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Ceramic materials have tremendous demand in manufacturing sectors. However, poor machinability impedes their widespread applications on an industrial scale. BK-7 falls in the same category and is normally processed by ultrasonic machining. But nowadays rotary ultrasonic machining is overtaking the ultrasonic machining for processing difficult to cut materials because of its superlative material removal mechanism. Current study aims to improve the surface quality of BK7 by studying the effect of input factors on surface roughness during rotary ultrasonic machining. Response surface methodology has been used to observe the effect of input variables ― spindle speed, feed rate and ultrasonic power ― on surface roughness (SR). Thereafter, central composite design was employed to estimate the regression coefficients of quadratic model for surface roughness. Fitness of developed quadratic model was checked by ANOVA test, which also revealed that all the model terms of input factors were significant except feed and speed interaction. Feed has the maximum impact over surface roughness descended by moderate impact of power and spindle speed. The study was further reinforced on observing the surface integrity of processed surfaces using scanning electron microscopic images. Mixed flow of material was observed to occur at lower feed rate and higher levels of rpm and ultrasonic power. 相似文献
3.
This paper performs a comprehensive analysis and calibration on the geometric error of the ultra-precision drum roll lathe with dual-spindle symmetrical structure and cross slider layout. Firstly, the volumetric error model which contains all geometric errors of the dual-spindle ultra-precision drum roll lathe (DSUPDRL) is developed based on the combination of the homogenous transfer matrix (HTM) and multi-body system (MBS) theory. Secondly, sensitivity analysis for the volumetric error model is conducted to identify the sensitive geometric error components of the DSUPDRL using an improved Sobol method based on the quasi-Monte Carlo algorithm. The result of sensitivity analysis laid the foundation for the subsequent geometric error calibration. Then, some sensitive error components along the X and Z directions are calibrated using a laser interferometer and a pair of inductance displacement probes. Besides the volumetric error model, the concentricity error caused by dual-spindle symmetrical structure is proposed and calibrated by the on-machine measurement using a classic reversal method. Finally, a large-scale roller mold with a diameter of 250 mm and a length of 600 mm is machined using the DSUPDRL after calibration. The experimental result shows that 1.4 μm/600 mm generatrix accuracy is obtained, which validate the effectiveness of the geometric error analysis and calibration. 相似文献
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High-performance aerospace component manufacturing requires stringent in-process geometrical and performance-based quality control. Real-time observation, understanding and control of machining processes are integral to optimizing the machining strategies of aerospace component manufacturing. Digital Twin can be used to model, monitor and control the machining process by fusing multi-dimensional in-context machining process data, such as changes in geometry, material properties and machining parameters. However, there is a lack of systematic and efficient Digital Twin modeling method that can adaptively develop high-fidelity multi-scale and multi-dimensional Digital Twins of machining processes. Aiming at addressing this challenge, we proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process. With this approach, we developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model. These Digital Twin sub-models can interact with each other and compose an integrated true representation of the physical machining process. To demonstrate the effectiveness of the proposed biomimicry-based Digital Twin modeling method, we tested the method in monitoring and controlling the machining process of an air rudder. 相似文献
6.
This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. In this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment. 相似文献
7.
We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models. 相似文献
8.
设计整体采用无级变速交流电机串联变速箱有级调速的方式,变速箱转速切换采用拨叉拨动输入轴两组双联齿轮滑移改变传动比,使主轴在转速范围内输出16级转速。操纵机构采用双液压缸带动拨叉操纵双联齿轮,使输入轴齿轮切换啮合位点。在中间轴采用电磁式离合器快速切换各级转速,合理利用主轴箱空间的同时,提高了整体的传动效率,整体设计符合自动化的要求。 相似文献
9.
Fiber orientations play the decisive role in grinding process of woven ceramic matrix composites, but the influence of woven fibers in grinding process is not clear. This paper studies the surface quality and grinding force by comparing different woven surfaces. Through a series of experiments in optimized sampling conditions, we analyze characteristics of the material surface topography height, wave distribution and surface support properties in details. And we find some outstanding characteristics of the surface microstructure. We also study the influence of grinding processing parameters on surface microstructure. The results show that machining surface which contains more parallel fibers is rougher and more keenness than gauss surface. Grinding wheel speed and depth of cut have great influence on surface topography and surface support properties. And it is discovered that grinding forces are also highly dependent on fiber orientations. The mechanism of the grinding phenomena is also analyzed in this paper according to knowledge of fracture mechanics and mechanical damage phenomenology. The research obtained will be an important technical support on improving the processing quality of woven ceramic matrix composites. 相似文献
10.
With ever increasing demand for small parts with complex shapes and high dimensional accuracy, many traditional machine tools have become ineffective for machining these miniature components. Typical examples include dental implants, parts used in mechanical watch movements, and parts used in medical endoscopes. This paper introduces our PC-controlled CNC turn-mill machining center. It has 5 axes, an automatic bar feeder, an automatic part collection tray, and a tool changer. It features a special control algorithm for the synchronization of its axes that produces not only higher accuracy but also makes the machine easier to use. In addition, a volumetric error compensation algorithm is implemented to improve accuracy. Based on experiments, the machining error is ±3 μm for turning, ±7 μm for milling and the maximum profile error is less than ±7.5 μm for gear hobbing. 相似文献