João Roberto Ferreira

TL;DR: The proposed tuning method leads to significant reduction of roughness prediction errors in machining operations in comparison to techniques currently used, and constitutes an effective option for the systematic design models based on ANN for prediction of surface roughness, filling the gap reported in the literature.

TL;DR: In this paper, the authors report practical experiments in turning, to study the performance of different tool materials such as ceramics, cemented carbide, cubic boron nitride (CBN), and diamond (PCD).

TL;DR: In this article, the influence of minimum volume of oil (MVO) on the wear of a cubic boron nitride (CBN) tool, when turning 52100 hardened steel, was studied.

TL;DR: In this article, the authors present a review of the helical milling process, which is an alternative hole-making machining process which presents several advantages when compared to conventional drilling.

TL;DR: The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the designs of RBF networks for roughness prediction than the most common trial and error approach.