Explore the vast range of titles available.

Abstract This study proposes a high precision machining method of near-net-shaped blade based on multi-process machining errors data flow control. The multi-process machining geometric error and mechanical models of the blade multi-process are firstly constructed. The stiffness of blade-fixture system is secondly analyzed. The machining error flow of the blade multi-process is finally controlled by the adaptive CNC machining process under the sufficient stiffness of blade-fixture system. The results show that the dynamic displacement response of the blade multi-process is controlled within 0.007 mm. The optimized adaptive CNC machining process of the multi-process geometric machining error data flow control can realize the high-precision manufacturing of blade.

Tolerance design of key components is an effective method to improve the machine tools accuracy. Currently, tolerance design highly depends on the personal experience, which often subjectively determines the machine tool accuracy and also increases the manufacturing costs. To avoid the phenomenon, a machining error prediction model (MEPM) assisting tolerance design is proposed and experimental verified based on a precision vertical grinding machine (PVGM). Firstly, the mapping relationship between the tolerance parameter and geometric error of key components is obtained as the superimposed Fourier series. Subsequently, the volumetric error model including geometric errors is established by the homogeneous transform matrix and multi-body system theory. Based on a typical workpiece, a tolerance-based MEPM is obtained by integrating the ideal machining path. Finally, a grinding test on PVGM is conducted to validate the MEPM. The results show that the predicted roundness of three positions is 3.033 μm, 2.905 μm, and 2.774 μm, respectively, and the measured roundness are 3.163 μm, 2.980 μm, and 2.904 μm, respectively. The error between PVGM predicted and measured roundness is not more than 5%. The MEPM is important for assisting the tolerance design and improving the machining accuracy in the machine tools.

Abstract Identification of key geometric errors is an essential prerequisite for improving the machining accuracy of five-axis machine tools. This paper presents a new sensitivity analysis (SA) method to extract key geometric errors, and then to improve the machining performance of machine tools by compensating key geometric error components. Development of geometric error prediction model is involved to obtain geometric error values at arbitrary positions at first. Based on the multi-body system theory and flank milling theory, the machining error model is developed, which considers 37 geometric errors. Then, a new SA method is introduced by taking the machining error model as sensitivity analysis model and taking the geometric errors as analytical factors. Meanwhile, a sensitivity index, which has the characteristics of simple expression and clear physical meaning, is proposed, i.e., the peak value of the machining error caused by each geometric error. Moreover, the simulations analysis is carried out to obtain the sensitivity coefficient of each geometric error and the key error components. Finally, the validity and correctness of the proposed method are demonstrated by the experiments. Furthermore, the SA method can be extended to multi-axis machine tools.

Stone-carving robotic manipulators (SCRMs) have a broad range of applications due to their high efficiency, diverse processing capabilities, and strong flexibility. However, due to its weak rigidity, the milling accuracy of an SCRM is lower than that of a computer numerical control (CNC) machine for working stone into special shapes, making it difficult to meet the requirements of stone milling processing. To improve the milling accuracy of SCRMs, multi-iteration error compensation technology considering the coupling relationship between compensation and deformation is proposed in this paper. First, a global stiffness model of an SCRM in which the robot arm bars are regarded as flexible links is established, and the relationship between the milling force and milling depth is fitted based on a milling force prediction model. Then, the machining error and milling depth when processing a marble workpiece are predicted. Finally, a multi-iteration method is applied for calculation using an interval correction strategy to construct a discrete control system for error compensation in the SCRM. The feasibility and effectiveness of the proposed compensation technology are verified by an experiment using the KUKA-240-2900 SCRM system.

The components with freeform surface are widely used in industrial fields. The machining quality of freeform surface becomes increasingly significant. Coordinate measuring machine (CMM), as a conventional dimensional measuring instrument, is commonly used to inspect freeform surface. The sampling strategy, consisting of distribution of sampling points and sample size, is important to the effectiveness and accuracy of the measurements performed on CMM. The present work aims at proposing an improved adaptive sampling strategy based on a machining error model (MEM) for freeform surface inspection on CMM. The machining error model is built to determine the distribution of sampling points adaptively. In addition, Hammersley sequence is adopted to address the aliasing problem. Experiments are performed to compare presented strategy with four well-known sampling strategies. Results prove that the MEM is valid and the present strategy is reliable and effective.

This study proposes an adaptive CNC machining process based on on-machine measurement to control the machining error of near-net-shaped blades. The multi-source and multi-process machining error transmission model of a near-net-shaped blade is established, and the reduction effect of the machining error transmission chain by the adaptive CNC machining process is qualitatively analyzed based on the machining error transmission flow model. The effects of the adaptive CNC machining process on the positioning benchmark error, machining position error, and machining contouring error are explored through the adaptive CNC machining process experiment. In particular, the ability of the adaptive CNC machining process to cooperatively control the blade position error and the contouring error is discussed in relation to the stiffness of the blade-fixture system. The results show that the adaptive CNC machining process can reasonably reduce the machining errors caused by the positioning benchmark. The final deviation band of the blade body is reduced by 60% based on the adaptive CNC machining process. The adaptive CNC machining process can optimize the contouring error and the position error of the blade tenon root under the enough premise of the stiffness of the blade-fixture system. The adaptive CNC machining process has the excellent ability to control machining errors to improve the machining quality of the blade.

Thin-walled parts such as blades are widely used in aerospace field, and their machining quality directly affects the service performance of core components. Due to obvious time-varying nonlinear effect and complex machining system, it is a great challenge to realize accurate and fast prediction of machining errors of such parts. To solve the above problems, an engineering knowledge based sparse Bayesian learning approach is proposed to realize in-situ prediction of machining errors of thin-walled blades. Firstly, an engineering knowledge based strategy is proposed to improve the generalization ability of the model by integrating multi-source engineering knowledge, including machining information, physical information and online monitoring information. Then, principal component analysis method is utilized for the dimensional reduction of features. Sparse Bayesian learning approach is developed for model training, which significantly reduce the complexity of the regression model. Finally, the superiority and effectiveness of the proposed approach have been proven in blade milling experiments. Experimental results show that the average deviation of the proposed in-situ prediction model is about 11 μm, and the model complexity is reduced by 66%.

The paper first introduces R offset tool axis error calculation method. In order to reduce the maximum machining error, the paper presents a new geometric model of tool axis vector for flank milling non-developable ruled surfaces and machining error exact analytical solution is derived. Its main advantage is that the theoretical machining error of each point on the straight generatrix can be calculated. MATLAB software simulation and actual NC machining experiment are conducted to validate the effectiveness of the proposed algorithm. The measurement experimental results show that the actual maximum machining error is basically consistent with the theoretical calculation error, and the machining error can be reduced compared with the previous literature algorithm.

Surface topography is an important element that reflects the machining quality of workpieces and the functional performance of components. To avoid expensive cutting experiments, forecasting the surface topography before actual machining is necessary. Surface topography of diamond turned surfaces is directly affected by generated tool path in ultraprecision single point diamond turning (SPDT). In this study, a novel surface topography modeling method was presented. The planned tool path based on active machining precision control was considered in this method. Simulation and experiment of an umbrella surface diamond turning were conducted to testify the usability of the proposed model. Through comparing the simulation and experimental data of surface topography and machining error, the results showed that the proposed model can be applied to forecast the surface topography and machining error in SPDT using the generated tool path.

The tolerances of key moving components are critical factors influencing the final machining error in the design, manufacturing, and assembly of machine tools. However, the precise mechanism linking tolerances to machining errors remains insufficiently understood. This leads to a heavy dependence on the experience of engineers for tolerance design. However, human expertise may be influenced by subjective factors. Such subjectivity introduces variability that may increase manufacturing costs. To handle this complication, this paper proposes a method for predicting machining errors in five-axis machine tools considering the tolerances of key moving components. Firstly, the structure and kinematic characteristics of five-axis machine tools are analyzed, and the mapping relationship between axis motion tolerances and geometric errors is represented using the Fourier series. Then, a volumetric error prediction model for the five-axis machine tool is developed based on multi-body system theory and homogeneous transformation matrices, followed by deriving a machining error model according to the machining method of the workpiece. Finally, machining experiments are carried out on the five-axis machine tool and measured with a Coordinate Measuring Machine. The results show that the maximum deviation between predicted and measured values is less than 10.00%. These findings provide theoretical support for the tolerance design of five-axis machine tools, helping to reduce manufacturing costs and improve machining accuracy.