Integrated optimisation of surface roughness and tool performance when face milling 416 SS

Tool life is an important factor to be considered during the optimisation of a machining process since cutting parameters can be adjusted to optimise tool changing, reducing cost and time of production. Also the performance of a tool is directly linked to the generated surface roughness and this is important in cases where there are strict surface quality requirements. The prediction of tool life and the resulting surface roughness in milling operations has attracted considerable research efforts. The research reported herein is focused on defining the influence of milling cutting parameters such as cutting speed, feed rate and axial depth of cut, on three major tool performance parameters namely, tool life, material removal and surface roughness. The research is seeking to define methods that will allow the selection of optimal parameters for best tool performance when face milling 416 stainless steel bars. For this study the Taguchi method was applied in a special design of an orthogonal array that allows studying the entire parameter space with only a number of experiments representing savings in cost and time of experiments. The findings were that the cutting speed has the most influence on tool life and surface roughness and very limited influence on material removal. By last tool life can be judged either from tool life or volume of material removal.

Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip°s width, and chip°s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed. © ASM International.

Empirical expression of tool wear when face milling 416 SS

During a machining process, cutting parameters must be taken into account, since depending on them the cutting edge starts to wear out to the point that tool can fail and needs to be change, which increases the cost and time of production. Since wear is a negative phenomenon on the cutting tool, due to the fact that tool life is reduced, it is important to optimize the cutting variables to be used during the machining process, in order to increase tool life. This research is focused on the influence of cutting parameters such as cutting speed, feed per tooth and axial depth of cut on tool wear during a face milling operation. The Taguchi method is applied in this study, since it uses a special design of orthogonal array to study the entire parameters space, with only few numbers of experiments. Also a relationship between tool wear and the cutting parameters is presented. For the studies, a martensitic 416 stainless steel was selected, due to the importance of this material in the machining of valve parts and pump shafts. Copyright © 2009 by ASME.