08-11-2012, 03:21 PM
A Review of Optimization Techniques, Effect of Process Parameter with Reference to Vibration in End Milling Processes
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Abstract
In today’s rapidly changing scenario in manufacturing industries, machining is an
operation which produces variety of shapes and surfaces. The application of optimization
techniques in metal cutting process is essential for a manufacturing to increase the quality
of a product. Condition Based Maintenance (CBM) is a key factor to achieve the optimal
machining conditions. An effective predictive maintenance program must include both
condition-driven and time-driven tasks. End milling is one of the operations which produce
several shapes with varying surface roughness value. During cutting, vibration is
unavoidable and has great impact on the machined surface and tool. However, with the
increase of the demand on the high quality of surface finish and long tool life, closer study
is needed to optimize the cutting parameters. This paper reviews existing studies and affect
of vibration during the cutting process in end milling.
Introduction
Predictive maintenance is a strategy used to reduce breakdown, vibration etc. It requires continuous
monitoring of equipment to detect and diagnose defects, when a defect is detected; the maintenance
work is planned and executed. Due to the important role played by vibration in machinery, tools, many
researchers worked in the direction and brought out certain information and knowledge.
Milling is one of the operations which produce several shapes with varying surface roughness
value. In milling, a predetermined amount of material is removed from the work piece at a relatively
slow rate of movement or feed by a milling cutter rotating at a comparatively high speed. Basically, milling cutter is divided in to: Peripheral milling, Face milling and End milling. The End milling
process is widely used in industry because of versatility and effectiveness. The end mill has edges in
the side surface and the bottom surface. During the operation in end milling, vibration is considered to
be one of the most important while machining. Three different types of mechanical vibrations such as
free vibrations, forced vibrations and self-excited vibrations that arise due to the lack of dynamic
stiffness, stability of the machine parts, vibrations generated under unsuitable cutting conditions creates
serious problem as it causes excessive tool wear, noise, tool breakage, and deterioration of the surface
quality.
Vibration in End Milling Operation
End milling is one of the most widely used in cutting processes in the automotive, aerospace, die/mold,
and machine parts industries. Vibrations arise during cutting process, causes extremely serious
problems like excessive tool wear, noise, tool breakage, and deterioration of the surface quality etc.
Impact of Process Parameter on Surface Roughness
Surface quality is greatly concerned in manufacturing industry; many attentions have been paid to the
effects of cutting vibration on surface finish. In the past efforts, the most researches in this area are
based on these two approaches. One uses virtual vibration signals to analyze the effects of cutting
vibrations on the machined surface, another uses predicted vibrations which are determined by the
cutting process dynamics. Even in the occurrence of vibrations of the machine tool, defects in the
structure of the work material, wear of tool, or irregularities of chip formation contribute to the surface
damage in practice during machining.
Impact of Vibration on Tool Life and Wear in End Milling Operation
During end milling, cutting tools are subjected to friction between cutting tool and work piece
materials results in progressive wear. Thus tool wear becomes an important parameter in the metal
cutting process; creates poor surface finish, increases in cutting force, increases in vibration of the
machine tool, increases in tool-work piece temperature during machining, decreases in dimension
accuracy. Choosing suitable machining methods, tooling systems, cutting conditions, cutter geometry,
tool and work piece material, chip formation is important to achieve the goals to maximize tool life
Statistical Regression Technique
Regression analysis has become one of the most widely used statistical tools for analyzing multifactor
data. It is a conceptually simple method for investigating functional relationship among variables. The
standard approach in regression analysis is to take data, fit a model, and then evaluate the fit using
statistics. Regression analysis has numerous areas of applications. There are limited research articles
available due to the effect of vibration in end milling. [45]. A multiple regression model in detecting
the tool breakage based on the resultant cutting forces in end milling operations. The feed rate and
depth of cut are particularly influenced by the force in the regression model [13]. A multiple regression
is developed to predict surface roughness by using spindle speed, cutting feed rate and depth of cut
[46].
Artificial Neural Network (ANN)-Based Modeling
A neural net is an artificial representation of the human brain that tries to simulate its learning process.
An artificial neural network (ANN) is often called a "Neural Network". Artificial Neural network is a
network of simple processing elements (neurons) which can exhibit complex global behavior,
determined by the connections between the processing elements and element parameters. Artificial
neural network is an adaptive system that changes its structure based on external or internal
information that flows through the network. An ANN is a data processing system, consisting large
number of simple highly interconnected processing elements as artificial neuron in a network structure.
Neural networks provide algorithms for learning, classification, and optimization. The use of neural
networks in control applications including process control, robotics, industrial manufacturing and
aerospace applications, among others-has recently experienced rapid growth. The basic objective of
control is to provide the appropriate input signal to a given physical process to yield its desired
response.