Surface roughness parameter prediction and evaluation are important factors in determining the satisfactory performance of machined surfaces in many fields. The recent trend towards the measurement and evaluation of surface roughness has led to renewed interest in the use of newly developed non-contact sensors. In the present work, an attempt has been made to measure the surface roughness parameter of different machined surfaces using a high sensitivity capacitive sensor. A capacitive response model is proposed to predict theoretical average capacitive surface roughness and compare it with the capacitive sensor measurement results. The measurements were carried out for 18 specimens using the proposed capacitive-sensor-based non-contact measurement setup. The results show that surface roughness values measured using a sensor well agree with the model output. For ground and milled surfaces, the correlation coefficients obtained are high, while for the surfaces generated by shaping, the correlation coefficient is low. It is observed that the sensor can effectively assess the fine and moderate rough-machined surfaces compared to rough surfaces generated by a shaping process. Furthermore, a linear regression model is proposed to predict the surface roughness from the measured average capacitive roughness. It can be further used in on-machine measurement, on-line monitoring and control of surface roughness in the machine tool environment.

This research presents an experimental study carried out for the modeling and optimization of some technological parameters for the machining of metallic materials. Certain controllable factors were analyzed such as cutting speed, depth of cut, and feed per tooth. A dedicated research methodology was used to obtain a model which subsequently led to a process optimization by performing a required number of experiments utilizing the Minitab software application. The methodology was followed, and the optimal value of the surface roughness was obtained by the milling process for an aluminum alloy type 7136-T76511. A SECO cutting tool was used, which is standard in aluminum machining by milling. Experiments led to defining a cutting regime that was optimal and which shows that the cutting speed has a significant influence on the quality of the machined surface and the depth of cut and feed per tooth has a relatively small impact on the chosen ranges of process parameters.