P1121052399

I N T E L L I G E N T
M A N U F A C T U R I N G
S Y S T E M S
University of Maribor -
SLOVENIA
INTELLIGENT AUTOMATIC CUTTING
TOOL
SELECTIONS FOR TURNING
OPERATIONS
USING NEURAL NETWORKS
J. BALIČ, F. Čuš & B. Vaupotič
University of Maribor, Faculty of Mechanical Engineering
Smetanova 17, SI-2000, Maribor, Slovenia
joze.balic@uni-mb.si

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Main aim of our work is
Monitoring
Modelling
Optimization
Control
Programming
Scheduling
Part production
AMPLIFIERAMPLIFIER
Manufacturing
to serve the MANUFATURING
%100(C, 18.10.102)
N10 G00 G90 G54 X0 Y200 B0

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The research field of this developing is using
of Artificial Intelligence in:
Manufacturing technology,
CAD/CAM systems,
CNC programming,
Intelligent devices and systems.
Main aim of our work is

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 Development of an intelligent system for
selecting the best set of cutting tools on
the
basis of a 3D CAD model.
 Optimization of cutting conditions
 Intelligent CNC programming of
manufacturing (turning)
Main aim of this presentation is:
Introduction

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Introduction
3D CAD model
Selection and optimization of cutting tools
CNC programming
Manufacturing - turning
Finished part

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Some facts:
I. Industry producers are subject of
concurrent pressures (global market)
II. Orientation to customer oriented
products
III. Functional characteristics of the
products increased
IV. Variety of the products increased
V. Quality and reliability of the products
increased
VI. Competitive production with optimal
production cost is needed.

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Such pressure leads to new challenges:
 Production on demand
 Smaller order quantity
 Just-in-time delivery
 Reduction of production time (through-put
time)
 Reduction of production costs

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The answer is:
Comprehensive,
Intelligent,
On-line monitoring,
Optimization,
Programming and
Control system
for
Intelligent Flexible Manufacturing Systems
(IFMS).

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Structure of the system
M a n u a l C A - b a s e d A u to m a t ic In t e llig e n t
X i
Y i
Z i
X
Y
Z
X
Y
Z
X
Y
Z
IN P U T /O U T P U T - p r e p r o c e s s in g /p o s tp ro c e s s in g
CENTRALDATABASE
M O N ITO R C O L L E C T O P TIM A L C N C D N C
5
10
15
20
25
0 0 , 0 5 0 , 1 0 , 1 5 0 , 2 0 , 25 0 , 3 0, 3 5 0 , 4
Tim e [s ]
Feedrate[mm/s]
M o d e l
Ex p e rim e nt

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Part of the system is complex intelligent
system
for optimization of machining (turning) using
neural networks and genetic algorithms.

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FEATURE
RECO G NITIO N
In pu t:
3-D C AD
mo d el
O utp ut:
Man ufac turing
fea tures
In pu t:
INTELLIGENTSEARCHER
Output:
-Work opera tion
-Se t of
cutting tools
Input
C AD
system s
1
2
N
INTELLIGENTCNC PROG RAMMING
CNC progra me
Input
Output:
Input information is CAD
model of the component
The task is completely
automatic, intelligent
optimum cutting tool
selection and optimization
of tool path-CNC programme
without any interference
of the human into the system.

Generation of optimal cutting tool set on the basis
of:
•3D CAD model and
•important selection parameters
Generation of tool path for turning (including):
•capability of learning in order to work
intelligently:
•sufficient memory capacities,
•concluding capacity (processor capacities),
•detecting capacities (input and output)
Pre-conditions & characteristics

Further, training requires some initial knowledge
which is inherent in living systems.
By training, the system’s capacity is enhanced
and its intelligence increased.
Pre-conditions & characteristics

Detailed structure I N T E L L I G E N T
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The system works in several stages:
the preparation of the 3D CAD model,
the stock preparation,
the workpiece for rough-turning and
the workpiece for finish-turning representing
and the final products.
The recognition of features for roughing and
finishing is also part of the system.
How it works ?

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How it works ?

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A data-base was created for the purpose of
neural network training.
It consist of four units, ach unit contains the
relationships between:
geometrical features,
workpiece material,
quality of the required surface,
other selection parameters and
cutting tools (approach limitations, exit angle
on the workpiece).
Data base ?

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Cutting tools limitations
β1
Tool entry
Intermediate
stage
α1
Tool exit
(3)
(2)
(1)
β1 β1maxβ1min ≤ ≤α1 α1maxα1min ≤ ≤
(3) (2) (1)
<_ <_<_<_
Workpiece contour

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Cutting tools limitations
α
β
α
β
1max
1max
1min
1min

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After training and testing, four neural networks
are ready to be applied to the real environment.
The workpiece (3D CAD model) should be
suitable for the external turning operation.
Example

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From 3D CAD model the individual geometrical
features is extracted.
The geometrical features are located in two-
dimensional cross-sections of 3D CAD model.
The outline of the half cross-section of the
product for finish-machining is the tool path for
finish machining, while the outline of the semi-
finished product is the path for rough-
machining
Example

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The tool-geometry determines which part of the
workpiece can be worked with that tool and what
cannot be manufactured.
Consequently, only a certain type of cutting-tools
can be used for the manufacture of a particularly
shape.
The recognized geometrical features and required
surface quality are automatically normalized and
transformed into suitable record for neural
networks.
On the basis of these data the NN select suitable
cutting-tool sets for rough and finish-turning, and
determine the optimum sequence.
Example

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The outputs of the first
neural-network are the
possible tools for rough-
turning and the outputs
from the second-neural
network are the cutting
tools for finish-turning
Example

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These results are
entered into the third and
fourth neural networks in
order to find out optimal
tool set.
Example

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Optimal tool set was input in GA based intelligent
CNC programming system. The task of this system
is to generate CNC programme for turning
operation.
Example
z
Blank
contoury
Part contour
Several chips
Ref. Point
z
y
Tool
Several cuts 1-4
Cut1
Cut2
Cut3
Cut4

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The result (CNC programme) was compared with
the selection of the expert who indicated two
identical sets of optimum tools.
It can be concluded that the system is capable of
classifying intelligently the most adequate cutting-
tool set similarly as an expert with 20 years of
experience
Example

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Example – video clip

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Advantages:
Very high-level of classification,
Constant enhancing of the knowledge pumped out
of the growing data base,
Each newly-solved example is stored in the base
so that the networks permanently learn and gain
knowledge and experience similar to the human
expert.
Other selection parameters can be also included in
the network training.
Conclusion

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Advantages:
Conventional programming has been replaced with
the use of neural networks.
System functions are flexibly and fully
autonomous.
Disadvantage:
Time-consuming creation of the cutting tool data
base with geometrical features.
Discrete cutting space
Requirement for good familiarization with the
cutting-theory in order to built adequate data base.
Conclusion

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Future work:
Non-discrete cutting space
More different cutting tools
Total integration of cutting parameters optimization
Conclusion

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Thank you !

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Example #2 CNC milling
Blank Product

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Nesting of parts
Example #3 CNC cutting
GA based cutting

Example #4 AGV movement and
optimization
No. of machine
tools
No. of
solutions
3 3! = 6
4 4! = 24
5 5! = 120
6 6! = 720
15 15! =
1,31⋅1012
… …
50 50!
=3,04⋅1064

Example #5 AGV movement in shop floor
S o u th
N o r th
W e s t E a s t
E
S ta rt p o in t
S
E n d p o in t
O b s t a c le s -
m a c h in e t o o ls
S
E
S t a r tin g p o in t-
r o u g h p a r t s to r a g e
P a r ts in s to r a g e -
m is s io n c o m p le te d
E n d p o in t- s to r a g e

Example #5 AGV movement in shop floor
23-rd generation O.K.
AGV cooperation
Complex environment

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Explanation - additional
4 NN - details

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NN stages
Back
Set
1g
Set
2g
Set
ng
- 1g
- 2g
- 3g
- 4g
- ...
Tool
Tool
Orodje
Orodje
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
Set1f
Set2f
Setnf
- Orodje 1f
- Orodje 2f
- Orodje 3f
- Orodje 4f
- ...
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
-
-
-
-
-
-
-
- ...
Direction (1)
Angle (1)
Shape (1)
Material
Ra
Others
Oblika (2)
- Smer (3)
- Kot (3)
- Oblika (3)
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
- Smer (1)
- Kot (1)
- Oblika (1)
- Oblika (2)
- Smer (3)
- Kot (3)
- Oblika (3)
- Material
- Ra
- Drugi parametri
- ...
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Rough turning
Input Processing Output
x
y
y
x
x
x y
x
y
y
x
x
x y
Finish turning

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Back propagation NN
Learning algorithm: error back propagation
generalized delta rule

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Back propagation NN
FORWARD

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Back propagation NN
BACKWARD

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Back propagation NN
WEIGHT SETTINGS

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Explanation
Learning results Mean squared error MSE
MSE za tretjo NMMSE za tretjo NM
MSE za prvo NMMSE za prvo NM MSE za drugo NMMSE za drugo NM
MSE za četrto NMMSE za četrto NM

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Explanation – GA
Back

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1 1 000
1 0 111
1 1 10
1 0 110
1
1 1 000
1 0 111
1 0 110
1 0 110
1 1 001
1 1 001
C R O S S O V E R
1 1 101
1 0 101
1 0 101
M U T A T I O N
E V A L U A T I O N
S E L E C T I O N
S O L U T I O N S
F i t n e e s
c a l c u l a t i o n
S O L U T I O N S
1 8
2 9
2 72 1
D E C O D I N G
C O D I N G
Basic GA selection algorithm

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Basic GA algorithm
procedure GA-lathe
begin
data input on blank and product, selection of machining
type, discretization of machining area
defining of starting, ending, and reference point of the tool
begin
t ← 0
if known_NC_program
then input P(t)
else initialize P(t)
evaluate P(t)
while (not termination_condition) do
begin
t → t + 1
alter P(t) by applying genetic operators
evaluate P(t)
end
end
end

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Explanation – GA
GA – FITNEES (process termination)
No. of generations
Best
fitnees
Croswise fitnees

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Genetic algorithms based CNC programming
Turning simulation
Totally automatic tool
path generation
Intelligent way to find
out optimal tool path
No intervention of
NC programmer

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Basic characteristics of the system
I. A GA was applied to the simulation model to determine
the process parameter values that would result the
optimal cutting parameters.
II. GA are search algorithms for simulation and
optimization, based on the mechanics of natural
selection and genetics.
III. The power of these algorithms is derived from a very
simple heuristic assumption that the best solution will
be found in the regions of solution space containing
high proposition of good solution, and that these
regions can be identified by robust sampling of the
solution space.

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Basic characteristics of the system
IV. The simplicity of operation and computational efficiency
are the two main attractions of the GA approach.
V. The computations are carried out in three stages to get a
result in one generation or iteration.

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On-line monitoring and data collection system
User interface unit
Signal processing
Data analysis
Machining data
acquisition
Expert
system
Decision making
Error
detection
Monitoring of
cutting process

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On-line monitoring and data collection system
DATADATA
AQUISITIONAQUISITION
WORKPIECEWORKPIECE
CUTTINGCUTTING
TOOLTOOL
0,000 0,005 0,010 0,015 0,020 0,025 0,030 0,035
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
140
160
180
200
220
Rezalnasila[N]
Čas t [s]
Material obdelovanca: Ck45 Širina in globina frezanja: RD
, AD
=0,4 mm Fx
:
Frezalo: R216.44-10030-040-AL10G Podajanje: fz
=0,10 mm/zob Fy
:
Premer frezala: D=10 mm Rezalna hitrost: Vc
=188,5 m/min Fz
:
DATA ANALYSISDATA ANALYSIS
MONITORING OFMONITORING OF
MACHINIG PROCESSMACHINIG PROCESS
HIGH SPEEDHIGH SPEED
MACHININGMACHINING
ROBOTROBOT
HSCHSC
SPINDLESPINDLE

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Intelligent optimization system – model

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247.60
100
500
30
10
0.56
0.1
0.2
0.2
0.6
0.6
0.8
125
0.12
250
0.4
0.4
0.1
188.5
247.60
0.4
0.4
0.11
199.5
247.61
Intelligent optimization system - results

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Genetic algorithms based CNC programming
The goal: from CAD solid model to finished part

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Conclusions
The basic characteristics of the system are:
I. General modules architecture,
II. Technological oriented system,
III. Automatic, intelligent generation of control
information for FMS,
IV. Integration of several sub-systems,
V. Implementation of artificial intelligence in the
system,

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Conclusions
The basic characteristics of the system are:
V. Only machine tools for cutting are included,
VI. Use of existing program modules and systems,
VII. Testing of the system in real production
environment,
VIII. Testing of the system with simulation describing
the real manufacturing system
IX. Devices and procedure could be aided to the
system.

P1121052399

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