A review on the erosion mechanisms in abrasive waterjet micromachining of brittle materials

The fabrication of miniature structures on components with high-integrity surface quality represents one of the cutting edge technologies in the 21st century.The materials used to construct such small structures are often difficult-to-machine.Many other readily available technologies either cannot realise necessary precision or are costly.Abrasive waterjet(AWJ)is a favourable technology for the machining of difficult-to-machine materials.However,this technology is generally aimed at large stock removal.A reduction in the scale of this technology is an attractive avenue for meeting the pressing need of industry in the production of damage-free micro features.This paper reviews some of the work that has been undertaken at UNSW Sydney about the development of such an AWJ technology,focusing on the system design currently employed to generate a micro abrasive jet,the erosion mechanisms associated with processing some typical brittle materials of both single-and two-phased.Processing models based on the findings are also presented.The review concludes on the viability of the technology and the prevailing trend in its development.

MODELING OF LASER MACHINING ON POLYMETHYL METHACRYLATE TO FABRICATE MICROFLUIDIC CHIP

The use of a CO2 laser system for fabrication of microfluidic chip on polymethyl methacrylate （PMMA） is presented to reduce fabrication cost and time of chip. The grooving process of the laser system and a model for the depth of microchannels are investigated. The relations between the depth of laser-cut channels and the laser beam power, velocity or the number of passes of the beam along the same channel are evaluated. In the experiments, the laser beam power varies from 0 to 50 W, the laser beam scanning velocity varies from 0 to 1 000 mm/s and the passes vary in the range of 1 to 10 times. Based on the principle of conservation of energy, the influence of the laser beam velocity, the laser power and the number of groove passes are examine. Considering the grooving interval energy loss, a modified mathematical model has been obtained and experimental data show good agreement with the theoretical model. This approach provides a simple way of predicting groove depths. The system provides a cost alternative of the other methods and it is especially useful on research work of rnicrofluidic prototyping due to the short cycle time of production.

MACHINING MECHANISM OF MIRROR-LIKE SURFACE OF ADVANCED CERAMICS

The conditions of convertion from brittle removal mechanism to plastic removal mecha-nism are studied through analysis of ecramic microstructure and the relatiotship between the ma-terial renicval form and machining unit. By the rabonal technology program worked out, theplane and cylindrical mirror-like surface machining is successfully achieved and its surfaceroughness Rais 0.01 ￣0.03 m after many times of expenmental study.

Advances in molecular dynamics simulation of ultra-precision machining of hard and brittle materials

Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.