Design of Compound Machine Tool for Ultra-Precision Shaft Parts

Ultra-precision shaft components are widely used, such as the shaft core of air-floating spindle, etc. At present, the final precision of such workpieces are difficult to reach through CNC machining tools, but often with the help of manual grinding, of which the machining precision and efficiency are greatly limited. Based on the deterministic figuring method, this paper uses an abrasive belt polishing machine to carry out ultra-precision figuring experiments on shaft parts. On this basis, an ultra-precision composite machine tool for shaft parts is designed with the functions of turning, grinding and abrasive belt polishing. The dynamic performance requirements of the machine tool are put forward by analyzing the precision index of the workpiece and the error control principle of deterministic figuring, and the structure of machine tool is designed and the performance parameters of each part are selected. combined with the finite element analysis, the key components are optimized to meet the machining requirements of 0.1μm roundness and 1μm cylindricity on the steel shaft.

Optimal Parameter Selection in Robotic Belt Polishing for Aeroengine Blade Based on GRA-RSM Method

Due to its flexibility and versatility, robotic belt polishing is one of the most effective processing methods to improve the surface quality of aeroengine blades. Since belt polishing of blades is a material removal process aimed at reducing surface roughness, it is difficult to achieve both minimum surface roughness and maximum material removal rates. In order to solve this problem, this paper proposes an optimization method combining grey correlation analysis (GRA), the Taguchi method, and the response surface method (RSM) for the multiobjective optimization of the process parameters of Ti–6Al–4V aeroengine blade polishing. Meanwhile, the problem of the influence of asymmetry on the polishing process parameters vis-a-vis the optimization goal was solved. Experiments of robotic belt polishing for aeroengine blades were carried out. Based on the results of the principal component analysis, the grey relational grade was established to turn multiobjective optimization into single-objective optimization. A quadratic regression model of Grey correlation grade was developed, and an optimal parameter combination was obtained by the RSM. Finally, verification experiments were performed, and the combination of optimal parameters was obtained as follows: feed rate of 232.09 mm/min, compression amount of 0.08 mm, and belt line speed of 16 m/s, which reduced surface roughness by 6.29% and increased the material removal rate by 16.11%. Comparing the results of GRA-RSM and GRA, the Grey correlation grade increased by 10.96%. In other words, the goal of simultaneously reducing the surface roughness and improving the material removal rate was achieved in robotic belt polishing for aeroengine blades.

Research on High Precision and Deterministic Figuring for Shaft Parts Based on Abrasive Belt Polishing

A deterministic figuring method for cylindrical surface based on abrasive belt polishing is proposed in this study in order to improve the geometric accuracy of metal shaft parts. The principal motion of material removal is performed through the axial oscillation of the abrasive belt, and the different material removal at different positions can be obtained through servo control of the machine tool spindle by removing high error spots on the cylindrical surface and finally deterministically corrects the roundness error. An abrasive belt-based deterministic figuring device was built, and the figuring experiments were performed on the surface of steel workpieces 100 mm in diameter and 130 mm in effective length. The roundness errors of the entire workpiece after twice figuring iterations decreased nearly from the initial 3 μm to 1 μm, which preliminary verified the feasibility of this method. This deterministic figuring method is expected to break the machining accuracy limit and improve the rotation precision of the precision shaft parts such as the aerostatic spindle.

Modeling and prediction of surface roughness in belt polishing based on artificial neural network

Surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall polishing system, which makes it an ever-increasing concern in industries and academia nowadays. In this article, from microscopic point of view, based on the statistics analysis, and by the use of the elastic contact theory and the plastic contact theory, the model of the maximum cutting depth of abrasive grains is developed. Then based on back-propagation neural network, taking the maximum cutting depth of abrasive grains, the rotation speed of belt and the feed rate of workpiece as the input parameters, a prediction model of surface roughness in belt polishing is presented. The prediction model fully takes the characteristics of polishing tool and workpiece into consideration which makes the model more comprehensive. Compared with the model that takes the polishing force as the input parameter, the model in this article needs fewer experiment samples which will save the experiment cost and time. Moreover, it has a wider range of uses and is suitable for different polishing situations such as different workpieces and polishing tools. The results indicate a good agreement between the predicted values and experimental values which verify the model.