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.

Manufacturing process challenges when machining superalloys

The implementation of advanced manufacturing systems with high process capability is an essential requirement for the high value manufacturing industries. To ensure high process capability, industry needs to deal with the requirement for tight tolerances and the unavoidable variations in materials, and manufacturing and inspection processes. In the case of machining superalloys, such variations result in the need to change the machine parameters for producing different batches of materials from different suppliers. This is required in order to get the process under control and reduce waste and defects, leading to better competitiveness. This papers discuss the variability in materials and the corresponding process requirements when machining superalloys, and highlights the impact of metrology in achieving manufacturing process improvement.

Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control

Deep hole drilling is one of the most complicated metal cutting processes and one of the most difficult to perform on CNC machine-tools or machining centres under conditions of limited manpower or unmanned operation. This research work investigates aspects of the deep hole drilling process with small diameter twist drills and presents a prototype system for real time process monitoring and adaptive control; two main research objectives are fulfilled in particular : First objective is the experimental investigation of the mechanics of the deep hole drilling process, using twist drills without internal coolant supply, in the range of diarneters Ø 2.4 to Ø4.5 mm and working length up to 40 diameters. The definition of the problems associated with the low strength of these tools and the study of mechanisms of catastrophic failure which manifest themselves well before and along with the classic mechanism of tool wear. The relationships between drilling thrust and torque with the depth of penetration and the various machining conditions are also investigated and the experimental evidence suggests that the process is inherently unstable at depths beyond a few diameters. Second objective is the design and implementation of a system for intelligent CNC deep hole drilling, the main task of which is to ensure integrity of the process and the safety of the tool and the workpiece. This task is achieved by means of interfacing the CNC system of the machine tool to an external computer which performs the following functions: On-line monitoring of the drilling thrust and torque, adaptive control of feed rate, spindle speed and tool penetration (Z-axis), indirect monitoring of tool wear by pattern recognition of variations of the drilling thrust with cumulative cutting time and drilled depth, operation as a data base for tools and workpieces and finally issuing of alarms and diagnostic messages.

Influence of cutting parameters and tool wear on martensitic stainless steel surface integrity after a face milling process

Surface finish is one of the most relevant aspects of machining operations, since it is one of the principle methods to assess quality. Also, surface finish influences mechanical properties such as fatigue behavior, wear, corrosion, etc. The feed, the cutting speed, the cutting tool material, the workpiece material and the cutting tool wear are some of the most important factors that affects the surface roughness of the machined surface. Due to the importance of the martensitic 416 stainless steel in the petroleum industry, especially in valve parts and pump shafts, this material was selected to study the influence of the feed per tooth and cutting speed on tool wear and surface integrity. Also the influence of tool wear on surface roughness is analyzed. Results showed that high values of roughness are obtained when using low cutting speed and feed per tooth and by using these conditions tool wear decreases prolonging tool life. Copyright © 2009 by ASME.