Download cutting values prediction with neural networks

CUTTING VALUES PREDICTION WITH NEURAL NETWORKS
A. Nestler, G. Schulz
Dresden University of Technology, Institute of Production Engineering, Mommsenstr. 13, D-01062 Dresden,
Germany
Abstract
Flexibility and intelligence are indispensable factors in automation and manufacturing. For such demands,
intelligent information systems are required, especially considering the technological data type. The process setting
parameters have to be determined in the best suitable way. A central point of technological data are the cutting values
feed and speed, especially because of their influence on cost, time, quality und security of the manufacturing process.
The rapid development of new advanced materials, tools etc. cause the lack of qualified, actual machining values.
A creative approach to make available technological data are artifical neural networks (ANN). Neural networks
offer a possibility to solve these problems by training networks using known cutting values and the generalizing ability
in a later utilization phase. Considering different data sources for cutting processes are possible. The authors
demonstrate results of a project based on neural network and technological database system.
Keywords: Milling Modeling, Cutting Values, Technological Database, Neural Network
1
Introduction
The planning and manufacturing processes need
an easy access to reliable technological information.
The usually existing conventional methods for
technological databases are insufficient. Selection
and prediction of appropriate machining process
parameters is critical. Tables from tool
manufacturer or machining handbooks give only a
wide range of cutting values for special cutting
conditions. Real good cutting values for specific
cutting operations are usually gained through
experience in long periods. It is still very common
that personal experience is used to determine cutting
values because the complexity of machining
conditions cannot fully be described with
calculation methods.
The handling of the given planning data before
manufacturing, the estimation of data during phases
of testing and the retrieval of realized cutting values
in connection with a database near the process are
not practically solved. General characterisitcs for
technological data are shown in the figure 1.
Incomplete, uncertain, heterogeneous information
Flexible solution fields, environments, constraints
Improved part and cutting materials
Distributed and/or cooperative problem solving
Alternative possibilities, strategies
Global informations for local users
Intelligent usage of resources
Machine tool - clamp workpiece material cutting material - adapter tool - manufacturing operation manufacturing condition
Complexity of
machining conditions
Cutting values
(feed, speed, ...)
Figure 1. General aspects to handle technological data
Technological information play an important role
in
computer
integrated
manufacturing.
Computerized
techniques
influenced
the
development stages of technological data
dissemination drastically - from simple ‘data
collection’, over the level ‘integration of
applications’to the ‘global communication’(Fig. 2)
Cutting values dissemination
Best value collection
Tool management
Electronic catalog
Database
Internet
Applicable methods for the main functionality of
databases has to be extended. Different methods for
data handling are available for this purpose, esp. for
the treatment of numerical data (Tab. 1).
3
Determination of cutting values
Table
Handbook
Figure 2. Levels of cutting data dissemination
2
The determination of cutting values includes
different methods parallel (Fig. 3).
Technological database
The planning and manufacturing processes need
an easy access to reliable technological information.
Such a database is an decision supporting system for
planning and manufacturing task. Relevant data
ranges for technological data are:
• Tools, measuring - and testing instruments
• Clamping devices, fixtures, chucks, stock
• Workpiece and cutting materials, cutting values
• Machine tools, attachments
• Manufacturing features, operations and cycles.
Almost of all these data ranges are the research
and development dominance of tool-, resource
management- or machining recommendation
systems.
A new technological database quality can be
gained if there can be considered various data
sources by means of different data processing
methods alternatively.
In practice can be found relevant data sources of
the cutting data area:
• Firm-independent recommended values
• Firm-dependent best values
• Personal experience and intuition
• Results of calculation models.
The crucial point of such database are the cutting
data. The cutting values depend on a lot of
influencing parameters and require complex
connections. Therefore different possibilities exist
for the availability of cutting values.
Table 1. Applicable methods for extended data management
Method for data handling Example
empirical model
constraints, optimization
statistical data analysis
detection of statistic
dependencies
explorative data analysis
detection of internal data
structure
fuzzy analysis
handling of unsharp, uncertain
information
artifical neural networks
knowledge acquisition, learning
data, generalising
decision trees
stuctureing knowledge
genetic algorithmus
optimization
Information
System
Handbook
Table
MEMO
Intuition
Calculation
Experience
Programm 1 - Dokument 2
Datei Bearbeiten Optionen
Ansicht Ändern
Hilfe
Constraints (%)
Mathematical model Neural network model
Fuzzy logic model
Stand-alone or hybrid intelligent structures
Figure 3. Possibilities for cutting data determination and
calculation methods
Conventional based methods for different
application situations are required: the generation of
results from intuitive actual cutting, personal
experience or calculation results. The calculation
contains mathematical models and knowledge based
methods (neural net, fuzzy logic) in connection with
database.
Applications of mathematical models for
technological data determination give useful start
parameters for using in different technological
software systems [NES-92].
Neural networks are applied for knowledge
acquisition and processing in intelligent machining
[MON-93] and process modelling [KLO-97]. The
computerised model neural network is directed to
automated knowledge processing. At present, neural
networks are especially used to solve sophisticated
problems. Unknown correlations between input and
output parameters can be learned and reproduced by
neural networks.
Another possibility exist with fuzzy logic models
for metal cutting [BAR-96]. The knowledge can
extracted from a catalog or handbook. With the help
of linguistic variables (parameter - very low, low,
medium, high, very high) can described fuzzy
expressions.
4
Neural networks for cutting values
The determination of cutting values with the
help of neural networks is a new alternative
method. Examples, demonstrated for milling
manufacturing, proves that neural networks are
capable of learning nonlinear functions from data
sources [FIC-97].
The procedure for establishing an applicable
network consits of the following main steps:
• description of the problem
• data selection
• design of neural network
• training and test of neural network
• practical performance test
• integration of net in a framework of an
application
The first step includes considerations about the
practical target and the purpose of the later
application. In preparation the available data sources
will be analysed.
5
Strategies for data selection
A good quality of the net output depends to a
high degree on a carefull selection and
preprocessing of training data [SCH-98]. The
selection of relevant data out of all available data
sources and the combination of different data is a
task that cannot be done automatically. Intelligent
software can only support the manual work of an
experienced person (Fig 4). Cutting parameters,
different data sources do have a different reliability.
Data that was gained through experience is the most
valuable one followed by firm-independent data
collections and standard values of tool and machine
suppliers.
internal data
external data
selection, evaluation, adaption, ...
data
analysis,
data
mining,
tools,
add-ins
data set
relevant net data
division
training data
test data
Figure 4. General preprocessing steps
In general, the structure, parameters and used
classificators out of different data sources are quite
different. They have to be transformed and made
compatible. The data structure of the most relevant
in-house data sets are in most cases the standard for
the structure. All other interesting external data
sources have to be transformed into this structure.
One major problem are missing parameters.
Special neural networks, self organising maps (Fig.
5), are capable of estimating single missing
parameters and completing data sets. With a special
network structure it is possible to handle data with
different, incomplete input pattern.
Figure 5. Self organising maps (SOM) for input data with gaps
Now, the best and most reliable data sets have to
be selected. For a selection of training data sets,
only data sets which are very close neighbours
within the field of input parameters can be
compared and selected. Data sets of minor
reliability should be only selected when no better
data is available within this distinct area of the
problem space. Therefore, the different data sources
are weighted and the multidimensional space of all
interesting cutting problems is divided into
numerous blocks. Within these blocks, only the
most reliable data sets are selected, the other ones
are ignored (Fig. 6).
pa
ra
m
ete
r3
parameter 2
Machine Tool
training pattern
in competition,
selection according
to reliability data
Workpiece
Material
Cutting
Material
Feed
Speed
Tool
parameter 2
Manufacturing
Parameters
training pattern
without competition,
always selected
Cutting
conditions
Figure 8. Input / output parameter of a net ‘cutting values’
parameter 1
Figure 6. Reference grid
Many other methods and software tools are
available. Their usage strongly depends on current
problem. After determinating the relevant
parameters, the selected data sets can be divided
into the training - and test data sets (two-third and
one-third).
6
Neural network model
6.1
Steps for implementation
After making available the cutting database with
the relevant net data follows the definition of the
network structure and the training of the network
with the training database. The test database is
needed for a detailed test of the quality of a trained
network. The result is a trained network and can
integrated in a technical environment. (Fig.7).
The architecture of the neurons and connections
in the hidden layers are subject of adapting to the
problem. In general, numerous experiments have
shown,
that
a
feed-forward,
multilayer
backprobagation net [ZEL-95] with two hidden
layers of 10 to 30 neurons each is appropriate (Fig.
8). If there exists partial knowledge (empirical)
about the problem, it’s worth it to integrate it. Is is
very useful to integrate calculations of existing
mathematical models into a network [SCH-97].
Because of the high flexibility and adaptability
of neural networks it is possible, to create a special
system for any special purpose. Because of the high
complexity of cutting processes and the high
number of influencing parameters, smaller networks
for special tasks do work more efficient than
networks that try to deal with a large variety of
conditions.
relevant net data
input
neural net
calculation
output
application
Figure 7. General processing steps
Figure 9. Network structure of firm-dependent cutting values
6.2
Neural network design
After classification of the relevant parameters,
the training database for the neural network will be
extracted. For the training of the neural network
approximate or discrete cutting value tables and
results of computations modules can be used.
For example, Fig. 9 shows a net which is trained
with cutting values from a company, that mainly
machines cast iron on milling machines of a similar
type with many different tools. In this case, it is not
useful to train the net with any information about
the machine or the material, because there will not
be any significant change in these parameters.
6.3
Training and test
The training and test show an example ‘Sandvik
catalog’. With a extract of the selected catalog data
sets, the net will be trained. The training is done
when the average fault of the output is lower than
10% while reproducing the training data (Fig. 10).
calculate correct cutting values for different types of
tools, materials, cutting conditions. Fig. 12 shows
the netoutput and expected feed value when varying
the tool diameter (all other parameters remain
constant).
Feed
1
net output
0,20
Real Value
Calculated Value
0,10
0,15
0,05
0
12
15
20
25
30
35
40
45
50
Tool Diameter
Figure 12. Net output for unknown tool diameter
It follows the test phase. For the testing data sets
the network has to calculate a correct output. The
amount of data sets in a 2% output-error-class for
1000 data sets shows (Fig.11).
A method for pediction test is validation of the
output values by practical tests The machining
experiments were carried out with cutting
parameters suggested by a neural net for a tool and a
material which was not subject of the training
database (Experiment 1, Fig. 13). To get a better
feeling about the quality of these parameters,
additional cuts with a modified parameter
environment were done. The setting limits are
results for instance of optimization models
(constraints) or from experince. In the example the
prediction area for feed and speed were altered +/10 % (Experiment 2, 3, 4, 5 Fig. 13)
number of data sets
per error class
Figure 10. Output error of training with successfull progress
700
600
training data sets: 2000
test data sets: 999
vc cutting speed
vf cutting feed
500
400
300
200
100
0
-22
-18
-14
-10
-6
-2
2
6
10
14
18
22
error classes (%)
vc [m/min]
Figure 11. Output error frequency of the trained network
7
Practical performance test
Before implementing a trained neural net into a
technical environment, the performance of the
netoutput and the generalising ability has to be
inspected. It is advantageous, to combine different
test methods. Usually, this is done through a
validation data set. In addition, practical machining
tests were done. The experiment based on cutting
values feed and speed from a manufactureres tool
catalog for milling tools.
A method for testing the generalisation ability is
the variation of single input parameters within a
wide range - before machining. An expert is able to
see if the net is doing the right thing. Varying tool,
material, and process parameters is of special
interest. It can be shown, that neural nets are able to
limit
settings
3
net output
4
1
2
5
f z [mm/U]
Figure 13. Cutting values area for testing saefty of the net
output
First machining experiments were done on the
shop floor for testing the output of a trained
network. It could be proven that cutting values
generated via the generalising ability of a neural net
are precise enough for practical requirements.
Network source:
Sandvik-Catalog
Experimental conditions:
35
30
25
20
15
10
not
acceptable
acceptable
good
5
0
very
good
number of experiments
(40 in total)
milling machine: Deckel FP4A, 4kW
tools: diameter 20, 25, 40 (Fa. Walter)
cutting material: WTL-71
cutting depth, overlapping: 50%, 100 %
workpiece material: AlCuMgPb, 210Cr46, St60, GG25
Figure 14. Experimental results
An example shows the practice: The training
data were taken from a Sandvik catalog. The output
was generated for Walter-tools which differ in few
details. The maching experiments were done with 3
different tools, different cutting depths and 4
different workpiece materials. The opinion of the
machining worker about the quality of the cutting
values is shown in Fig. 14., most of the values are
good.
8
networks. The generation of the input data has to be
as easy as possible with an intelligent support. Most
of the values for the input can be extracted by the
operator from a technological database (Fig. 16).
Application for usage of the neural
networks
For a comfortable handling of the trained
network, a proper user interface is to make
available. Figure 15 shows the integration of trained
networks into the framework of a technological
database system.
application
technological database
- ...
network specific
user interface
C- source code
trained network
extended application
extended data management
data management technological database
- CUTTING VALUES
- single value ...
- calculation methods
- optimization
- neural network
technological data
(training-, test data)
RDBS SQL Windows
Figure 15. Extended data management
The user must be able to work with the net
without any detailed knowledge about neural
Figure 16. User interface for ANN in the framework of a
database system
This software and the trained network can be
installed on a PC, Notebook or within the CNC. The
general advantage of a neural network is, that once
it is trained, it does need only a small amount of
memory and computation time. A trained network
can be transformed into a short C subroutine which
means an enormous compression of data, compared
to the training database.
9
Conclusions
Artificial neural networks are able to learn from
examples and, through good generalisation abilities,
to apply the trained knowledge to new situations.
Trained networks can integrated into an existing
database system and do extended its functionality
into the direction of future intelligent database
assistants.
Applications of neural networks can be
integrated into the manufacturing process for the
acquisition and handling of cutting values. Neural
techniques offer new possibilities to store, handle
and retrieve technological informations on shop
floor, for NC-programming and for planning tasks.
10
Acknowledgement
This research was a part of the project " Knowledge based
cutting value determination with the help of neural networks"
funded by the German Association for Research (DFG).
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