Parameters for tool life optimisation with respect to face milling

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Feasibility of automating the production of carbide-tipped milling cutters

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Diffusion of technology in flour milling and the emergence of the modern British milling industry

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On a Singular Value Problem for the Fractional Laplacian on the Exterior of the Unit Ball

2000 Mathematics Subject Classification: Primary 26A33; Secondary 47G20, 31B05

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8. mm and 2.5. mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data. © 2014 The Society of Manufacturing Engineers.

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.

Tool cutting force modeling and wear estimation of micro -end-milling operations

The applications of micro-end-milling operations have increased recently. A Micro-End-Milling Operation Guide and Research Tool (MOGART) package has been developed for the study and monitoring of micro-end-milling operations. It includes an analytical cutting force model, neural network based data mapping and forecasting processes, and genetic algorithms based optimization routines. MOGART uses neural networks to estimate tool machinability and forecast tool wear from the experimental cutting force data, and genetic algorithms with the analytical model to monitor tool wear, breakage, run-out, cutting conditions from the cutting force profiles. ^ The performance of MOGART has been tested on the experimental data of over 800 experimental cases and very good agreement has been observed between the theoretical and experimental results. The MOGART package has been applied to the micro-end-milling operation study of Engineering Prototype Center of Radio Technology Division of Motorola Inc. ^

An experimental study of micro-end-milling operations

Cutting tools less than 2mm diameter can be considered as micro-tool. Microtools are used in variety of applications where precision and accuracy are indispensable. In micro-machining operations, a small amount of material is removed and very small cutting forces are created. The small cross sectional area of the micro-tools drastically reduces their strength and makes their useful life short and unpredictable; so cutting parameters should be selected carefully to avoid premature tool breakage. The main objective of this study is to develop new techniques to select the optimal cutting conditions with minimum number of experiments and to evaluate the tool wear in machining operations. Several experimental setups were prepared and used to investigate the characteristics of cutting force and AE signals during the micro-end-milling of different materials including steel, aluminum and graphite electrodes. The proposed optimal cutting condition selection method required fewer experiments than conventional approaches and avoided premature tool breakage. The developed tool wear monitoring technique estimated the used tool life with ±10% accuracy from the machining data collected during the end-milling of non-metal materials.

Tool cutting force modeling and wear estimation of micro-end-milling operations

The applications of micro-end-milling operations have increased recently. A Micro-End-Milling Operation Guide and Research Tool (MOGART) package has been developed for the study and monitoring of micro-end-milling operations. It includes an analytical cutting force model, neural network based data mapping and forecasting processes, and genetic algorithms based optimization routines. MOGART uses neural networks to estimate tool machinability and forecast tool wear from the experimental cutting force data, and genetic algorithms with the analytical model to monitor tool wear, breakage, run-out, cutting conditions from the cutting force profiles. The performance of MOGART has been tested on the experimental data of over 800 experimental cases and very good agreement has been observed between the theoretical and experimental results. The MOGART package has been applied to the micro-end-milling operation study of Engineering Prototype Center of Radio Technology Division of Motorola Inc.

Estudo de um compósito Nb-15%pCu obtidos por moagem de alta energia e sinterizados por fase líquida

There is great difficulty in forming a composite refractory metal niobium with copper. This is due to the fact that Nb-Cu system is almost mutually immiscible and may be neglected solubility between them. These properties hinder or prevent obtaining homogeneous and high-density structures, conventionally prepared. This study aims to analyze the use of high-energy milling process (MAE) to implement these natural difficulties, with regard to the densification of the sintered bodies. The MAE and the press were used in the preparation of powders, to obtain a fine and homogeneous distribution of the grain size. Four loads Nb and Cu powders containing 15% by weight of Cu were then milled for MAE in a planetary type ball mill under various milling times and speeds. The results obtained by MAE were analyzed by scanning electron microscopy (SEM), according to the parameters of time and grinding speed. The samples were compacted under pressure of 200 MPa, were then sintered in liquid phase in a vacuum furnace at temperatures of 1100 ° C / 60 min and 1200 ° C / 60 min. Then it was used to characterize diffraction of X-rays to identify the phases. The microstructures of the sintered samples were observed and evaluated using scanning electron microscopy (SEM). Vickers Microhardness tests were performed, obtaining higher values for the sintered bodies in the largest of the post milling times and the larger grinding speeds. It was found that the liquid phase sintering of the samples that were processed by MAE produced at the end of a homogeneous and densified structure in 77,4% relative to the value of the theoretical density of the composite

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