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The precision turning process has attracted enormous interest in the standardized and mass production of micro-shafts because it produces more efficiency and lower cost compared with the electrical-discharge machining method and micro-machining method. However, there are many critical factors that influence the dimensional accuracy of micro-shafts, such as the error of workpiece deformation, cutting force, and tool setting error. In this paper, a novel error compensation method was proposed to improve the dimensional accuracy of micro-shafts manufactured with the conventional precision machine tool, based on the constant force cutting method and tool path planning. Firstly, a cutting force model was established considering the effect of the tool nose radius, and the finite element method was used to simulate the turning process and predict the workpiece deformation. Secondly, an error analysis model was created by taking tool setting error, workpiece deformation error, and multi-process cumulative error into account, and the optimized tool path and cutting parameters were obtained. Finally, a series of turning experiments were carried out to verify the error compensation strategy and the cutting force model. The results show that the dimensional error of the micro-shafts with a large length-diameter ratio (l = 5 mm, d = 0.4 mm) can be reduced by 90% by means of the proposed error compensation strategy. In addition, the maximum prediction error of cutting forces is less than 8.6%, and the effect of cutting parameters on cutting forces can be concluded as “feed rate > depth of cut > spindle speed.”

In the process of high strength steel turning, tool wear will reduce the surface quality of the workpiece and increase cutting force and cutting temperature. To obtain the fine surface quality and avoid unnecessary loss, it is necessary to monitor the state of tool wear in the dry turning. In this article, the cutting force, vibration signal and surface texture of the machined surface were collected by tool condition monitoring system and signal processing techniques are being used for extracting the time-domain, frequency-domain and time-frequency features of cutting force and vibration. The gray level processing technique is used to extract the features of the gray co-occurrence matrix of the surface texture and found that these features changed simultaneously when the cutting tool broke. After this, an intelligent prediction model of tool wear was built using the support vector regression (SVR) whose kernel function parameters were optimized by the grid search algorithm (GS), the genetic algorithm (GA) and the particle swarm optimization algorithm respectively. The features extracted from the signals and surface texture are used to train the prediction model in MATLAB. It was found that after the surface texture features were fused using the intelligent prediction model on the basis of the features of cutting force and vibration, prediction accuracy of the proposed method is found as 97.32% and 96.72% respectively under the two prediction models of GA-SVR and GS-SVR. Moreover, the intelligent prediction model can not only predict the tool wear under different cutting conditions, but also the different wear stages in a single wear cycle and the absolute error between the predicted value and the actual value is less than 10 μm, the confidence coefficient of prediction curve is around 0.99.

The surface topography of milled workpieces often defines their performances, and it directly affects the precision and service life of parts. In view of the influence of tool inclination angle on the 3D surface topography in micro-ball-end milling, this paper carries out micro-milling surface predictions and tests based on the theoretical model of cutting edge trajectory model, providing technical basis for the high-precision machining of micro-surface. A theoretical model for the description of the cutting edge trajectory is established prior which takes inclination of cutter, flutter, run-out, cutting force, and plastic deformation of the material into account. Then, the surface topography and roughness are predicted based on the model. In addition, a series of newly designed surface topography experiments are carried out to verify the prediction. Through the comparison of the simulated micro-surface with the experimental results, the theoretical model showed considerable reliability, which means the 3D surface topography could be well controlled with the parameter recommended by the constructed model under certain conditions. The above research has a great significance in improving machining quality.

This paper developed a novel, simple-to-use method for detecting tool failure in cutting. An EEMD filtering and restructuring method is launched according to entropy and correlation of cutting force signal in turning high-strength and high-hardness materials using carbide cutting tools. Then, friction coefficient and mean power theories are adopted to characterize tool failure status using restructured cutting force signal. Finally, an automatic detector is proposed using CUSUM control chart to monitor tool chipping and tool wear failure in real time based on previous analysis and can be verified by semi-finishing experiment. Without any need of other sensor equipment and long computing time, the method can be reliable and simple to operate in detecting tool failure in real time.

The machined surface integrity of blades is of utmost importance in the power equipment manufacturing industry. Recently, many blade accidents have been attributable to the misuse of cutting fluids that were necessary in the actual machining of difficult-to-cut materials, but the effect of the cutting fluid on surface integrity and service performance has been persistently neglected. In this article, an investigation into the effect of cutting fluids on the surface quality of a typical blade material was undertaken, combined with properties of two universal cutting fluids. Element composition, surface morphology, residual stress and hardness of the machined surfaces were investigated. The results indicated that cutting fluids could not reduce cutting forces in precision machining. There were some places where local and irregular elemental Cr loss was incurred when machined with the additive Cl, and the depth of Cr loss was 1–2 µm. The machined surface under CF-206 was smooth, and the roughness of CF-210 was the highest. The tensile residual stresses of dry cutting were the highest, whereas those of CF-210 were the lowest. Surface work hardening under cutting fluids was higher, with depths of 20–30 µm. These results are significant for the control of precision machined surfaces and subsurfaces of blades with high integrity and service performance.

High-speed milling tests for 65vol.%SiCp/6063Al composites were performed by polycrystalline diamond (PCD) tools, response surface methodology was utilized in this study, and a cutting force model was developed through response surface methodology, which contained some important parameters such as cutting speed, cutting feed rate, cutting depth and cutting width. The analysis of variance (ANOVA) indicated that the proposed mathematical model can adequately describe the relationship between cutting force and cutting process parameters. The results show that cutting depth is the biggest important factor of milling force, and cutting feed rate is the second important factor, cutting speed is the third; milling force would not increase with the increasing of cutting width.

Based on the design of orthogonal cutting tests, study the processing and the analysis method of orthogonal test data, program the module for solving regression equation, research on the calculation of cutting force curve, drawing and display issues based on the cutting test data which is stored cutting database. The software, developed by using the development plat of Visual C + +, can easily deal with orthogonal experiment data , draw and display the fitting equation of the test curve after user enters the relevant parameters according to needs intuitively, has laid the foundation for the draw curve ,such as tool wear, tool life, the chip breaking test. Thus it is enriched the cutting data storage capabilities of the database, and data processing capabilities and test curve processing capabilities.

The present paper demonstrates a study of the impact of cutting condition on turning high strength steel 34CrNiMo6. Based on Taguchi method, a plan of experiments was performed with ceramic cutting insert. The first and second cutting force equations are developed through the response surface methodology (RSM) to investigate the effect of input cutting parameters (cutting speed, feed rate and depth) on cutting force. In term of input parameters, the cutting force contours are showed and the analysis of the predicted models is performed with aid of the statistical software package. In addition, the separate influence of individual cutting parameter and the interaction between these factors are also discussed in this study. In general, the results obtained from the mathematical model agree well with the experimental data.

By dry cutting 34CrNiMo6, hard-machined material, with carbide cutting tool and ceramic cutting tool, experimental analysis was carried out in tool wear and cutting force. Cutting tool wear in the process is researched in order to obtain the cutting tool wear patterns and analyze the influencing factors. Meanwhile, it is found that the cutting force with the cutting parameters (cutting speed, cutting depth, feed) changes regularly, and then the experience formula of cutting force can be established.

The objective of this paper is to study the cutting forces in hard turning T250 steel with CBN tools. Experiments based on the Box-Behnken design were conducted to develop the cutting forces models by response surface methodology (RSM). Significance tests of the model are performed by the analysis of variance (ANOVA). It is also discussed the effects of cutting parameters (cutting speed, feed rate and depth of cut) on the cutting force components. The results show that the models can fit experimental data via analysis of variance. The most important cutting parameter is depth of cut, followed by feed rate, while the effect of cutting speed can be neglected. Compared to cutting force and feed force, thrust force is the largest. In addition, the cutting forces generated by the uncoated tool are smaller than by the coated one due to tool wear.