研究目的
To determine the optimum cutting conditions leading to minimum surface roughness and electrostatic charge and maximum productivity in single-point diamond turning of PMMA contact lens polymer.
研究成果
The study successfully modeled and optimized cutting parameters for SPDT of PMMA contact lens polymer. Cutting speed was the most significant factor for surface roughness, feed rate for material removal rate, and a combination of parameters for electrostatic charge. The optimal conditions (cutting speed = 3712.78 rpm, feed rate = 10.55 mm/min, depth of cut = 36.67 μm) achieved minimal surface roughness (1.699 nm), minimal electrostatic charge (0.009 kV), and maximal material removal rate (23.972 mm3/min) with high desirability (0.997).
研究不足
The study is limited to PMMA contact lens polymer and specific machining parameters; results may not generalize to other materials or conditions. The models are empirical and based on laboratory experiments, which may not fully capture real-world variability.
1:Experimental Design and Method Selection:
Response surface methodology (RSM) based on Box-Behnken design was employed to develop models for surface roughness (Ra), electrostatic charge (ESC), and material removal rate (MRR). A second-order polynomial model was used for regression analysis.
2:Sample Selection and Data Sources:
PMMA contact lens polymer buttons (17 mm diameter, 4 mm thickness) were used as workpiece material.
3:List of Experimental Equipment and Materials:
Precitech Nanoform Ultragrind 250 ultra-high precision lathe, Taylor Hobson PGI Dimension 5XL 3D surface profilometer, SMC IZD10 electrostatic sensor, ESD monitor, National Instruments PXIe-1071 data acquisition system, LabVIEW 2016 software, Design-Expert 7 software, Minitab 18 software.
4:Experimental Procedures and Operational Workflow:
Experiments were conducted in a controlled environment (23°C, 50% humidity). Cutting parameters (cutting speed, feed rate, depth of cut) were varied according to the Box-Behnken design. Surface roughness was measured using the profilometer, electrostatic charge was measured with the sensor and DAQ system, and MRR was calculated using a formula.
5:Data Analysis Methods:
ANOVA was used to determine the significance of factors, and regression analysis was performed to develop predictive models. Desirability function approach was used for multi-response optimization.
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