Abstract: In the process of polyethylene extrusion polymer
material similar to powder or granule is under compression, melting
and transmission operation and on base of special form, extrudate has
been produced. Twin-screw extruders are applicable in industries
because of their high capacity. The powder mixing with chemical
additives and melting with thermal and mechanical energy in three
zones (feed, compression and metering zone) and because of gear
pump and screw's pressure, converting to final product in latest plate.
Extruders with twin-screw and short distance between screws are
better than other types because of their high capacity and good
thermal and mechanical stress.
In this paper, process of polyethylene extrusion and various tapes
of extruders are studied. It is necessary to have an exact control on
process to producing high quality products with safe operation and
optimum energy consumption.
The granule size is depending on granulator motor speed. Results
show at constant feed rate a decrease in granule size was found whit
Increase in motor speed. Relationships between HDPE feed rate and
speed of granulator motor, main motor and gear pump are calculated
following as:
x = HDPE feed flow rate, yM = Main motor speed
yM = (-3.6076e-3) x^4+ (0.24597) x^3+ (-5.49003) x^2+ (64.22092)
x+61.66786 (1)
x = HDPE feed flow rate, yG = Gear pump speed
yG = (-2.4996e-3) x^4+ (0.18018) x^3+ (-4.22794) x^2+ (48.45536)
x+18.78880 (2)
x = HDPE feed flow rate, y = Granulator motor speed
10th Degree Polynomial Fit: y = a+bx+cx^2+dx^3... (3)
a = 1.2751, b = 282.4655, c = -165.2098,
d = 48.3106, e = -8.18715, f = 0.84997
g = -0.056094, h = 0.002358, i = -6.11816e-5
j = 8.919726e-7, k = -5.59050e-9
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: The paper presents a novel method for the 3D shaping
of different materials using a high-pressure abrasive water jet and a
flat target image. For steering movement process of the jet a principle
similar to raster image way of record and readout was used.
However, respective colors of pixel of such a bitmap are connected
with adequate jet feed rate that causes erosion of material with
adequate depth. Thanks to that innovation, one can observe spatial
imaging of the object. Theoretical basis as well as spatial model of
material shaping and experimental stand including steering program
are presented in. There are also presented methodic and some
experimental erosion results as well as practical example of object-s
bas-relief made of metal.
Abstract: A judicious choice of insert material, tool geometry and
cutting conditions can make hard turning produce better surfaces than
grinding. In the present study, an attempt has been made to
investigate the effect of cutting tool materials on cutting forces (feed
force, thrust force and cutting force) in finish hard turning of AISI
D2 cold work tool steel. In conclusion of the results obtained with a
constant depth of cut and feed rate, it is important to note that cutting
force is directly affected by cutting tool material.
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: In present work, prediction the effect of nose radius, rz (mm) on the equivalent strain (PEEQ) and surface finish during the machining of titanium alloy (Ti-6Al-4V) through orthogonal cutting process. The results were performed at several of the nose radiuses, rz (mm) while the cutting speed, vc (m/min), feed rate, f (mm/tooth) and depth of cut, d (mm) were remained constant. The equivalent plastic strain (PEEQ) was estimated by using finite element modeling (FEM) and applied through ABAQUS/EXPLICIT software. The simulation results led to conclude that the equivalent plastic strain (PEEQ) was increased and surface roughness (Ra) decreased when increasing nose radius, rz (mm) during the machining of titanium alloy (Ti–6Al–4V) in dry cutting conditions.
Abstract: The objective of this research is parameters optimized
of the stair shape workpiece which is cut by CNC Wire-Cut EDM
(WEDW). The experiment material is SKD-11 steel of stair-shaped
with variable height workpiece 10, 20, 30 and 40 mm. with the same
10 mm. thickness are cut by Sodick's CNC Wire-Cut EDM model
AD325L.
The experiments are designed by 3k full factorial experimental
design at 3 level 2 factors and 9 experiments with 2 replicate. The
selected two factor are servo voltage (SV) and servo feed rate (SF)
and the response is cutting thickness error. The experiment is divided
in two experiments. The first experiment determines the significant
effective factor at confidential interval 95%. The SV factor is the
significant effective factor from first result. In order to result smallest
cutting thickness error of workpieces is 17 micron with the SV value
is 46 volt. Also show that the lower SV value, the smaller different
thickness error of workpiece. Then the second experiment is done to
reduce different cutting thickness error of workpiece as small as
possible by lower SV. The second experiment result show the
significant effective factor at confidential interval 95% is the SV
factor and the smallest cutting thickness error of workpieces reduce
to 11 micron with the experiment SV value is 36 volt.
Abstract: Inconel 718, a nickel based super-alloy is an
extensively used alloy, accounting for about 50% by weight of
materials used in an aerospace engine, mainly in the gas turbine
compartment. This is owing to their outstanding strength and
oxidation resistance at elevated temperatures in excess of 5500 C.
Machining is a requisite operation in the aircraft industries for the
manufacture of the components especially for gas turbines. This
paper is concerned with optimization of the surface roughness when
turning Inconel 718 with cermet inserts. Optimization of turning
operation is very useful to reduce cost and time for machining. The
approach is based on Response Surface Method (RSM). In this work,
second-order quadratic models are developed for surface roughness,
considering the cutting speed, feed rate and depth of cut as the cutting
parameters, using central composite design. The developed models
are used to determine the optimum machining parameters. These
optimized machining parameters are validated experimentally, and it
is observed that the response values are in reasonable agreement with
the predicted values.
Abstract: This paper presents an advance in monitoring and
process control of surface roughness in CNC machine for the turning
and milling processes. An integration of the in-process monitoring
and process control of the surface roughness is proposed and
developed during the machining process by using the cutting force
ratio. The previously developed surface roughness models for turning
and milling processes of the author are adopted to predict the inprocess
surface roughness, which consist of the cutting speed, the
feed rate, the tool nose radius, the depth of cut, the rake angle, and
the cutting force ratio. The cutting force ratios obtained from the
turning and the milling are utilized to estimate the in-process surface
roughness. The dynamometers are installed on the tool turret of CNC
turning machine and the table of 5-axis machining center to monitor
the cutting forces. The in-process control of the surface roughness
has been developed and proposed to control the predicted surface
roughness. It has been proved by the cutting tests that the proposed
integration system of the in-process monitoring and the process
control can be used to check the surface roughness during the cutting
by utilizing the cutting force ratio.
Abstract: The objective of this research was to study factors,
which were affected on surface roughness in high speed milling of
hardened tool steel. Material used in the experiment was tool steel JIS
SKD 61 that hardened on 60 ±2 HRC. Full factorial experimental
design was conducted on 3 factors and 3 levels (3
3
designs) with 2
replications. Factors were consisted of cutting speed, feed rate, and
depth of cut. The results showed that influenced factor affected to
surface roughness was cutting speed, feed rate and depth of cut which
showed statistical significant. Higher cutting speed would cause on
better surface quality. On the other hand, higher feed rate would cause
on poorer surface quality. Interaction of factor was found that cutting
speed and depth of cut were significantly to surface quality. The
interaction of high cutting speed associated with low depth of cut
affected to better surface quality than low cutting speed and high depth
of cut.
Abstract: The dynamical contouring error is a critical element for the accuracy of machine tools. The contouring error is defined as the difference between the processing actual path and commanded path, which is implemented by following the command curves from feeding driving system in machine tools. The contouring error is resulted from various factors, such as the external loads, friction, inertia moment, feed rate, speed control, servo control, and etc. Thus, the study proposes a 2D compensating system for the contouring accuracy of machine tools. Optical method is adopted by using stable frequency laser diode and the high precision position sensor detector (PSD) to performno-contact measurement. Results show the related accuracy of position sensor detector (PSD) of 2D contouring accuracy compensating system was ±1.5 μm for a calculated range of ±3 mm, and improvement accuracy is over 80% at high-speed feed rate.
Abstract: The quality of a machined surface is becoming more and more important to justify the increasing demands of sophisticated component performance, longevity, and reliability. Usually, any machining operation leaves its own characteristic evidence on the machined surface in the form of finely spaced micro irregularities (surface roughness) left by the associated indeterministic characteristics of the different elements of the system: tool-machineworkpart- cutting parameters. However, one of the most influential sources in machining affecting surface roughness is the instantaneous state of tool edge. The main objective of the current work is to relate the in-process immeasurable cutting edge deformation and surface roughness to a more reliable easy-to-measure force signals using a robust non-linear time-dependent modeling regression techniques. Time-dependent modeling is beneficial when modern machining systems, such as adaptive control techniques are considered, where the state of the machined surface and the health of the cutting edge are monitored, assessed and controlled online using realtime information provided by the variability encountered in the measured force signals. Correlation between wear propagation and roughness variation is developed throughout the different edge lifetimes. The surface roughness is further evaluated in the light of the variation in both the static and the dynamic force signals. Consistent correlation is found between surface roughness variation and tool wear progress within its initial and constant regions. At the first few seconds of cutting, expected and well known trend of the effect of the cutting parameters is observed. Surface roughness is positively influenced by the level of the feed rate and negatively by the cutting speed. As cutting continues, roughness is affected, to different extents, by the rather localized wear modes either on the tool nose or on its flank areas. Moreover, it seems that roughness varies as wear attitude transfers from one mode to another and, in general, it is shown that it is improved as wear increases but with possible corresponding workpart dimensional inaccuracy. The dynamic force signals are found reasonably sensitive to simulate either the progressive or the random modes of tool edge deformation. While the frictional force components, feeding and radial, are found informative regarding progressive wear modes, the vertical (power) components is found more representative carrier to system instability resulting from the edge-s random deformation.
Abstract: The objective of this study is to design an adaptive
neuro-fuzzy inference system (ANFIS) for estimation of surface
roughness in grinding process. The Used data have been generated
from experimental observations when the wheel has been dressed
using a rotary diamond disc dresser. The input parameters of model
are dressing speed ratio, dressing depth and dresser cross-feed rate
and output parameter is surface roughness. In the experimental
procedure the grinding conditions are constant and only the dressing
conditions are varied. The comparison of the predicted values and the
experimental data indicates that the ANFIS model has a better
performance with respect to back-propagation neural network
(BPNN) model which has been presented by the authors in previous
work for estimation of the surface roughness.
Abstract: Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
Abstract: The present work is concerned with the effect of turning process parameters (cutting speed, feed rate, and depth of cut) and distance from the center of work piece as input variables on the chip micro-hardness as response or output. Three experiments were conducted; they were used to investigate the chip micro-hardness behavior at diameter of work piece for 30[mm], 40[mm], and 50[mm]. Response surface methodology (R.S.M) is used to determine and present the cause and effect of the relationship between true mean response and input control variables influencing the response as a two or three dimensional hyper surface. R.S.M has been used for designing a three factor with five level central composite rotatable factors design in order to construct statistical models capable of accurate prediction of responses. The results obtained showed that the application of R.S.M can predict the effect of machining parameters on chip micro-hardness. The five level factorial designs can be employed easily for developing statistical models to predict chip micro-hardness by controllable machining parameters. Results obtained showed that the combined effect of cutting speed at it?s lower level, feed rate and depth of cut at their higher values, and larger work piece diameter can result increasing chi micro-hardness.
Abstract: Nowadays, the demand for high product quality
focuses extensive attention to the quality of machined surface. The
(CNC) milling machine facilities provides a wide variety of
parameters set-up, making the machining process on the glass
excellent in manufacturing complicated special products compared to
other machining processes. However, the application of grinding
process on the CNC milling machine could be an ideal solution to
improve the product quality, but adopting the right machining
parameters is required. In glass milling operation, several machining
parameters are considered to be significant in affecting surface
roughness. These parameters include the lubrication pressure, spindle
speed, feed rate and depth of cut. In this research work, a fuzzy logic
model is offered to predict the surface roughness of a machined
surface in glass milling operation using CBN grinding tool. Four
membership functions are allocated to be connected with each input
of the model. The predicted results achieved via fuzzy logic model
are compared to the experimental result. The result demonstrated
settlement between the fuzzy model and experimental results with the
93.103% accuracy.
Abstract: Reliable information about tool temperature
distribution is of central importance in metal cutting. In this study,
tool-chip interface temperature was determined in cutting of ST37
steel workpiece by applying HSS as the cutting tool in dry turning.
Two different approaches were implemented for temperature
measuring: an embedded thermocouple (RTD) in to the cutting tool
and infrared (IR) camera. Comparisons are made between
experimental data and results of MSC.SuperForm and FLUENT
software.
An investigation of heat generation in cutting tool was performed
by varying cutting parameters at the stable cutting tool geometry and
results were saved in a computer; then the diagrams of tool
temperature vs. various cutting parameters were obtained. The
experimental results reveal that the main factors of the increasing
cutting temperature are cutting speed (V ), feed rate ( S ) and depth
of cut ( h ), respectively. It was also determined that simultaneously
change in cutting speed and feed rate has the maximum effect on
increasing cutting temperature.
Abstract: Abrasive waterjet cutting (AWJ) is a highly efficient
method for cutting almost any type of material. When holes shall be
cut the waterjet first needs to pierce the material.This paper presents a
vast experimental analysis of piercing parameters effect on piercing
time. Results from experimentation on feed rates, work piece
thicknesses, abrasive flow rates, standoff distances and water
pressure are also presented as well as studies on three methods for
dynamic piercing. It is shown that a large amount of time and
resources can be saved by choosing the piercing parameters in a
correct way. The large number of experiments puts demands on the
experimental setup. An automated experimental setup including
piercing detection is presented to enable large series of experiments
to be carried out efficiently.
Abstract: Deep cold rolling (DCR) and low plasticity burnishing (LPB) process are cold working processes, which easily produce a smooth and work-hardened surface by plastic deformation of surface irregularities. The present study focuses on the surface roughness and surface hardness aspects of AISI 4140 work material, using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in order to identify the predominant factors amongst the selected parameters. They were then categorized in order of significance followed by setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. In the present work, the influence of main process parameters (force, feed rate, number of tool passes/overruns, initial roughness of the work piece, ball material, ball diameter and lubricant used) on the surface roughness and the hardness of AISI 4140 steel were studied for both LPB and DCR process and the results are compared. It was observed that by using LPB process surface hardness has been improved by 167% and in DCR process surface hardness has been improved by 442%. It was also found that the force, ball diameter, number of tool passes and initial roughness of the workpiece are the most pronounced parameters, which has a significant effect on the work piece-s surface during deep cold rolling and low plasticity burnishing process.
Abstract: A mathematical model of the surface roughness
has been developed by using response surface methodology
(RSM) in grinding of AISI D2 cold work tool steels. Analysis
of variance (ANOVA) was used to check the validity of the
model. Low and high value for work speed and feed rate are
decided from design of experiment. The influences of all
machining parameters on surface roughness have been
analyzed based on the developed mathematical model. The
developed prediction equation shows that both the feed rate
and work speed are the most important factor that influences
the surface roughness. The surface roughness was found to be
the lowers with the used of low feed rate and low work speed.
Accuracy of the best model was proved with the testing data.