IMPLEMENTATION OF INTELLIGENT SYSTEM ON AUTOMATED TOOL CHANGER
(ATC) BASED ON ANALYSIS
CUTTING STYLE AND EATING STLE
Abstract
Application of automation systems in a production process has been successful in increasing
product quality, costs and enhance security in the implementation process. In the field of
machining processes, machine tool development capabilities continue to be made, among
others, by applying intelligent systems that can actively correct the course of the cutting
process. This paper discusses the automation process of changing the cutting style and eating
style. Style actual pieces and eating style was measured simultaneously with the cutting
process and the results compared with a reference data stored in a database system. Specific
values shown can be used to identify changes in process parameters including the occurrence
of wear and tear that occurs chisel. The identification data is used as a command execution
for the mechanical system ATC. The results showed that the accuracy of the identification
process is determined by the value of R2 (coefficient determinant) which means the system
needs cutting force measurements and precise style of eating.
Keywords: ATC, cutting style, eating style, value of correlation, intelligent systems
Introduction
The invention of CNC machine is a major breakthrough in manufacturing process technology both in
terms of quality production and cost of production. Development capabilities CNC machines
continue to be made primarily for reasons of ease of operation and effectiveness of the
implementation of the machining process. One method is the application of intelligent systems
developed in the CNC machine so that the machine has the ability to independently diagnose the
entire process worked to obtain the optimum conditions based on the specified criteria. These
studies include: research to estimate the dynamic tool life on a CNC lathing process performed by
Tangjitsitcharoen (Tangjitsitcharoen, 2005). The system developed is based on tool wear monitoring.
Price is always updated and incorporated into the tool life equation that will be used in the
optimization procedure in order to obtain optimum cutting price referring to the criteria for
maximum production speed and production costs to a minimum.
Jerard et al (2008) developed the intelligent machining system (smart machining -SMS system) is
described as a system that can estimate the model coefficients tool wear tangential cutting forces.
Model coefficients were estimated by measuring spindle motor power (Jerard et al, 2008).
Fang presented the analytical predictions and experimental validation using slip line models for the
machining process with cutting chisel contact restrictions. The main finding of the study is: the use
of regional model of slip line is the extreme friction conditions and the determination of common
rules variations in conditions of friction between fury and chisel (Fang and Jawahir, 2002).
Identification of the chatter is a challenge in automated machining process. In addition to cause a
decline in the quality of surface, chatter also lower tool life. Khalifa et al (2006) uses the processing
of the image data to identify vibration chatter in the process of turning. Chatter is characterized by
the formation of the relationship between surface roughnesses with the vibration level. Image data
without chatter and chatter due to the process of turning analyzed. Some qualification parameter is
used to distinguish between the conditions without chatter chatter with the condition, and then the
average level computed gray area. Intensity histogram reconstructed and then put in a variant, the
average roughness parameters to calculate the optimal (Khalifa et al, 2006). Dijk et al (2008)
presented a real time method in the detection and control of chatter at high speed milling machine
tools. They concluded that the working conditions were stable (free of chatter) are usually chosen by
adjusting the spindle speed and depth of cut (Dijk et al, 2008). Development of machine operating
system also has done a lot, among others, for the application of machine tools concept
mechatronics. Operating system built with optimization approach, adaptation and diagnosis
independently (Zoriktuev, 2008).
This study aimed to develop an operating system with the intelligent lathe cutting forces and utilize
data feeds. The data is used as a feedback signal to optimize the speed setting eating, cutting speed
and turn chisel based on the desired characteristics of the machining process. Specifically this paper
will discuss a method of replacing a chisel automatically based on the identification of the results of
the analysis tool wear style cut and style of eating. Intelligent system that is built to be integrated
into the operating system CNC lathe that has been developed previously.
RESEARCH METHODOLOGY
Measurement-Based Intelligent Systems Style Cut and Style Eating
In the process of cutting, cutting forces and eating styles can be used as a reference to determine
the specific conditions that occur. Associated with the work piece material, the geometry of the
chisel and enabling conditions, cutting force and style of eating can indicate specific values for
various parameter changes can therefore be used as a basis for decision-making process. Like the
case of tool wear (flank mapun crater) in the process of cutting, cutting forces and dining styles will
change as shown in Figure 1
In its application, cutting force measurement results and the actual funeral is stored in a memory
registers. Referring to the sampling time specified, the stored data used to compile the equation of a
line cut style and the style of eating. The second slope of the correlation value can be calculated.
Furthermore, the actual correlation value compared with the reference correlation value data in the
data base system. Correlation value approaching interpreted as representing the value of the
parameter changes that occur. For each value of the correlation, paired with a number of command
execution is stored in a data base system. The workings of intelligent systems can be shown in the
form of a flow diagram Figure 2.
Chisel is illustrated in Figure 3. In current command still turn chisel based on the input command
format G / M code. ATC system mechanism The proposed described as follows after data showed
the results of the analysis process which shows the tool wear occurs, then the actual usage data
chisel combined with data from process analysis is used to determine the position between the
actual relative chisel with chisel replacement. Position data will be used as reference to the motor
commands ATC to put the chisel replacement the cutting position. On stage the same data removal
chisel positions as done which means the position of the chisel the same cannot be used returned as
an alternative to replacement chisel.
The application of intelligent systems
The device replacement process automation process measuring cutting forces
and eating style work simultaneously the motion control system and positioning. Considering the
conditions this, the determination of sample data and calculations the time to process the analysis
becomes very important that intelligent systems work optimally for each parameter changes that
are very dynamic. In general the force measuring system consists of a device load cell, signal
converter system (signal conditioning device that consists of a Wheatstone bridge (changing R into
V), amplifier and ADC and interface software to display the desired output format and calculate price
based on the data outputs priced calibration measurements. The device measuring force as shown
schematically in Figure 4.
The main device style of cutting forces measuring system and style of eating in the form of a device
that used a transducer strain gage load cell. Load cell is basically a mechanical device whose
dimensions are determined by calculating the amount of strain. Load cell mechanical construction
shown in Figure 5.
Load cell design procedure is described as follows:
Material: duralumin with the modulus of elasticity (E) = 75 GPa, maximum strain of 400 mikrostrain
selected (depending on the type of election strain gage and price to determine the price sensitivity
and maximum load cell), so that the load voltage for:
= 400.106.75.109
= 300 105
= 3 kg / mm2
When the load cell area is assumed to be square (y), then the loading voltage can be used to
calculate y, namely:
By assuming the cutting force of 10 kg with a fulcrum distance to the point of load on the cantilever
beam construction is 46 mm, then the edge of the square load cell obtained by:
y3 = 115
y = 4,7mm long side of the cross-section is load cell.
In the installation, the ATC system mounted on load cell systems which are located on cross slide
construction lathe... Therefore the burden borne by the load cell is not just a style of cut and style of
eating due to the process of cutting, but also in the form of static load is the mass of the mechanical
system and the ATC other styles that may arise as impact loads. Based on the analysis of the load
force sectional dimension load cell then enlarged to 10 x 10 mm. ATC system construction and load
cell mounted on the machine trainer lathe as shown in
Figure 6.
Measurement Style Cut and Style Eat As Reference Data
Execution process which is outcome of intelligent systems based on the comparison between the
actual data results measurement with reference data stored in the system database. Similarity value
is the address of the execution procedure which will be used to stabilize cutting process. To produce
execution process specific to each parameter changes that occur, the value the comparison should
also be worth specific. By using the data system logger, cutting force measurement results and style
eating can be displayed in form equation of a line and each the line equation is calculated slope.
Correlation value is obtained by the second compares the gradient equation the lines that are
proven to used to identify lathing process parameter changes. Correlation value measurement
results incorporated into the database system will be used as a comparison value (reference value).
These values obtained from the measurement process cutting with a variety of parameters depth of
cut (a), the condition of the chisel, feeding speed (Vf) and speed piece (V). Test material used in the
form of Al 2024 alloy (duralumin) with such as inserting a chisel cutting tool tip carbide with
geometry.
ANALYSIS AND DISCUSSION
Analysis of Measurement Data Cutting force measurement results and style eat line equation for
each variation of parameters as shown in Table 1. Rates R2 (coefficient determinant) shows
the condition of the distribution points of measurement results as a function of time, while the price
Correlation shows the price comparison the gradient of the cutting forces gradient style dining.
Table 1 data line cutting force equation and eating style measurement results and processing
process
Based on the data in Table 1 it can be concluded, if the actual correlation value close to the price of
about 2.4 to 3.3, then chisel indicated experiencing wear and tear. However, in the range of 3.0 to
3.3, the correlation value can also be indicated chisel under normal conditions. For that additional
data is required so that the process of interpretation to be accurate. Additional data can be the price
of eating style gradient between normal cutting conditions that range in price and style eating 2200
on condition chisel wear (flank or crater) valued at over 3200. In practice, price range correlations
apply very specific depending on work piece material, the type and geometry of the chisel and force
measuring system is used.
CONCLUSION
A method that aims to identify changes lathing process parameters have been developed by using
the correlation between cutting styles and eating style. From the analysis of value, specifically the
correlation value can indicate changes in the cutting process parameters, but the accuracy is
determined by the price of R2 interpretation of the equation line cutting forces or force feeding. It
required a cutting force measuring device and eating style precision.
BIBLIOGRAPHY
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