NC technology as one of the core technologies of modern manufacturing, its development level directly affects the quality and efficiency of product manufacturing.
With the rapid development of aerospace, precision instruments and other high-end manufacturing industry, the machining accuracy and surface quality of parts put forward higher requirements.
Traditional machining methods in the face of high-precision, high-efficiency, complex surface machining needs, has shown obvious limitations.
Therefore, it is of great significance to conduct an in-depth study on the application of CNC technology in precision machining.
This includes optimizing machining parameters and process methods. Such efforts aim to enhance the overall level of the manufacturing industry.
This study explores the optimization method of CNC technology in efficient precision machining through experimental verification and data analysis.
CNC precision machining technology status quo
With the rapid development of the manufacturing industry, CNC precision machining technology in many countries has made significant progress.
At present, developed countries hold a leading position in the research and development of five-axis linkage machining centers, ultra-precision machining equipment, and other high-end technologies.
The precision of their CNC systems can reach the nanometer level.
These advanced systems have been widely applied in high-precision fields such as aerospace and optical component manufacturing.
Experimental program design
Experimental equipment and materials
Experiments using DMG MORI DMU50 five-axis machining center for high-precision parts processing, the maximum spindle speed of 24,000 rpm, positioning accuracy of ± 0.003mm.
The workpiece is made of aircraft-grade 7075 aluminum alloy with dimensions of 100×80×60mm and hardness of HB150.
The tool is a Japanese Mitsubishi brand carbide end mill with a diameter of φ12mm, and the overhang length of the tool is set to 36mm to ensure the machining rigidity.
Selection of process parameters
Based on the pre-experimental data, a three-factor five-level orthogonal experiment was designed.
The range of cutting speed was set at 100-300m/min, with one level every 50m/min; the feed rate was set at 0.05-300m/min.
The feed rate was set in the range of 0.05~0.25mm/z and 5 levels were taken; The depth of cut was taken from 0.2mm to 1.0mm at 0.2mm intervals.
The L25 orthogonal table was generated by the Design of Experiments Assistant, and a total of 25 sets of process parameter combination experiments were conducted.
The machining path adopts the contouring strategy, and the radial depth of cut is kept at 30% of the tool diameter.
Test methods and evaluation indexes
After the machining of the workpiece is completed, the surface roughness Ra is measured using Alicona InfiniteFocus G5 3D surface profiler, and the formula is as follows.
The dimensional accuracy is characterized by the true roundness deviation E, which is calculated as follows.
Where Rmax – maximum radius,mm
Rmin – minimum radius,mm
The comprehensive evaluation index Q of machined surface quality adopts the following weighted calculation model.
Where Ra0, E0, T0 – surface roughness, true roundness and machining time, respectively, the benchmark value
w1, w2, w3 – weighting coefficients, and w1 + w2 + w3 = 1
Tool wear rate VB was calculated by measuring the wear width of the back face:.
Where A1 – tool area after wear,mm2
A0 – initial tool area,mm2
t – cutting time,min
All the measured data were recorded into Minitab 19 for ANOVA to establish a mathematical model and optimize the combination of process parameters.
During the measurement process, the ambient temperature was controlled at 20±0.5°C and the humidity was 45%±5%.
Optimization of process parameters
Cutting speed on machining accuracy impact
Experimental data show (see Table 1), cutting speed in the range of 100-300m/min changes, the machining accuracy shows non-linear change characteristics.
When the cutting speed is 180m/min, the dimensional accuracy of the workpiece reaches the optimum, and the deviation of true roundness is only 0.004mm.
Table 1 Effect of cutting speed on machining accuracy (feed: 0.08mm/z, depth of cut: 0.6mm)
In the experiment, a cutting speed of 180 m/min was used. Under this condition, the temperature rise of the spindle was controlled within 3.5 °C.
The tool wear rate was reduced to 0.015 mm/min. Additionally, the qualified rate of workpiece size increased to 98.5%.
Influence of feed on surface quality
The test interval of feed rate is 0.05~0.25mm/z, and the test results show that the feed rate is positively correlated with the Ra value of surface roughness.
The best surface quality was obtained at a feed rate of 0.08 mm/z, and the Ra value was 0.4 μm.
Through the scanning electron microscope to observe the surface morphology, the feed of 0.08mm/z tool cutting traces uniform, no obvious tearing and adhesion phenomenon.
After the feed increased to 0.15mm/z, the surface appeared periodic ripples, Ra value rose to 0.8μm.
Depth of cut optimization analysis
The experimental range of depth of cut was set at 0.2~1.0mm, and the study showed that there was a significant interaction between depth of cut and machining efficiency and surface quality.
Data analysis shows that the depth of cut of 0.6mm reaches the optimal equilibrium point, the removal efficiency is 43.2cm3/min, and the surface roughness Ra is kept below 0.5μm.
The cutting force test data show that the ratio of radial force to axial force at this depth is 1.8:1, and the vibration amplitude of the tool is controlled within 2.5μm.
Cooling method selection study
Three cooling methods, namely, dry cutting, normal wet cooling and minimum quantity lubrication (MQL), were comparatively analyzed.
The MQL oil-air mixture flow rate was set at 60 ml/h and the air pressure was 0.5 MPa.
The experimental data showed that the surface roughness of the workpiece was reduced by 28% to Ra0.35 μm with MQL compared with normal wet cooling.
At the same time, MQL technology saves 95% of cutting fluid consumption than traditional wet cooling, which significantly reduces the production cost and environmental pollution.
Intelligent control strategy
Tool compensation technology
Based on the measurement data of Renishaw OMP60 optical tool setting instrument to establish a tool wear prediction model.
Experimentally measured tool in the cutting 180min, the tip arc radius increased from 0.8mm to 1.2mm, the tool back angle from 12 ° to 8.5 ° reduction.
By tracking the tool wear status in real time, the system automatically calculates the compensation amount and updates the tool position data.
The compensation strategy adopts a segmented compensation method. When the wear amount is less than 0.1 mm, one-time compensation is applied.
When the wear amount exceeds 0.1 mm, step-by-step compensation is used. In this case, the compensation amount for each step does not exceed 0.05 mm.
Experimental verification shows that the compensation strategy, the workpiece accuracy increased by 35%, tool life extended by 1.8 times, processing costs reduced by 22%.
Real-time monitoring system design
The development of real-time monitoring system based on multi-sensor fusion, integrated Kis⁃tler 9257B force gauge, PCB 352C33 acceleration sensor and OptrisPI400 infrared thermal imaging camera.
The system uses a PXI platform for data acquisition and processing, realizing the simultaneous monitoring of cutting force, vibration and temperature.
The experimental data show (see Fig. 1) that the surface roughness increases by 52% when the cutting force changes by more than 20% of the set value.
When the vibration amplitude exceeds 2.5 μm, the roundness error increases by 0.008 mm.
When the cutting zone temperature exceeds 120°C, the workpiece undergoes obvious thermal deformation.
Note: Sampling frequency: cutting force 20kHz, vibration 50kHz, temperature 50Hz, data filtered.
Adaptive control algorithm
A fuzzy neural network adaptive control algorithm is designed to realize real-time optimization of machining parameters. The cutting force control model is as follows.
Where:F(t) is the cutting force output value;e(t) is the cutting force error;
Kp, Ki, Kd are PID parameters.
Adaptive tuning rule.
Where η1, η2, η3 – learning rate
u(t)-control amount
The experimental results show that the cutting force fluctuation is reduced by 45% and machining accuracy is improved by 32% after using this algorithm.
Error compensation method
Through the laser interferometer measurement to obtain the geometric error data of the machine tool, 21 error compensation model was established.
Measured data show that the compensation system is based on measured data to establish a spatial error mapping grid, the grid spacing is set to 50mm.
At a feed rate of 6,000 mm/min, the system realized real-time compensation at the submicron level.
After compensation, the cylindricity error of the workpiece is reduced from 0.018mm to 0.006mm, and the surface contour error is reduced from 0.025mm to 0.008mm, with the overall accuracy improved by 65%.
Conclusion
Through systematic experimental research and data analysis, a set of CNC technology optimization scheme applicable to high-efficiency precision machining has been successfully constructed.
The study shows that the optimal combination of cutting parameters can significantly improve machining accuracy and efficiency.
In addition, the introduction of intelligent control strategies helps maintain machining stability. These strategies provide strong support for the overall machining process.
The experimental validation results show that the optimization scheme can significantly improve productivity.
At the same time, it ensures the required machining accuracy. Therefore, the scheme holds good value for engineering applications.
In the future, the study will further explore the application of artificial intelligence technology in CNC machining to realize a higher level of intelligent manufacturing.
