Chemical machining of advanced ceramics

Not until recently did we see an enormous surge of interest in the study of machining of advanced ceramics. This has resulted in significant advances lately in their development and usage. Machinable glass ceramics, boron nitride and silicon carbide are commonly used in the industry and their major features of attraction are their inherent properties. Previous studies on machining of these materials were mainly performed by other machining methods, such as electrode discharge machining, laser beam machining and abrasive jet machining. Although chemical machining is one of the oldest machining methods employed, the literature survey reveals a lack of knowledge in this particular aspect. Further understanding is required on the chemical machining characteristics of advanced ceramics as well as their performance and relationship between the variables and parameters involved in the process. Therefore, the aim of our study is to examine and establish the relationship between etching rate, surface roughness and dimensional accuracy with the relevant variables involved and at the same time to develop the predictive models for all outputs that we believe are beneficial to the manufacturing industries.A comprehensive review was written and published recently in a Journal on the current advanced ceramics machining techniques [1]. The chemical machining process was successfully conducted in this study with a variety of selected etchants. Using the RSM methodology the first and second order models were developed to study the chemical machining process and relationship between the outputs (etching rate, surface roughness and dimensional accuracy) with the selected variables, namely, etching temperature, etching duration, etchant and etchant’s concentration. A number of predictive models were developed followed by optimisation studies of chemical machining to obtain the best performance of chemical machining of advanced ceramics. Artificial neural network was also used as the analytical tool to evaluate the experimental data and validate the results generated by response surface roughness, and both results were found to be in good agreement with each other. Artificial neural network was performed by software of NeuroSolution 5.From the chemical etching studies both the etching temperature and etchant used have significant influence on the etch rate. Generally, the higher the etching temperature the greater the etch rates was observed for the substrates. The best etch rate was found in HBr etchant for MGC and BN, and the highest etch rate performance for SiC was found in H3PO4 etchant. For surface roughness, different substrates were found to be influenced by different variables. For MGC and BN, these substrates were affected by etching temperature and the best surface roughness occurred at high etching temperature of 90oC. Etching duration was also found to be critical in determining the quality of SiC surface roughness during chemical machining.Experimental data revealed that etching rate was closely correlated to surface roughness as well as the etching ratio. However, using the best etching rate it failed to yield the quality surface roughness, but produced the best etching ratio. Each variable presented different level of significance for each output of chemical machining. The results of etch rate and etch ratio also showed that etching temperature and etching duration imparted significant impact on the chemical machining of all substrates. In the analysis of surface roughness, etching temperature was found to be the critical variable in chemical machining of machinable glass ceramics. Etching temperature and etchant influenced the surface roughness of boron nitride whereas surface roughness of silicon carbide was more dependent on etching duration and etchant used.Predictive models were developed using DE 7 once the analysis of data was completed. A total of 27 predictive models were developed for each substrate and each output. This predictive model can be used directly in the industry with the selected substrate and etchant. Optimisation of chemical machining was also performed. For machinable glass ceramic, the optimum of chemical machining happened at 100oC in 10.5 molarity HCl etchant for 30 minutes. Results of chemical machining of machinable glass ceramics were obtained with optimal etching rate of 0.0008g/min, surface roughness improvement of 81.818nm (48% improvement) and etching ratio of 3.403. In chemical etching of boron nitride, the best result occurred at 40oC in 6 molarity HBr for 62 minutes. The etching rate obtained for BN is 0.00025g/min, with surface roughness improvement of 0.01nm (16% improvement) and etching ratio of 3.153. For the chemical etching of silicon carbide, the best performance occurred at 75oC in 8.5 molarity of HBr for 240 minutes. The optimal value of etching rate for silicon carbide is 0.0009g/min, with surface roughness improvement of 128.71um (35% improvement) and etching ratio of 10.004

Etch Rate and Dimensional Accuracy of Machinable Glass Ceramics in Chemical Etching

Machinable glass ceramic (MGC) is well known in the micro-electromechanical system and semiconductor industry. Chemical etching is used in this experiment to study the performance of MGC. The etching rate of MGC and its accuracy by indentation method is studied. The categoric parameter applied here is the type of chemical etchant used: hydrochloric (HCl), hydrophosphoric (H3PO4) and hydrobromic (HBr) acids; and, numerical parameters are etching temperature and etching solution. The experimental investigation that was carried out is governed by design of experiment (DoE)

Prediction of Etching Rate of Alumino-Silicate Glass by RSM and ANN

920-924In this study, response surface methodology (RSM) andartificial neural network (ANN) were applied to predict material removal rate in chemical etching process of alumino-silicate glass (SiO2 57/Al2O3 36/CaO/MgO/BaO). 2k Factorial design was performed to evaluate linearity condition among process parameters. Analysis of variance (ANOVA) was performed and quadratic model was found most significant for data values of process parameters. New models were able to predict etching rate of alumino-silicate glass, with a great confidence. Input parameters analyzed were temperature, etching period and type of setup with and without condensation