The Implementation of Microprocessor Device for ...

2013 International Conference on Process Control (PC) June 18–21, 2013, Štrbské Pleso, Slovakia

The Implementation of Microprocessor Device for Drilling Process Monitoring based on Artificial Neural Network %H]iN 3DYRO

KaravaevYury, Klekovkin Anton Department of Mechatronic Systems Kalashnikov Izhevsk State Technical University Izhevsk, Russia e-mail: [email protected], [email protected]

Abstract²This paper deals with research of implementation of artificial neural networks for machining processes monitoring. A microprocessor device, neural network algorithm and program for it were developed. Different neural networks parameters were simulate, and on the example of the real drilling process the artificial neural network was trained to recognize three possible cases: normal drilling process, drilling bit wear, and drilling bit breakage. The results of experiments are described. Keywords²drilling process monitoring; artificial network; microprocessor device; automatisation

I.

neural

Slovak University of Technology Faculty of Materials Science and Technology Trnava, Slovakia E-mail: [email protected]

II.

DEVICE FOR MONITORING BASED ON ARTIFICIAL NEURAL NETWORK

In this work it is suggested the design of the informational monitoring system which realizes the neural networking identification of the tools state in processing and informs the operator. For the realization of the monitoring process the informational monitoring system was designed. Its structuralfunctional scheme is shown in ³Fig. ´

INTRODUCTION

Modern multi-axis machines allow producing parts of very complex geometry and their cost is very high. Eventual surface damages of work parts that can occur at each production step lead to rejects of the whole part. The analysis of statistical data of the machinery production shows that the main reasons of the rejects are defects in work pieces, mistakes of the operator (technologist) and breakage or excessive wear of tool. Number RI RSHUDWRU¶V mistakes started to decrease quickly when his experience is increased. The quality of the work pieces can be better in case of increasing of requirements and responsibility of the providers and also by introducing an incoming inspection. And the hardest thing is the third group of reasons. This is a detection of the state of cutting tool in machining process. Information about the tool state gives possibility to stop the process at the moment of tool breaking or by excessive tools wearing. That helps decrease number of the rejects and the prime cost of the output products. There are many monitoring systems which define the state of tool even in processing. Most of them are based on the measuring of the cutting forces and/or the vibrations. However it is impossible to define the state of the tool uniquely by analyzing these characteristics [1].

c 978-1-4799-0927-8/13/$31.00 2013 IEEE

PC

Microprocessor device with NN

Measuring system DDU

Machine tool (machining process)

Fig. 1. Structural-functional scheme of the informational-measuring system of the cutting operation

This system can be embedded in each machine, which process the cutting operation and has informational-measuring system. In this work a drilling is considered as the cutting operation. The boring tool holder with the measuring (or sensor) system DDU by Artis is fixed on a spindle of the boring (or milling) machine. This sensor system provides the synchronous measuring of the axial force and the cutting torque by drilling, boring and tapping processes [1], [2]. Sensor consists of tensometric rotor DDU fixed on tool holder,

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and stator DDU receiving signal from rotor. Both sensor elements have level of protection IP67. Measurement precision LV “ RI PHDVXUHPHQW range. Analog data of axial force and torque are received from DDU to conversion device DDU4. Digital data from conversion device is transferred to the microprocessor through RS232. On the base this microprocessor the neural networking monitoring system is realized. STM32F407VG by STMicroelectronics was chosen. It has required memory size and the high-speed core. The personal computer is used for saving data from the monitoring system and for learning the neural network by these data. In spite of many versions of neural networks, all of them have similar characteristics. They have many neurons which are connected to many other neurons. The scheme of a typical neuron is shown in ³Fig. 2´.

f ' ( x) D ˜ f ( x)(1

The artificial neuron has synapses which connect inputs of neuron to the core. The next it has the core which processes input signals. And also it has the axon which links to neurons of the next layer. Each synapse has a weight which defines how its input of the neuron influences on its state [4]. State of a neuron is defined by following formula: n

S

¦x w i

i

(1)

i 1

Where: n ± number of inputs of the neuron; xi ± value of i-th neuron; wi ± weight of i-th synapse; The next a value of the axon is calculated by following formula: Y = f(S) (2) where f is an activation function. We use the Gaussian activation function, which is shown below:

1 1 e

Dx

(3)

164

f ( x))

(4)

The most important property of the neural networks is the possibility of learning on the base of data of the environment and eventually to increase their productivity. Learning of the neural network means the interactive process of changing of "weights". In this work it is used supervised learning (learning with a "teacher"). Supervised learning guess that each input vector of data have a target value of the output vector. These vectors make the learning pair. Weights are changed until an error between WKH QHWZRUN V RXWSXW DQG WKH WDUJHW YDOXH GRHVQ¶W KDYH DQ acceptable level of displacement. The algorithm of backward propagation of errors: 1. To initialize weighting factors by little random values; 2. To choose a learning pair from the learning set, to give a input vector to the input of the neural network; 3. To calculate output values of the network; 4. To calculate an error between the network's output and the target value. 5. To correct weights for GHFUHDVLQJ DQ HUURU¶V YDOXH The step of correcting weights is examined below in detail. The first case is correcting weighting factors of the output layer. The index p designates a neuron from which a weight of synapse comes. And the index q designates a neuron to which a weight goes. There is a IDFWRU / ,WV IRUPXOD LV VKRZQ EHORZ

Gq

Fig. 2. The scheme of the neuron

f ( x)

The important advantage of this function is that it has the easy derivative on the x-axis. The derivative is shown below:

OUTq (1 OUTq )(Tq

OUTq )

(5)

Thus the weights of the output layer after correction are:

w p q (i 1)

w p q (i) KG q OUTq

(6) Where: i ± number of current iteration of learning; wp±q ± value of a weighting factor which connects the pneuron to the q-neuron; ± factor of the speed of learning; OUT ± value of the output of a neuron; The second case is correcting weighting factors of the hidden layer: M

Gq

OUTq (1 OUTq )¦ G k wq

k

(7)

k 1

In this algorithm propagation of errors comes from outputs of the network to its inputs. It is an inverse direction of propagation of signals in usual work. By way of input data it is used values of the cutting force and the torque (in the future also there will be information from a vibration sensor, which is mounted on the work piece and/or on the bearing assembly of spindle) [3]. For learning of

the neural network preliminary experiments of drilling of steel were made by carbide drill bits with diameter ± 10,7 mm. The type of derived relations, which were supplied to inputs of the neural network, is shown in ³)ig. 5, 6´. The neural networks have many parameters that determine the speed of learning and the result. For example, number of OD\HUV RI QHWZRUN QXPEHU QHXURQV LQ WKH OD\HU WKH IDFWRU . LQ the Gaussian activation function (3), the factor of the speed of learning (6) (8). A choice of these factors is a very laborious problem demanding an experience and an independent review [4]. To find optimal values of learning parameters such as number of learniQJ VWHSV VSHHG RI OHDUQLQJ DQG WKH IDFWRU . LQ the Gaussian activation function (3), the analysis of value of the mean-squared error of learning was made by MATLAB using different values of these factors. 7KH HUURU¶V IXQFWLRQ RI such factors as the factor of speed of learning, number of step RI OHDUQLQJ DQG WKH IDFWRU . LQ WKH *DXVVLDQ DFWLYDWLRQ function. Graphs are shown in the ³)ig. 3´ when number of step of learning is 100, the factor of speed of learning is 0.5 (blue), 1 (green), 1.5 (red).

Fig. 4. The structure of the neural network

In the process of learning data of the cutting force and the torque from the monitoring system were given to the inputs of the neural network in case of set values of spindle rotation speed, feed rate, diameter and type of tool, work pieces material. There was 3 state of tools as output values: normal process of drilling ³Fig. 5´, drilling in case of a wear and a breakage of drill bit ³Fig. 6´. For a simplification of the experiments and the demonstration of operability of the system it was used only data of the torque, because they describe the situation more qualitative. Used the following operation modes: - spindle rotation speed 1000, 2000 and 4000 rpm; - feed rate 0,03; 0,1; 0,3; 0,5 mm/rev; - material± Steel 40; - tool ±carbide GULOO ELWV ‘ ,7mm. Experimental data are adduces in articles [1] and [2].

Fig. 3. The graphs of learning error when number of step of learning is 100, the factor of speed of learning is 0.5 (blue), 1 (green), 1.5 (red).

Fig. 5. The graph of torque in case of normal work

In the result the conclusion was drawn that for the effective work of the neural network the factor of speed of learning should take a value from 1 to 1.5 7KH IDFWRU . LQ WKH *DXVVLDQ activation function should take a value from 0.5 to 1.5 and it is necessary to make a minimum of 100 steps of learning. It needs to choose correct values by the experiment depending on percents of the correct identification by one or other values of the factors. Also the two-layer neural network with 4 neurons in the first layer and 2 neurons in the second was chosen. The structure of the neural network is shown in ³)ig. 4´.

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Fig. 6. The graph of the torque in case drill bit breakage (after drilling 17 holes without coolant).

ogid3[] - array of target values for input values of the torque in case of the drill bit breakage; void Res(Neyro* nr) ± function that calculates output values of the neural network;

Fig. 7. The graph of the torque in case of the normal drilling (green) and drill bit breakage (blue).

On the ³Fig. 7´ two graphs are shown for comparison. The green graph is a torque in case of drilling by absolutely new drill bit, and the blue graph is a torque of drilling of 18th hole with a drill bit breakage. Intermediate graphs (from 14 to 17) were considered as a wear of drill bits by operator. In the SURFHVV RI H[SHULPHQW GULOOLQJ GLGQ¶W KDYH FXWWLQJ IOXLG IRU D larger wear. There were 20 learning samples that were preliminary experimental data which were given to the input of the network. For experimentation we developed the program of the artificial neural network for the microprocessor and the control program for the PC. For learning the neural network saving data were transmitted from the PC. After learning the developed device was directly connected to the sensor system Artis DDU. The program for microprocessor is written in C language. For the creation of the neural networking model a structure is XVHG 7KH SURJUDP¶V WH[W RI KHDGHU ILOH RI LQLWLDOL]DWLRQ RI WKH neural networking model is shown in the ³)ig. 8´. Where: Number_Input ± number of inputs of the neural network; Neyr1, Neyr2 ± number of neuron of the first and second layers respectively; input[] ± array of values of inputs of the neural network; prom[] ± array of values of outputs of the first layer of the neural network; res[] - array of values of outputs of the neural network; w1[][] ± matrix of weighting factors of the first layer; w2[][] ± matrix of weighting factors of the second layer; k ± factor of the activation function (3); skor ± factor of the speed of learning; ogid1[] - array of target values for input values of the torque in case of normal drilling; ogid2[] - array of target values for input values of the torque in case of the drill bit wearing;

166

Fig. 8. 7KH SURJUDP¶V WH[W RI KHDGHU ILOH RI LQLWLDOL]DWLRQ RI WKH QHXUDO networking model

void CreateNeyro(Neyro* nr) - function of initialization of the neural networking model; void Class(Neyro* nr) ± function of identification one or another situation; void Write_input(Neyro* nr, const double *buf) ± function of formatting of input values of the network; void Learning(Neyro* nr, const double *buf, const int *ogid) ± function of learning of the neural network; The output values of the neural network are from 0 to 1, that is why this values are rounded to integer numbers. There are 3 situation for the identification that is why 2 neurons on the output layer are enough. Values of output neurons for the situation of the identification are following: 1 and 0 - drill bit wearing; 0 and 1 ± drill bit breakage; 1 and 1 ± normal work; The system recognizes the state of tool by analysis of input data. For example, if you give to the input of the network data which are shown as the relation in the ³)ig. 4´, the program shows a message for an operator, that it is necessary to change the tool. The accuracy of identification was about 97% in case of drilling by the same drill bit and with the same factors by learning. We made an experiment of drilling of aluminum by 20 mm drill bit (spindle rotation speed 2000 rpm, feed rate 0,16 mm/rev) for the test of the neural network. These values are critical values from the recommendation RI WKH WRRO¶V producer. In the result of the monitoring by microprocessor with the trained QHXUDO QHWZRUN RSHUDWRU¶V PHVVDJH ZDV VKRZQ WKDW

there was a critical wear and the operator stopped the drilling ³Fig. 9´. Because of that the work piece and tool were saved. The geometry of worn tool can be restored, and broken tool cannot. A critical wear led to breakage of tool by drilling wiWKRXW WKH PRQLWRULQJ V\VWHP ³Fig. 10´. On the ³Fig. 10´ the value of the torque exceeded the upper limit of the measuring system (for drill bit with a diameter 20 mm).

Also number of learning samples is increased for each device of monitoring. This system needs to use on productions which have several machining centers. They will exchange information which necessary for the correcting weighting factors. This scheme makes it possible to increase speed of learning in several times. IV.

CONCLUSION

Using artificial neural networks in monitoring different processes has many outlooks. In that cases when it needs to consider many factors and data from sensors, the neural networks have a great advantage over another similar systems. The computational experiment confirms operability of the neural networking models. Fig. 9. The graph of the torque in case of the drill bit wearing (with the neural networking system)

Fig. 10. The graph of the torque in case of the drill bit breakage (without the neural networking system)

However, it reveals following disadvantages in processing of realization this monitoring system: - a large number of learning samples for the each individual situation; - a large number of experiments and time for the first learning (number defines the accuracy); - complication of the program in case of increasing number of input data; - necessity of relearning the network in case of using another tools or worked material; Advantages of this system: - possibility of learning in the processing of work; - possibility of using in the many automation systems; - possibility of building of the neural networking models by a microprocessor without using PC; By way of outlooks of the development this topic it can be noted such unrealized possibilities such as interaction with the control system and increasing number of input data, for example, a ZRUNHG PDWHULDO WRRO¶V JHRPHWU\ DYDLODELOLW\ RI cutting fluid, rigidity of machine etc. And also there are choice of optimal neural network structure and realization of the network model. ACKNOWLEDGMENT

Fig. 11. Structural scheme of the network model of intelligent monitoring system for mechanical operation

In future, it will be plan to integrate microprocessor device of monitoring with the artificial neural network in the control system of the machine tool. III.

NETWORK MODEL OF INTELLIGENT MONITORING SYSTEM FOR MECHANICAL OPERATION

In the future the database for learning and correcting weighting factors will be saved directly in the microprocessor device of monitoring, for example, on a SD-FDUG ³)LJ 11´. The scheme which is shown in the ³)ig. 8´, realizes a network monitoring model of some mechanical operations at the same time. So if to connect some similar devices to the network and to unite their accumulated in the processing databases, it is possible considerably to increase productivity of these devices.

This work was written with a financial support VEGA agency LQ WKH IUDPH RI WKH SURMHFW Ä7KH GDWD PLQLQJ XVDJH LQ PDQXIDFWXULQJ V\VWHPV FRQWURO³ The contribution is sponsored by KEGA 003STU-4/2012 SUHSDUHG SURMHFW Ä(ODERration of interactive multimedia WH[WERRN RI 0HFKDWURQLFV IRU VHFRQGDU\ YRFDWLRQDO VFKRROV³ REFERENCES [1] I. Abramov, Y. Karavaev, P. Lekomtsev, A. Abramov ³Force influence estimation on a mechatroQLF¶V LQVWUXPHQWDO PRGXOH GXULQJ ERULQJ ZRUN´ Vestnik of IS78 ‹ ,]KHYVN SS -35, ISSN 1813-7903 [2] - ýHUQHFNê ( 3LYDUþLRYi ³3RVVLELOLWLHV DQG SURVSHFWV RI KRORJUDSK\´ Izhevsk State Technical University, Russia, 2007, 124 pg. ISBN 978-57526-0303-7 [3] Y. Karavaev, P. Lekomtsev, A. AEUDPRY ³&RPSDUDWLYH DQDO\VLV RI IRUFH FRQGLWLRQV GXULQJ GULOOLQJ SODVWLF DQG IUDJLOH PDWHULDOV´ 9HVWQLN RI ,678 ‹ ,]KHYVN SS -15, ISSN 1813-7903 [4] J. A. Anderson, An Introduction to Neural Networks, MIT Press, 1995

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