Abstract: Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.
Abstract: In this study, a multi objective optimization for end
milling of Al 6061 alloy has been presented to provide better
surface quality and higher Material Removal Rate (MRR). The input
parameters considered for the analysis are spindle speed, depth of cut
and feed. The experiments were planned as per Taguchis design of
experiment, with L27 orthogonal array. The Grey Relational Analysis
(GRA) has been used for transforming multiple quality responses
into a single response and the weights of the each performance
characteristics are determined by employing the Principal Component
Analysis (PCA), so that their relative importance can be properly and
objectively described. The results reveal that Taguchi based G-PCA
can effectively acquire the optimal combination of cutting parameters.
Abstract: The present work analyses different parameters of end
milling to minimize the surface roughness for AISI D2 steel. D2 Steel
is generally used for stamping or forming dies, punches, forming
rolls, knives, slitters, shear blades, tools, scrap choppers, tyre
shredders etc. Surface roughness is one of the main indices that
determines the quality of machined products and is influenced by
various cutting parameters. In machining operations, achieving
desired surface quality by optimization of machining parameters, is a
challenging job. In case of mating components the surface roughness
become more essential and is influenced by the cutting parameters,
because, these quality structures are highly correlated and are
expected to be influenced directly or indirectly by the direct effect of
process parameters or their interactive effects (i.e. on process
environment). In this work, the effects of selected process parameters
on surface roughness and subsequent setting of parameters with the
levels have been accomplished by Taguchi’s parameter design
approach. The experiments have been performed as per the
combination of levels of different process parameters suggested by
L9 orthogonal array. Experimental investigation of the end milling of
AISI D2 steel with carbide tool by varying feed, speed and depth of
cut and the surface roughness has been measured using surface
roughness tester. Analyses of variance have been performed for mean
and signal-to-noise ratio to estimate the contribution of the different
process parameters on the process.
Abstract: This work deals with problems of tool axis inclination angles in ball-end milling. Tool axis inclination angle contributes to improvement of functional surface properties (surface integrity - surface roughness, residual stress, micro hardness, etc.), decreasing cutting forces and improving production. By milling with ball-end milling tool, using standard way of cutting, when work piece and cutting tool contain right angle, we have zero cutting speed on edge. At this point cutting tool only pushes material into the work piece. Here we can observe the following undesirable effects - chip contraction, increasing of cutting temperature, increasing vibrations or creation of built-up edge. These effects have negative results – low quality of surface and decreasing of tool life (in the worse case even it is pinching out). These effects can be eliminated with the tilt of cutting tool or tilt of work piece.
Abstract: The chatter is one of the major limitations of the productivity in the ball end milling process. It affects the surface roughness, the dimensional accuracy and the tool life. The aim of this research is to propose the new system to detect the chatter during the ball end milling process by using the wavelet transform. The proposed method is implemented on the 5-axis CNC machining center and the new three parameters are introduced from three dynamic cutting forces, which are calculated by taking the ratio of the average variances of dynamic cutting forces to the absolute variances of themselves. It had been proved that the chatter can be easier to detect during the in-process cutting by using the new parameters which are proposed in this research. The experimentally obtained results showed that the wavelet transform can provide the reliable results to detect the chatter under various cutting conditions.
Abstract: Metal matrix composites have been increasingly used
as materials for components in automotive and aerospace industries
because of their improved properties compared with non-reinforced
alloys. During machining the selection of appropriate machining
parameters to produce job for desired surface roughness is of great
concern considering the economy of manufacturing process. In this
study, a surface roughness prediction model using fuzzy logic is
developed for end milling of Al-SiCp metal matrix composite
component using carbide end mill cutter. The surface roughness is
modeled as a function of spindle speed (N), feed rate (f), depth of cut
(d) and the SiCp percentage (S). The predicted values surface
roughness is compared with experimental result. The model predicts
average percentage error as 4.56% and mean square error as 0.0729.
It is observed that surface roughness is most influenced by feed rate,
spindle speed and SiC percentage. Depth of cut has least influence.
Abstract: This paper presents an experimental investigation on
the machinability of laser-sintered material using small ball end mill focusing on wear mechanisms. Laser-sintered material was produced
by irradiating a laser beam on a layer of loose fine SCM-Ni-Cu powder. Bulk carbon steel JIS S55C was selected as a reference steel.
The effects of powder consolidation mechanisms and unsintered
powder on the tool life and wear mechanisms were carried out. Results indicated that tool life in cutting laser-sintered material is
lower than that in cutting JIS S55C. Adhesion of the work material and chipping were the main wear mechanisms of the ball end mill in
cutting laser-sintered material. Cutting with the unsintered powder
surrounding the tool and laser-sintered material had caused major fracture on the cutting edge.
Abstract: Optimization of cutting parameters important in precision machining in regards to efficiency and surface integrity of the machined part. Usually productivity and precision in machining is limited by the forces emanating from the cutting process. Due to the inherent varying nature of the workpiece in terms of geometry and material composition, the peak cutting forces vary from point to point during machining process. In order to increase productivity without compromising on machining accuracy, it is important to control these cutting forces. In this paper a fuzzy logic control algorithm is developed that can be applied in the control of peak cutting forces in milling of spherical surfaces using ball end mills. The controller can adaptively vary the feedrate to maintain allowable cutting force on the tool. This control algorithm is implemented in a computer numerical control (CNC) machine. It has been demonstrated that the controller can provide stable machining and improve the performance of the CNC milling process by varying feedrate.
Abstract: This paper presents a study of the Taguchi design
application to optimize surface quality in damper inserted end milling
operation. Maintaining good surface quality usually involves
additional manufacturing cost or loss of productivity. The Taguchi
design is an efficient and effective experimental method in which a
response variable can be optimized, given various factors, using
fewer resources than a factorial design. This Study included spindle
speed, feed rate, and depth of cut as control factors, usage of different
tools in the same specification, which introduced tool condition and
dimensional variability. An orthogonal array of L9(3^4)was used;
ANOVA analyses were carried out to identify the significant factors
affecting surface roughness, and the optimal cutting combination was
determined by seeking the best surface roughness (response) and
signal-to-noise ratio. Finally, confirmation tests verified that the
Taguchi design was successful in optimizing milling parameters for
surface roughness.
Abstract: End milling process is one of the common metal
cutting operations used for machining parts in manufacturing
industry. It is usually performed at the final stage in manufacturing a
product and surface roughness of the produced job plays an
important role. In general, the surface roughness affects wear
resistance, ductility, tensile, fatigue strength, etc., for machined parts
and cannot be neglected in design. In the present work an
experimental investigation of end milling of aluminium alloy with
carbide tool is carried out and the effect of different cutting
parameters on the response are studied with three-dimensional
surface plots. An artificial neural network (ANN) is used to establish
the relationship between the surface roughness and the input cutting
parameters (i.e., spindle speed, feed, and depth of cut). The Matlab
ANN toolbox works on feed forward back propagation algorithm is
used for modeling purpose. 3-12-1 network structure having
minimum average prediction error found as best network architecture
for predicting surface roughness value. The network predicts surface
roughness for unseen data and found that the result/prediction is
better. For desired surface finish of the component to be produced
there are many different combination of cutting parameters are
available. The optimum cutting parameter for obtaining desired
surface finish, to maximize tool life is predicted. The methodology is
demonstrated, number of problems are solved and algorithm is coded
in Matlab®.