No abstract

Micromachining of advanced ceramics has been growing tremendously especially in the MEMs industry. All the time, researchers and industrial engineers have strived to achieve the lowest production cost at possible highest quality in micromachining operations. In this paper, the micromachining operation by means of chemical etching of ceramics is discussed. Machinable glass-ceramic (MGC) is used as the substrate and the influence of various input factors of the etching process is analyzed. These factors include etching temperature, etching period and, etching solution. The etching rate is then analyzed by calculating the weight loss per minutes. In order to establish the relationship between these factors, central composite design (CCD) and artificial neural network are used. Additionally, a prediction model that can be used with a high level of confidence in the industry is created at the end of the analysis.

The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization

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).