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A serious challenge faced by manufacturers of large-aperture aspheric optical components of glass ceramics is the long processing time. Ultrasonic vibration–assisted grinding (UVG) allows one to effectively shorten the subsequent polishing process by several times, which is essential for grinding of aspheric components. However, the surface creatio...
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Citations
... Glass-ceramic is a kind of material in which glass phase and crystal phase coexist, it contains glass phase and a certain amount of microcrystals. glass-ceramic can have higher hardness and mechanical strength by controlling the crystallization process [1,2]. Glassceramic is gradually replacing some traditional materials and widely used in chemical, aerospace, construction and other fields with good mechanical properties and mechanical properties [3,4]. ...
Glass-ceramic is a typical hard and brittle material that is difficult to machine. In order to improve the surface quality of laser-assisted fast tool servo machining optical free-form surface of glass-ceramic, the effects of spindle speed, feed speed, piezoelectric frequency and laser power on the surface roughness were investigated. Firstly, the Taguchi method (TM) was used to establish the orthogonal experiment, and the contribution rate of each machining parameter to the surface roughness was obtained through variance and signal-to-noise ratio (S/N) analysis. The order of the influence degree of each parameter on the surface roughness is as follows: laser power > spindle speed > feed speed > piezoelectric frequency. The optimal machining parameter combinations obtained for the TM experiment are as follows: spindle speed 50 rpm, feed speed 0.01 mm rev⁻¹, piezoelectric frequency 8 Hz, laser power 75 W. The range of surface roughness reduction obtained by comparing laser-assisted machining (LAM) with pure fast tool servo (FTS) machining is 38.75%∼58.77%. The Box-Behnken Design (BBD) in response surface methodology (RSM) was used to design experiments and a regression model for surface roughness was established through RSM. The deviation between the surface roughness predicted by the regression equation and the experimental value is less than ±6%. The influence law of various machining parameters on surface roughness was studied through three-dimensional response surface. RSM optimized the minimum surface roughness with a desirability of 99.43%. The optimal combination of machining parameters optimized through RSM is as follows: spindle speed 53.71 rpm, feed speed 0.02 mm rev⁻¹, piezoelectric frequency 6.73 Hz, laser power 72 W. This paper is the first to combine LAM with FTS for machining optical free-form surface of glass-ceramic. This study provides a reference for laser-assisted fast tool servo machining and the research methods of surface quality.
As a typical optical hard and brittle material, the efficient machining methods of glass-ceramic have always been a research hotspot. Based on previous research, this article conducted surface damage analysis on glass-ceramic using in-situ laser-assisted machining (LAM) orthogonal experimental results, and summarized that the surface damage of glass-ceramic mainly includes scratches, pits, and brittle fracture. Surface analysis confirmed that in-situ LAM can effectively reduce cutting forces and improve surface quality compared to conventional cutting. The artificial neural network (ANN) and genetic algorithm (GA) were used to fit and train and conduct multi-objective optimization for the data from in-situ LAM orthogonal experiments with resultant cutting force and surface roughness as eigenvalues. The Pareto optimal front curve with multiple groups of optimal solutions was obtained through multi-objective optimization using GA. The actual in-situ LAM experimental values were compared with the predicted values in the Pareto front, the relative error of the resultant force and the relative error of the surface roughness are both very small. In-situ LAM experiments based on response surface methodology (RSM) with surface roughness as the characteristic value were conducted. The optimal machining parameters for RSM optimization, as well as the minimum values for resultant force and surface roughness were obtained. Through comparative analysis, it was found that RSM has better multi-objective optimization performance than GA. Research content of this article provides reference and guidance for the multi-objective optimization analysis method of hard and brittle materials such as glass-ceramic after LAM.
This study investigated the tool wear behavior of white alumina (WA) and seeded gel (SG) abrasive wheels in the ultrasonic vibration-assisted creep feed grinding (UVACFG) of nickel-based single-crystal alloy. The abrasive wheel wear characteristics were examined, and their effects on the grinding force, temperature, and surface quality were evaluated. Finally, the wheel wear mechanism in the UVACFG was discussed by analyzing the intermittent cutting behavior and wear patterns of a single grain. Experimental results indicated that the workpiece material adhesion and pore-clogging were the main wear patterns of abrasive wheels without ultrasonic vibration. In contrast, these were adequately mitigated after introducing ultrasonic vibration, and the main wear pattern for both wheels became the grain fracture. The WA wheel in the UVACFG experienced reduced radial wear by around 60.7% in the initial wear state, 40.2% in the stable wear state, and 25.3% in the rapid wear stage, respectively, compared to the conventional grinding (CG). The intermittent cutting behavior of SG grains caused by high-frequency vibration promoted the micro-fracture capacity and ensured the coolant entered the grinding zone sufficiently, which reduced the grinding force and temperature by up to 63.2% and 46.7%, respectively, and meanwhile introduced the defects of high bulges and slight plastic deformation on the workpiece surface.
A precision grinding machine equipped with a cup grinding wheel has been employed for the aspheric grinding of spherical fused silica elements. This study aims to achieve two main objectives: firstly, to identify the contributions of feed speed and rotational speed ratio (RSR) on the profile and waviness errors of the workpieces and, secondly, to improve the prediction of profile errors by developing a high-fidelity data-driven machine learning framework. Accurate prediction of profile errors of the workpieces can be used to correct manufacturing errors. Furthermore, assessing the impact of input parameters on surface waviness errors is a viable approach to optimize the manufacturing process. Full-factorial experiments were designed for three levels of feed speed and RSR to find the sensitivity of modeling and predictions to these pertinent design variables. The trigonometric Fourier series model was used to fit form errors onto profile errors. The form errors evaluation revealed that RSR and the interaction between feed speed and RSR significantly affected the workpiece's form error. Reducing the feed speed led to a decrease in profile and waviness errors. The minimum waviness and profile errors were attained at an RSR of 50.25, regardless of the feed speed. The ensemble learning regression framework was subsequently trained using three sets of experimental datasets encompassing diverse combinations of RSR and feed speed values to model profile errors. While the profile model underwent validation on previously unseen segments of the datasets the network had been exposed to during training, the final tests were carried out using six datasets featuring entirely different profile errors that the networks had not seen before. The ensemble learning regression framework's performance tests were assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R-squared metrics. The framework demonstrated considerable predictive efficacy for the six left profile errors (unseen data), as evidenced by the calculated R-squared values from 0.767 to 0.965, signifying the lower and upper bounds of precision, respectively.
