The monitoring of the turning tool wear process using by emu13578

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									              The monitoring of the turning tool wear process using
                          an artificial neural network
                                          G. C. Balan, A. Epureanu
           Universitatea Dunarea de Jos din Galati, Str. Domneasca nr. 47, Galati–800 008, Romania




Abstract

     The study of machine tool dynamics is performed here as “monitoring”, which involves the checking and
improving of machine functioning. Signals collected from certain sensors are processed by a computer. These data
then lead to the monitoring decision, which is to associate the current state of operation with one of the classes from a
set of known classes. For monitoring in turning, the classes (tool conditions) are shown.
     The experimental setup, experimental results and data processing are presented. For the monitoring of the tool
wear, an artificial neural network (ANN) is used.

Keywords: monitoring, turning, ANN (= artificial-neural-network)



1. Introduction                                                where x is the set (vector) of monitoring indices x
                                                               = [ x 1 , x 2 , … , x m ] , and t – the admissible limit
      In turning, tool flank wear is one of the major          values.
factors contributing to the geometric error and thermal             The samples can be registered as in table 1, in
damage in a machined work-piece. Tool wear not only            which m is the number of monitoring indices, n is the
directly reduces the part geometry accuracy but also           number of classes, and N - the number of samples. So
increases the cutting forces drastically. By changing the      x k =[x (k,1), x(k,2), . . . , x (k,m)]. represents “the
worn tool before or just at the time it fails, the loss        vector k”, and c ( x k ) ∈ [c1, c 2,..., c n ] indicates the
caused by defect product can be reduced greatly and            fact that in this recording the result was one of the
thus product quality and reliability is improved.              classes: c1 , c 2 ,... , c n .
      To accomplish these objectives, artificial                    The function can be introduced Q:c→ x,             (2)
intelligence methods are the most modern means.                which is “obscure“ because on it you can do only
      The study of machine-tool dynamics is realised           indirect measurements which are, or which can be
here as “monitoring”, which means the checking and             bound to the function. If for Q no theoretical relation
improving of machine functioning. Signals collected            can be obtained, a two phases data interpretation
from certain sensors are processed by a computer.              method involving learning and classification can be
These data then lead to the monitoring decision, which         used. For a set of samples in which both x and c are
is to associate the current state of operation with one of     known (a part of data from the table 1), in the learning
the classes from a set of known classes (process               phase an empirical relation between x and c is formed.
conditions) c = [ c 1 , c 2 , …. , c n ], according to:        In the classification phase the other part of the table 1
      if t i n f < x ≤ t s u p , then c = c i ,        (1)     is used to predicting of c, thus testing and adjusting
Table 1
The samples
    Sam -                             Monitoring indices                                           CLASSES
     ples         x1          x2         .....      xi        ......         xm             ( Process conditions )
      x1       x( 1,1 )    x( 1,2 )    ......... x( 1,i )    .........    x( 1,m )       c ( x 1 ) ∈ [ c 1, c 2,....,c n ]
      x2       x( 2,1 )    x( 2,2 )    ......... x( 2,i )    .........    x( 2,m )       c ( x 2 ) ∈ [ c 1, c 2,....,c n ]
       ...        ...         ...         ...       ...         ...          ...                      .....
      xN       x( N,1)     x( N,2)     ......... x( N,i )     .......     x( N,m )       c ( x N ) ∈ [ c 1, c 2,....,c n ]

the empirical relation.                                              back edge in B zone is V B B = 0.3 mm if this has a
     Thus, function Q is reversed: Q -1:x→ c .         (3)           regular form ; - the maximum breadth of the same
     Now the empirical relation is able to classify a new            wear V B B max = 0.6 mm , if this has an irregular form”.
sample x in a certain class c k . So Q can have different            C = rε = the top radius of the tool = max. 2 mm.
aspects: an analytical one (with a little probability), an                The classes c1 , c3 , c4 and c5 in table 2 will be
artificial neural network (ANN), a pattern recognition,              adapted in accordance with these prescriptions and they
a fuzzy system, etc.                                                 will be rearranged as in table 3. [6] shows, in
     Du, et al. [4] represent the main source of                     connection with class c2 in table 2, that “the advanced
inspiration for this paper, our contribution being the               (catastrophic) deterioration means the intense
improvement of the classes and of the monitoring                     deterioration of the cutting-tool edges after a period of
indices.                                                             normal cutting, under the combined action of all the
                                                                     factors involved in the processing“. For the quantitative
                                                                     evaluation of this state, an overfulfilment with more
2. Monitoring in turning                                             than 0.1 mm of the wear criteria, and the overtaking till
                                                                     0.1 mm showing a severe wear are proposed.
     Table 2 [4] shows and defines the classes (tool                      Class c7 in table 2 will be eliminated, so that
conditions) for monitoring in turning. A chip on the                 continual cutting (which might occur if keyway exits)
tool inserts bigger than 0.05 mm2 identified “Tool                   does not occur on the lathe, the making of the keyway
breakage”. “Chatter” is identified by the high                       being the last operation in the shaft working process. In
frequency noise and the chatter marks on the machined                conclusion, the n = 7 classes referring to the working
surface. “Transient cutting” (or intermittent cutting) is            conditions are those in table 3, where to the first three
produced by machining a work piece that has a slot                   classes the working conditions are normal and the
along the feed direction.                                            others are abnormal.
     The conditions in table 2 will be worked in order                    In order to obtain the monitoring indices the
to coordinate them with [5,6] from which we quote:                   following will be used:
“According to figure 1, the usual criteria for the wear              - strain gauges glued on the cutting-tool, which
                                                                     measure the components of the cutting force (Fy - the
                                                                     repelling force , Fz - the main force);
                                                                     - accelerations of cutter holder vibrations (a x , a y , a z).
                                                                          The signals of the sensors are registered
                                                                     simultaneously by means of device SPIDER 8
                                                                     (H.B.M.). It is 4.8 carrier-frequency technology for
                                                                     S/G (strain gauges) or inductive transducers. Spider 8
                                                                     is an electronic measuring system for PC, for electric
                                                                     measurement of mechanical variables such as strain,
                                                                     force pressure, path, acceleration and for temperatures.
                                                                     Each channel works with a separate A/D converter
                                                                     which allows measuring rates from 1/s to 9600/s.
                                                                          In [2] we made “the monitoring simulation”. The
               Fig. 1 The cutting-tool wear                          vibrograms representing: - the component variations of
of the rapid steel cutting-tools and of the cutting-tools            the cutting force; - the relative displacement between
with hard-cutting alloy plates are: - the average                    tool and piece, on the repelling direction; - the power
breadth of the wear by the separation from the main                  furnished by the electric engine, are realized with the
Table 2
The classes (tool conditions)
     Class               Tool                      Identification               Identification            Number of
                      conditions                     on cutter                  on workpiece               samples
      c1               Normal                    wear < 0 . 1 mm                   ---------              M 1 = 144
      c2            Tool breakage              chipping > 0.05 mm2                 ---------              M 2 = 49
      c3             Slight wear              0.11 < wear < 0.15 mm                ---------              M 3 = 114
      c4            Medium wear               0.16 < wear < 0.30 mm                ---------              M 4 = 114
      c5             Severe wear                 0.31 mm < wear                    ---------              M 5 = 114
      c6               Chatter                       Fresh tool                 Chatter marks             M 6 = 61
      c7           Transient cutting                 Fresh tool                 An axial slot             M 7 = 15
      c8              Air cutting                     ---------                    ---------              M 8 = 13

Table 3
The classes in turning
           Class                  Tool                            Identification                  Identification
                                conditions                          on cutter                     on work-piece
            c1                   Normal                        V B < 0.1 mm , or                     ---------
                                                               V B m a x < 0.2 mm
            c2                  Slight wear                 0.11 < V B < 0.2 mm , or                 ---------
                                                            0.21 < V B m a x < 0.4 mm
            c3                 Medium wear                  0.21 < V B < 0.3 mm , or                 ---------
                                                            0.41 < V B m a x < 0.6 mm
            c4                  Severe wear                 0.31 < V B < 0.4 mm , or                 ---------
                                                            0.61 < VB m a x < 0.7 mm
            c5                     Tool                        V B > 0.41 mm , or                    ---------
                                 breakage                      V B m a x > 0.71mm
            c6                    Chatter                           Fresh tool                    Chatter marks
            c7                  Air cutting                          ---------                      ---------

functions RANDN and RAND (from MATLAB). Based                     looked irregular (like a triangle), so the wear criterion
on them, 11 monitoring indices are calculated. The                VBmax was used.
ANN with 11 inputs (the number of monitoring
indices) and 8 outputs (the number of classes) is                 3. Experimental results
realized with 3 layers.
In [3] the experimental setup and experimental results                 191 recordings were made and the parameters of
are presented:                                                    the Spider device were set on: sampling frequency =
- Components of cutting force were calculated on the              9600 /s, no. of periods = 1, samples / period = 4800; i.
basis of the experimental study of the lathe cutting-tool         e. the device samples the received signals with a
bending and with the help of two strain gauges, stuck             frequency of 9600 Hz, but it can send to PC a
on the lathe cutting-tool and connected to SPIDER.                recording with 4800 samples, which corresponds to 0.5
The recordings were made during the longitudinal                  sec. Each working session lasted nearly 30 sec., and by
turning of a OLC 45 cylinder (Φ 113, L = 1000), with a            half this time, the Spider device was connected for one
lathe cutting-tool with metal carbide P20 and ℜ = 45o.            second.
It results:                                                            The cutting working conditions were: piece
       F z = 1136 ( ε2 I n r - ε1 I n r ) [daN],     (4)          diameters D = 113 ÷ 93.4 mm, the cutting depths t =
where ε1 I n r and ε2 I n r are the registered relative           0.5÷3mm, rotations n = 63÷500 rot/min., longitudinal
deformations of the strain gauges.                                advances s = 0.024 ÷ 0.5 mm/rot.; cutting speeds v =
- Cutter-holder accelerations (3 Bruel&Kjaer 4329 type            π Dmed n / 1000 = 22.3÷177.4 m/min.
accelerometers were mounted on a plate solidary with                   On each passing on the whole piece length (L =
cutter holder).                                                   1000 mm) the t constant was preserved, while s or n
- Tool wear (after each passing, the tool wear was                varied.
measured with the help of a Brinell lens. The wear spot                12 monitoring indices were calculated:
Z1 = v → cutting speed;                                      layer s2 = 27. The input matrix p has the dimensions 12
Z2 = t → cutting depth;                                      (monitoring indices) x 655 (recordings), and the output
Z3 = s → longitudinal advance;                               matrix y has dimensions 7 (classes) x 655. The training
                                                             functions are: tf1 = purelin, tf2 = tansig, tf3 = logsig,
Z4 = Fz → average value of the main cutting force;
                                                             therefore the output vectors have 7 elements, with
Z5 → Fz variation range (recording which has 960             values in domain (0, 1).
samples was split into 4 equal parts – 240 samples each           The first runs (with the training functions trainrp,
– and the maximum and minimum values were                    trainscg, etc.) showed errors only in the positions
calculated for each part; X5 is the difference between       corresponding to the recordings in classes c5 and c7, i.
the maximum and minimum average values);                     e. in the case of those classes which have the fewest
Z6 → number of intersections of oscillogram Fz with its      recordings. The increase of the number of recordings –
average value Fz ;                                           without making new experiments – may be performed
Z7 → the average of Fz power spectral density in the         by adding the same recordings several times to the
frequencies range 1 - 2400 Hz ;                              same class, which may be eventually affected by a
Z8 → the average of Fz power spectral density in the         noise of an average value of 0.1.
frequencies range 2401 - 4800 Hz ;                                Consequently:
                                                             - we fourfold the recordings in class c5: an average
Z9 → the average of Fz power spectral density in the
                                                             value noise of 0.1 is attached to the first set, the second
frequencies range 4801 - 9600 Hz ;
                                                             set is identical with the original one, an average value
Z10 → the average of azinr power spectral density in the
                                                             noise of 0.15 will be attached to the third set;
frequencies range 1 - 2400 Hz ;
                                                             - we threefold the recordings in class c7, acting by a
Z11 → the average of azinr power spectral density in the
                                                             way of analogy with the foregoing (only with first and
frequencies range 2401 - 4800 Hz ;
                                                             the second set).
Z12 → the average of azinr power spectral density in the          Therefore, c5 will consist of 60 recordings, c7 – 75
frequencies range 4801 - 9600 Hz .                           recordings, and the number of columns in the above
                                                             matrices grows from 655 to 750.
                                                                  Using the following instructions:
4. Use of ANN on monitoring of the tool wear                  ind = find(c = = 0); dim = length(ind); er = dim / 750,
                                                             we find: the “c” elements indices which are null, the
     The recordings are divided in two sets, “Learning”      “ind” number of elements as well as the network error,
and “Classification”, the first set having 60% of the        respectively.
number of recordings (those noted with “a”, “c” and               The results of a run are presented in what follows:
“e”), and the second set having the recordings noted            R = 8 ; Q = 750
with “b” and “d”.                                                 There was a redundancy in the data set, since the
     Columns “az” and “Fz” belonging to the recordings       principal component analysis has reduced the size of
in the “Learning” set, will be transferred into              the input vectors from 12 to 8.
MATLAB, where 7 tables corresponding to the 7                TRAINRP, Epoch 0/300, MSE 1.53359/0, Gradient
classes will be made up.                                     0.292509/1e-006
     The table analysis shows that – except for two          TRAINRP, Epoch 25/300, MSE 0.487294/0, Gradient
cases – the recordings in one class have quite similar       0.0150939/1e-006
average values of Fz and, as expected, values which          TRAINRP, Epoch 43/300, MSE 0.477332/0, Gradient
grow (as the wear grows) from one class to another.          0.0148044/1e-006
     The training set will contain the recordings in the     TRAINRP, Validation stop.
“Learning” set, therefore 60% of the total number of         ind =
recordings. Recordings “b” in the “Classification” set       Columns 1 through 18
will be allotted to the validation set, and recordings “d”   440 441 445 446 447 448 449 450 454 455
will be input into the testing set; therefore each set has   456 457 458 459 463 468 472 473
20 % of the recordings.                                      Columns 19 through 37
     ANN consists of 3 layers, with 2 hidden layers,         474 475 476 477 481 482 483 519 520 521
No. of Inputs = 12 (monitoring indices), No. Output          533 545 594 595 596 646 647 658 672
Neurons s3 = 7(classes), No. Neurons in the first              dim = 37 ; er = dim / 750 = 0.0493 = 4.93 %.
hidden layer s1 = 23, No. Neurons in the second hidden
     It is a useful diagnostic tool to plot the training,
validation and test errors to check the progress of
training. The result as shown in the figure 2 is
reasonable, since the test set error and the validation set
error has similar characteristics.




                                                                                Fig. 3. The experimental setup
                                                                  should transmit a recording to the PC, based on which
                                                                  with table 3). Then “Spider” connects (therefore itself
                                                                  again, and so on. In case the class exceeds 3 ANN will
                                                                  say the class the processing is into (in conformity
                                                                  “abnormal”), PC will produce a sonorous signal, or
                                                                  stop the processing.
                                                                        To detail: the recording has 4800 samples, being a
             Fig. 2. The progress of training
                                                                  table in EXCEL with 4800 rows and 3 columns (A =
     In other runs:
                                                                  ε 1inreg, B = ε 2inreg, C = a z). The transfer Spider → PC
- carried out under the same circumstances, results
                                                                  is carried out within nearly 1 min. Out of this table a
were twice as above, and once as follows:
                                                                  new (smaller) table is selected and it will consists of
      51 epochs; dim = 27 ; er = 3.6 %;
                                                                  960 rows and 4 columns, the first element being
- without “init” function (to reinitialize weights and
                                                                  selected at random, at a location higher than A500.
biases), in two runs the errors were 4.67% and 13.3%;
                                                                  Function Fz (= 1136*A-1136*B) is calculated in fourth
- with the trainscg training function without “init” the
                                                                  column, according to formula (4). This table is
error was 5.33%, whereas with “init”- 25.6%.
                                                                  transferred in MATLAB, where 12 monitoring indices
                                                                  will be calculated and then they are presented at the
                                                                  ANN input.
5. Monitoring
                                                                  To see how ANN responds we use some of the
                                                                  recordings; the results are presented in table 4. With
    Monitoring the tool wear involves that during the
                                                                  the first 4 recordings, after the first run a second run
continuous process of cutting (fig. 3) the “Spider”
                                                                  was carried out, and it lasted 2 min., the class given by
device should be connected into the system, and it
                                                                  ANN coinciding with the one of the first run.
Table 4
The answer of ANN

                                    v             t              s                          Error       Class      Time
  No      Rec.      Class        [m/min]        [mm]          [mm/rot]       Epochs          %          from       [min]
                                                                                                        ANN
   0       1          2              3            4              5               6             7          8          9
   1      046         1             69           0.5            0.25            43           4.93         1          10
   2      183         5             71            3            0.302            40           4.4          5          8
   3      168         4            75.4           2            0.302            76            2.5         4           6
   4      174         4           120.6           2            0.416            67          3.47          4          8
   5      099         2            83.2           1            0.334            43           4.93         2
   6      190         7              0            0              0              47           3.6          7          7
   7      095         6            83.2           1            0.353            47          23.87         6          7

    The coincidence of the values in columns 2 and 8                  To asses the soft efficiency, the runs will be
shows that ANN provides correct outputs.                          resumed, as in table 4, but for one third of the 44
Table 5
The answer of ANN for class 3
  No.       Recor.         v             t             s            L         Epochs         Error        Class
                        [m/min]        [mm]        [mm/rot]       [mm]                         %         in ANN
    0         1            2             3            4             5            6              7           8
    1        115         130.6          1.2         0.334          245           40           4.4           2
    2        118         130.6          1.2         0.416           90           76           2.53          3
    3        126          79.3          1.4         0.292          770           43           4.93          3
    4        129          79.3          1.4         0.353          630           43           4.93          3
    5        133          63.4          1.4         0.353          425           40            4.4          4
    6        136          63.4          1.4         0.292          300           76           2.53          3
    7        139          63.4          1.4         0.212          200           64           2.67          3
    8        142          63.4          1.4         0.167          120           67           3.47          3
    9        146          77.3          1.5         0.375          780           43           4.93          3
   10        149          77.3          1.5         0.302          615           47           3.6           3
   11        152          77.3          1.5          0.25          480           47          23.87          3
   12        155         123.7          1.5         0.177          340           40           4.4           3
   13        158         123.7          1.5         0.146          230           31          15.2           3
   14        161         123.7          1.5         0.118          130           76          2.53           3

recordings of class 3. Table 5 presents the results; “L”      possible.
is the distance from the centre of the cut zone to
universal. It is noticed that only the recordings under
no. 1 and no. 5 did not give the correct class (3), but a     7. Acknowledgement
neighboring class. Other two runs carried out for each
of these recordings – with the modification of the               This research was supported through two grants by
location of the first element in the table (960 x 4) –        Ministry of Education of Romania [1, 7].
showed the correct class (3). Therefore, with 25 runs
only two were erroneous, the error amounting to 8 %.          References
We consider this error to decrease in the future if we
                                                              [1] Balan, G, 2002, The monitoring of a lathe using an
take several recordings in tables 4 and 5.
                                                              artificial neural network, Grant type A nr. 33 445,
                                                              Theme 19, Cod CNCSIS 451
                                                              [2] Balan, G., Tarau, C., 2003, The monitoring
6- Conclusions
                                                              simulation of a lathe, Mathematical & Computational
     The algorithm to monitor the tool wear making use        Applications, an International Journal published by the
of ANN proved efficient, the error range being below 5        Association for Scientific Research, Vol. 8, Nr. 3, pp.
percent.                                                      279-286
                                                              [3] Balan G. and Epureanu A., 2005, The monitoring
     In the case of real monitoring, when the cutting is
                                                              of a lathe using an artificial neural network (1-st part),
continuous, to avoid “thermal no-compensation” a
                                                              Annals of DAAAM for 2005 & Proceedings of the 16-
cooling of the knife should be provided. However,
                                                              th International DAAAM Symposium “Intelligent
water can cause trouble in the circuits of strain gauges,
                                                              Manufacturing…”, Croatia, p. 019-020.
although they are protected (with Poxipol).
                                                              [4] Du, R., Elbestawi, M. A., Wu, S. M., 1995,
Consequently, for this step of the experiment, the strain
                                                              Automated Monitoring of Manufacturing Processes,
gauges will be removed and the cutting force
                                                              Part 1: Monitoring Methods, Part 2: Applications,
components will be measured by averages of a
                                                              ASME Journal of Engineering for Industry, may, vol.
KISLER device (Austria).
     Moreover, column 9 in table 4 shows that the             117, Part 1-pp. 121 - 132, Part 2 − pp .133 - 141.
current hard provides delayed information, i. e. we           [5] STAS 12046 / 1 - 81, Cutting life testing. Wear.
know that the tool is – for example - in class 4 (Severe      General notions
wear), when it may have reached class 5 (Breakage), or        [6] STAS 12046 / 2 - 81, Cutting life testing. Tool -
6 (Chatters). Therefore, a highly specialized PC is           life testing methods in turning tools.
required, to reduce the responding time as much as            [7] A. Epureanu, Contract nr. 22CEEXI03/’05, MEdC.

								
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