Abstract: Machining instability, or chatter, can impose an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the TU Wien, Vienna, Austria. The recorded data from a milling test cut were used to classify the cut as stable or unstable based on a frequency analysis. The test cut result was used in a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculated the probability of stability as a function of axial depth of cut and spindle speed based on the test result and recommended parameters for the next test cut. The iterative process between two transatlantic locations was repeated until convergence to a stable optimal process parameter set was achieved.
Abstract: Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.
Abstract: Electron Beam Melting (EBM) is a metal powder bed-based Additive Manufacturing (AM) technology, which uses computer-controlled electron beams to create fully dense three-dimensional near-net-shaped parts from metal powder. It gives the ability to produce any complex parts directly from a computer-aided design (CAD) model without tools and dies, and with a variety of materials. However, the quality of the surface finish in EBM process has limitations to meeting the performance requirements of additively manufactured components. The aim of this study is to investigate the cutting forces induced during milling Ti6Al4V produced by EBM as well as the surface quality of the milled surfaces. The effects of cutting speed and radial depth of cut on the cutting forces, surface roughness, and surface morphology were investigated. The results indicated that the cutting speed was found to be proportional to the resultant cutting force at any cutting conditions while the surface roughness improved significantly with the increase in cutting speed and radial depth of cut.
Abstract: The main objective of this research is to optimize the surface roughness of a milling operation on AISI 1018 steel using live tooling on a HAAS ST-20 lathe. In this study, Taguchi analysis is used to optimize the milling process by investigating the effect of different machining parameters on surface roughness. The L9 orthogonal array is designed with four controllable factors with three different levels each and an uncontrollable factor, resulting in 18 experimental runs. The optimal parameters determined from Taguchi analysis were feed rate – 76.2 mm/min, spindle speed 1150 rpm, depth of cut – 0.762 mm and 2-flute TiN coated high-speed steel as tool material. The process capability Cp and process capability index Cpk values were improved from 0.62 and -0.44 to 1.39 and 1.24 respectively. The average surface roughness values from the confirmation runs were 1.30 µ, decreasing the defect rate from 87.72% to 0.01%. The purpose of this study is to efficiently utilize the Taguchi design to optimize the surface roughness in a milling operation using live tooling.
Abstract: The research follows a systematic approach to optimize the parameters for parts machined by turning and milling processes. The quality characteristic chosen is surface roughness since the surface finish plays an important role for parts that require surface contact. A tapered cylindrical surface is designed as a test specimen for the research. The material chosen for machining is aluminum alloy 6061 due to its wide variety of industrial and engineering applications. HAAS VF-2 TR computer numerical control (CNC) vertical machining center is used for milling and HAAS ST-20 CNC machine is used for turning in this research. Taguchi analysis is used to optimize the surface roughness of the machined parts. The L9 Orthogonal Array is designed for four controllable factors with three different levels each, resulting in 18 experimental runs. Signal to Noise (S/N) Ratio is calculated for achieving the specific target value of 75 ± 15 µin. The controllable parameters chosen for turning process are feed rate, depth of cut, coolant flow and finish cut and for milling process are feed rate, spindle speed, step over and coolant flow. The uncontrollable factors are tool geometry for turning process and tool material for milling process. Hypothesis testing is conducted to study the significance of different uncontrollable factors on the surface roughnesses. The optimal parameter settings were identified from the Taguchi analysis and the process capability Cp and the process capability index Cpk were improved from 1.76 and 0.02 to 3.70 and 2.10 respectively for turning process and from 0.87 and 0.19 to 3.85 and 2.70 respectively for the milling process. The surface roughnesses were improved from 60.17 µin to 68.50 µin, reducing the defect rate from 52.39% to 0% for the turning process and from 93.18 µin to 79.49 µin, reducing the defect rate from 71.23% to 0% for the milling process. The purpose of this study is to efficiently utilize the Taguchi design analysis to improve the surface roughness.
Abstract: This paper focuses on using Six Sigma methodologies to improve the surface roughness of a manufactured part produced by the CNC milling machine. It presents a case study where the surface roughness of milled aluminum is required to reduce or eliminate defects and to improve the process capability index Cp and Cpk for a CNC milling process. The six sigma methodology, DMAIC (design, measure, analyze, improve, and control) approach, was applied in this study to improve the process, reduce defects, and ultimately reduce costs. The Taguchi-based six sigma approach was applied to identify the optimized processing parameters that led to the targeted surface roughness specified by our customer. A L9 orthogonal array was applied in the Taguchi experimental design, with four controllable factors and one non-controllable/noise factor. The four controllable factors identified consist of feed rate, depth of cut, spindle speed, and surface roughness. The noise factor is the difference between the old cutting tool and the new cutting tool. The confirmation run with the optimal parameters confirmed that the new parameter settings are correct. The new settings also improved the process capability index. The purpose of this study is that the Taguchi–based six sigma approach can be efficiently used to phase out defects and improve the process capability index of the CNC milling process.
Abstract: Many orthopedic implants like proximal humerus cases require lower surface roughness and almost immediate/short lead time surgery. Thus, rapid response from the manufacturer is very crucial. Tool path strategy of milling process has a direct influence on the surface roughness and lead time of medical implant. High-speed milling as promised process would improve the machined surface quality, but conventional or super-abrasive grinding still required which imposes some drawbacks such as additional costs and time. Currently, many CAD/CAM software offers some different tool path strategies to milling free form surfaces. Nevertheless, the users must identify how to choose the strategies according to cutting tool geometry, geometry complexity, and their effects on the machined surface. This study investigates the effect of different tool path strategies for milling a proximal humerus head during finishing operation on stainless steel 316L. Experiments have been performed using MAHO MH700 S vertical milling machine and four machining strategies, namely, spiral outward, spiral inward, and radial as well as zig-zag. In all cases, the obtained surfaces were analyzed in terms of roughness and dimension accuracy compared with those obtained by simulation. The findings provide evidence that surface roughness, dimensional accuracy, and machining time have been affected by the considered tool path strategy.
Abstract: Chatter vibrations and process instabilities are the
most important factors limiting the productivity of the milling
process. Chatter can leads to damage of the tool, the part or the
machine tool. Therefore, the estimation and prediction of the process
stability is very important. The process stability depends on the
spindle speed, the depth of cut and the width of cut. In milling, the
process conditions are defined in the NC-program. While the spindle
speed is directly coded in the NC-program, the depth and width of cut
are unknown. This paper presents a new simulation based approach
for the prediction of the depth and width of cut of a milling process.
The prediction is based on a material removal simulation with an
analytically represented tool shape and a multi-dexel approach for the
workpiece. The new calculation method allows the direct estimation
of the depth and width of cut, which are the influencing parameters of
the process stability, instead of the removed volume as existing
approaches do. The knowledge can be used to predict the stability of
new, unknown parts. Moreover with an additional vibration sensor,
the stability lobe diagram of a milling process can be estimated and
improved based on the estimated depth and width of cut.
Abstract: This study investigates the effects of the lead angle
and chip thickness variation on surface roughness during the
machining of compacted graphite iron using ceramic cutting tools
under dry cutting conditions. Analytical models were developed for
predicting the surface roughness values of the specimens after the
face milling process. Experimental data was collected and imported
to the artificial neural network model. A multilayer perceptron model
was used with the back propagation algorithm employing the input
parameters of lead angle, cutting speed and feed rate in connection
with chip thickness. Furthermore, analysis of variance was employed
to determine the effects of the cutting parameters on surface
roughness. Artificial neural network and regression analysis were
used to predict surface roughness. The values thus predicted were
compared with the collected experimental data, and the
corresponding percentage error was computed. Analysis results
revealed that the lead angle is the dominant factor affecting surface
roughness. Experimental results indicated an improvement in the
surface roughness value with decreasing lead angle value from 88° to
45°.
Abstract: The aim of this investigation is to elaborate nearinfrared
methods for testing and recognition of chemical components
and quality in “Pannon wheat” allied (i.e. true to variety or variety
identified) milling fractions as well as to develop spectroscopic
methods following the milling processes and evaluate the stability of
the milling technology by different types of milling products and
according to sampling times, respectively. These wheat categories
produced under industrial conditions where samples were collected
versus sampling time and maximum or minimum yields. The changes
of the main chemical components (such as starch, protein, lipid) and
physical properties of fractions (particle size) were analysed by
dispersive spectrophotometers using visible (VIS) and near-infrared
(NIR) regions of the electromagnetic radiation. Close correlation
were obtained between the data of spectroscopic measurement
techniques processed by various chemometric methods (e.g. principal
component analysis [PCA], cluster analysis [CA]) and operation
condition of milling technology. It is obvious that NIR methods are
able to detect the deviation of the yield parameters and differences of
the sampling times by a wide variety of fractions, respectively. NIR
technology can be used in the sensitive monitoring of milling
technology.
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: Experimental & numeral study of temperature
distribution during milling process, is important in milling quality
and tools life aspects. In the present study the milling cross-section
temperature is determined by using Artificial Neural Networks
(ANN) according to the temperature of certain points of the work
piece and the point specifications and the milling rotational speed of
the blade. In the present work, at first three-dimensional model of the
work piece is provided and then by using the Computational Heat
Transfer (CHT) simulations, temperature in different nods of the
work piece are specified in steady-state conditions. Results obtained
from CHT are used for training and testing the ANN approach. Using
reverse engineering and setting the desired x, y, z and the milling
rotational speed of the blade as input data to the network, the milling
surface temperature determined by neural network is presented as
output data. The desired points temperature for different milling
blade rotational speed are obtained experimentally and by
extrapolation method for the milling surface temperature is obtained
and a comparison is performed among the soft programming ANN,
CHT results and experimental data and it is observed that ANN soft
programming code can be used more efficiently to determine the
temperature in a milling process.
Abstract: Metal matrix composites (MMCs) attract considerable
attention as a result from its ability in providing a high strength, high
modulus, high toughness, high impact properties, improving wear
resistance and providing good corrosion resistance compared to
unreinforced alloy. Aluminium Silicon (Al/Si) alloy MMC has been
widely used in various industrial sectors such as in transportation,
domestic equipment, aerospace, military, construction, etc.
Aluminium silicon alloy is an MMC that had been reinforced with
aluminium nitrate (AlN) particle and become a new generation
material use in automotive and aerospace sector. The AlN is one of
the advance material that have a bright prospect in future since it has
features such as lightweight, high strength, high hardness and
stiffness quality. However, the high degree of ceramic particle
reinforcement and the irregular nature of the particles along the
matrix material that contribute to its low density is the main problem
which leads to difficulties in machining process. This paper examined
the tool wear when milling AlSi/AlN Metal Matrix Composite using
a TiB2 (Titanium diboride) coated carbide cutting tool. The volume
of the AlN reinforced particle was 10% and milling process was
carried out under dry cutting condition. The TiB2 coated carbide
insert parameters used were at the cutting speed of (230, 300 and
370m/min, feed rate of 0.8, Depth of Cut (DoC) at 0.4m). The
Sometech SV-35 video microscope system used to quantify of the
tool wear. The result shown that tool life span increasing with the
cutting speeds at (370m/min, feed rate of 0.8mm/tooth and DoC at
0.4mm) which constituted an optimum condition for longer tool life
lasted until 123.2 mins. Meanwhile, at medium cutting speed which
at 300m/m, feed rate of 0.8mm/tooth and depth of cut at 0.4mm we
found that tool life span lasted until 119.86 mins while at low cutting
speed it lasted in 119.66 mins. High cutting speed will give the best
parameter in cutting AlSi/AlN MMCs material. The result will help
manufacturers in machining process of AlSi/AlN MMCs materials.
Abstract: The objective in this work is to generate and discuss the stability results of fully-immersed end-milling process with parameters; tool mass m=0.0431kg,tool natural frequency ωn = 5700 rads^-1, damping factor ξ=0.002 and workpiece cutting coefficient C=3.5x10^7 Nm^-7/4. Different no of teeth is considered for the end-milling. Both 1-DOF and 2-DOF chatter models of the system are generated on the basis of non-linear force law. Chatter stability analysis is carried out using a modified form (generalized for both 1-DOF and 2-DOF models) of recently developed method called Full-discretization. The full-immersion three tooth end-milling together with higher toothed end-milling processes has secondary Hopf bifurcation lobes (SHBL’s) that exhibit one turning (minimum) point each. Each of such SHBL is demarcated by its minimum point into two portions; (i) the Lower Spindle Speed Portion (LSSP) in which bifurcations occur in the right half portion of the unit circle centred at the origin of the complex plane and (ii) the Higher Spindle Speed Portion (HSSP) in which bifurcations occur in the left half portion of the unit circle. Comments are made regarding why bifurcation lobes should generally get bigger and more visible with increase in spindle speed and why flip bifurcation lobes (FBL’s) could be invisible in the low-speed stability chart but visible in the high-speed stability chart of the fully-immersed three-tooth miller.
Abstract: In this study, the effect of mechanical activation on the synthesis of Fe3Al/Al2O3 nanocomposite has been investigated by using mechanochemical method. For this purpose, Aluminum powder and hematite as precursors, with stoichiometric ratio, have been utilized and other effective parameters in milling process were kept constant. Phase formation analysis, crystallite size measurement and lattice strain were studied by X-ray diffraction (XRD) by using Williamson-Hall method as well as microstructure and morphology were explored by Scanning electron microscopy (SEM). Also, Energy-dispersive X-ray spectroscopy (EDX) analysis was used in order to probe the particle distribution. The results showed that after 30-hour milling, the reaction was started, combustibly done and completed.
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: This paper presents how the real-time chatter
prevention can be realized by feedback of acoustic cutting signal, and
the efficacy of the proposed adaptive spindle speed tuning algorithm is
verified by intensive experimental simulations. A pair of
microphones, perpendicular to each other, is used to acquire the
acoustic cutting signal resulting from milling chatter. A real-time
feedback control loop is constructed for spindle speed compensation
so that the milling process can be ensured to be within the stability
zone of stability lobe diagram. Acoustic Chatter Signal Index (ACSI)
and Spindle Speed Compensation Strategy (SSCS) are proposed to
quantify the acoustic signal and actively tune the spindle speed
respectively. By converting the acoustic feedback signal into ACSI,
an appropriate Spindle Speed Compensation Rate (SSCR) can be
determined by SSCS based on real-time chatter level or ACSI.
Accordingly, the compensation command, referred to as Added-On
Voltage (AOV), is applied to increase/decrease the spindle motor
speed. By inspection on the precision and quality of the workpiece
surface after milling, the efficacy of the real-time chatter prevention
strategy via acoustic signal feedback is further assured.
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: Increasing the demand for effectively use of the
production facility requires the tools for sharing the manufacturing
facility through remote operation of the machining process. This
research introduces the methodology of machining technology for
direct remote operation of networked milling machine. The
integrated tools with virtual simulation, remote desktop protocol and
Setup Free Attachment for remote operation of milling process are
proposed. Accessing and monitoring of machining operation is
performed by remote desktop interface and 3D virtual simulations.
Capability of remote operation is supported by an auto setup
attachment with a reconfigurable pin type setup free technology
installed on the table of CNC milling machine to perform unattended
machining process. The system is designed using a computer server
and connected to a PC based controlled CNC machine for real time
monitoring. A client will access the server through internet
communication and virtually simulate the machine activity. The
result has been presented that combination between real time virtual
simulation and remote desktop tool is enabling to operate all machine
tool functions and as well as workpiece setup..
Abstract: Nanostructured materials have attracted many
researchers due to their outstanding mechanical and physical
properties. For example, carbon nanotubes (CNTs) or carbon
nanofibres (CNFs) are considered to be attractive reinforcement
materials for light weight and high strength metal matrix composites.
These composites are being projected for use in structural
applications for their high specific strength as well as functional
materials for their exciting thermal and electrical characteristics. The
critical issues of CNT-reinforced MMCs include processing
techniques, nanotube dispersion, interface, strengthening mechanisms
and mechanical properties. One of the major obstacles to the effective
use of carbon nanotubes as reinforcements in metal matrix
composites is their agglomeration and poor distribution/dispersion
within the metallic matrix. In order to tap into the advantages of the
properties of CNTs (or CNFs) in composites, the high dispersion of
CNTs (or CNFs) and strong interfacial bonding are the key issues
which are still challenging. Processing techniques used for synthesis
of the composites have been studied with an objective to achieve
homogeneous distribution of carbon nanotubes in the matrix.
Modified mechanical alloying (ball milling) techniques have emerged
as promising routes for the fabrication of carbon nanotube (CNT)
reinforced metal matrix composites. In order to obtain a
homogeneous product, good control of the milling process, in
particular control of the ball movement, is essential. The control of
the ball motion during the milling leads to a reduction in grinding
energy and a more homogeneous product. Also, the critical inner
diameter of the milling container at a particular rotational speed can
be calculated. In the present work, we use conventional and modified
mechanical alloying to generate a homogenous distribution of 2 wt.
% CNT within Al powders. 99% purity Aluminium powder (Acros,
200mesh) was used along with two different types of multiwall
carbon nanotube (MWCNTs) having different aspect ratios to
produce Al-CNT composites. The composite powders were processed
into bulk material by compaction, and sintering using a cylindrical
compaction and tube furnace. Field Emission Scanning electron
microscopy (FESEM), X-Ray diffraction (XRD), Raman
spectroscopy and Vickers macro hardness tester were used to
evaluate CNT dispersion, powder morphology, CNT damage, phase
analysis, mechanical properties and crystal size determination.
Despite the success of ball milling in dispersing CNTs in Al powder,
it is often accompanied with considerable strain hardening of the Al
powder, which may have implications on the final properties of the
composite. The results show that particle size and morphology vary
with milling time. Also, by using the mixing process and sonication
before mechanical alloying and modified ball mill, dispersion of the
CNTs in Al matrix improves.