thermo-invariant material (Invar) was produced. This artifact was measured cyclically on a MT with three linear axes, which was exposed to a controlled thermal load in a temperature chamber. The positions of the geometric features were collected using a 3D touch trigger probe. In addition, the ambient temperatures were recorded. The results of this measurement are discussed in this paper.

Tolerance-constrained adaptive machining of negative allowance surface based on composite on-machine measurement

针对复杂舱体进气道曲面热成形铸造毛坯尺寸一致性差、轮廓度和壁厚成形精度低等问题, 提出基于复合在机测量的双公差约束下负余量曲面适应性加工方法. 首先搭建复合在机测量平台, 为减少数据迟滞, 开发同步数据采集系统, 提出激光传感器位姿迭代标定方法并分析测量误差; 随后在曲面轮廓测量数据指导下, 通过精确法矢解算实现壁厚的高精度超声在机测量. 面向负余量曲面加工, 建立轮廓度公差和壁厚公差双重约束下的数学模型, 以公差占用度、不均匀度以及一致度为优化目标, 在不同权重参数下实现数据驱动的新曲面重建; 随后, 针对复杂舱体进气道曲面制定了复合在机测量与适应性加工策略, 并进行有效性验证. 结果表明, 轮廓度测量最大正负误差分别为+0.0083 mm和-0.0073 mm, 壁厚测量误差均在0.03 mm以内，表明在机测量系统有效; 此外, 该舱体进气道曲面存在负余量为-0.0524 mm区域, 适应性加工后轮廓度最大正偏差和最小负偏差分别为+0.0538 mm和-0.3655 mm, 壁厚偏差最大值和最小值分别为+0.16 mm和-0.22 mm, 满足公差要求(轮廓度: -0.5 mm～+1 mm, 壁厚: -0.4 mm～+0.2 mm)且可实现全曲面高精高效适应性加工.

Knowledge Embedded Lightweight Vision Transformer for Machine Condition Monitoring

Machine condition monitoring methods are a powerful tool for the physical condition evaluation of high-end equipment. As one of the main manufacturing applications of machine condition monitoring, tool performance evaluation (TPE) suffers the limitation of model generalization ability across multiple operating conditions. Subsequently, designing a method that can meet the diversified industrial scenarios’ requirements to confirm the cutters’ machinability is of great practical significance. However, recent TPE studies indicate that the on-machine measurement processes are predominantly affected by the interference of ambient noise and signal aliasing of multi-source sensors. Recent advances in knowledge-embedding and artificial intelligence bring new vitality to the development of industrial condition-based maintenance applications. Considering the above-mentioned analysis, this paper proposes a new knowledge-embedded intelligent TPE method, where the prior knowledge of machining parameters is fused with features extracted from multiple sensor information. The proposed method gives an effective solution for TPE tasks under multiple working conditions.

Study on the Method of Tilt Error Compensation for a Laser On-machine Measurement System

This paper proposes a spindle vector compensation method based on laser triangulation for the inclination measurement error of a laser on-machine measurement system. By using the fixed relationship between the calibration vector and the spindle vector, the laser’s axis vector and the surface’s normal vector coincide in the actual measurement by adjusting the spindle vector. The tilt measurement and calculation errors are compensated simultaneously using this spindle vector compensation method.