Sewing Machines Based on High Accuracy Magnetic Encoder - VS

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The whole system mainly include the power supply module, PMSM, magnetic encoder, central control unit, detection and protection circuit and a few other auxiliary control parts. The hardware block diagram is shown as the figure 1. In order to enhance the control accuracy and processing speed, two 32-bit microcontrollers uPD70F3184 produced by NEC Corporation of Japan are used as the core-controller of the control board and the signals processor of the magnetic encoder, respectively. After digital difference and error correction the absolute magnetic encoder has a very high precision[5]. The core-controller realizes input-output function and motor control function. In the corecontroller, a full-digital design method is adopted to implement the close-loop control of PMSM, including current loop, speed loop and position loop[7]. Most of the control function can be achieved by software instead of hardware, so can reduce the cost of industrial sewing machines driving control system. Switching Power Supply Driver Circuit Rectifier Protection U nits I P M M o d u le P M S M Magnetic Encoder Power G r id C o re -controller Current D ecetion S C I A D I/O Fig. 1 Hardware block diagram 

CONTROL SYSTEM STRUCTURE 

A. Mathematic model of PMSM Sine-wave PMSM most commonly used method of analysis is the rotating d-q coordinate mathematical model. The model not only can be used for analyzing the motor’s steady performance, can also be used to analysis the dynamic performance of the motor. In the drive control system it is used to estimate the motor’s speed and the position of the magnetic pole. The stator voltage and flux equations of PMSM in the rotating d-q coordinate are given by: ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ + Ψ + Ψ = − Ψ + Ψ = e d s q q q e q s d d d R i dt d u R i dt d u ω ω 

(1) Position Control of PM Synchronous Motors Used for Sewing Machines Based on High Accuracy Magnetic Encoder Hongqin Xie 1 , Kai Yang2 , Dadai Lu3 State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan, 430074, R.P.China E-mail: hongqin125@126.com ⎩ ⎨ ⎧ Ψ = Ψ = + Ψ d q q d d d f L i L i 

(2) where ud, uq, id and iq are the stator voltages and currents, respectively, Rs is the stator resistance, Ld and Lq are the d-q axis stator inductances, respectively, Ψd and Ψq are the d-q axis stator magnetic flux, respectively, ωe is the electrical angular velocity, Ψf is the rotor flux. For the surface-mounted PMSM studied by the paper, the Ld equals to the Lq. So the electromagnetic torque equation is e n f q 2 3 T = P Ψ i  Sewing machine shops in chennai

(3) where Pn is the pole pairs. The mechanical equation of the PMSM is described as: e e e ω ω − = + RΩ dt d T T J L 

(4) where J and RΩ are the inertia and the friction coefficient of the motor, respectively, Te and TL are the electromagnetic torque and the load torque, respectively. 

B. Structure of the control system Fig.2 shows the various components of the control system, including PI, Park, IPark, Clark module and so on. The udc comes from the single-phase rectifier bridge[8]. In the position loop, a negative feedback and a proportional regulator is used in order to get a rigidity position control without overshoot. In the speed loop, a negative feedback of the motor’s speed and a PI regulator is used. The output of the speed regulator is the reference current value. The classical PI control strategy is used in the current loop[9]. Before entering PI module the calculated actual speed and the currents should go through low-pass filter, reducing highfrequency interference. For a PMSM there are several strategies to calculate the d-axes and q-axes currents. In the article a vector control method on the basis of zero d-axes current is adopted to get a maximum torque current ratio. Inverter SVPW M P I d/d t Park P M S M Magnetic Encoder IPark iqre f idre f =0 ud c iq id ia ib ic iα iβ θ ωe θ * udre f uαre f ━ ━ ━ uβre f ━ θ P P I P I Clark ω uqre f * e Fig. 2 Structure of the Control System 

C. Magnetic encoder module The structure of the improved magnetic encoder is shown as the figure 3. It mainly includes the alnico, the soft magnetic ferrite and the Hall elements, etc. A pair of permanent magnets, which are placed in the alnico symmetrically and rotate following the motor, are the sources of the angle signals. The soft magnetic ferrite is used to reduce the flux leakage, makes sure the internal magnetic field is more uniform and concentration. The six channels voltage produced by the Hall elements are divided into three groups as shown in the figure. After digital difference, the value scope of the samples is expanded and the system error by installation is reduced. N S x y Hallelement A ir G ap So ft Magnetic Fe rrite Aln ic o A+ A- B+ BC+ CFig. 3 Structure of the Magnetic Encoder The signal processing part of the magnetic encoder is given in figure 4. The core-controller communicates with the processor of the magnetic encoder for the motor’s real-time speed signal. After getting the order of the core-controller, the processor starts AD converter to sampling the voltages, which are from the Hall elements. https://vssewingmachine.in/ Then it matches the samples values with the standard table, prepared in its storage, to gain the speed signal and feedback the result to the core-controller. Hallelements Computing U nit A D Converter S C I Co r e -controller Signal Processor Six Channels Voltage Sta rt-u p A D Digital Voltage Signal Results Ord e rs Re a l-time Sp e e d Signal So u rce Control Board Fig. 4 Signal Processing Part of the Magnetic Encoder Fig.5 shows the program flow chart of the magnetic encoder, which is compiled according to the working process above. As can be seen from the chart, the basic idea of the program is that the signal processor makes different responses depending on the various instructions given by the core-controller. Initialization Receive the Instructions ? Static AD Sampling Sampling Voltages and Calculating Sp e ed Case 1: Sta rt-u p A D ? Case 2: Req u ire the Re a l-time Sp e e d ? Send Real-time Sp eed Default: Send Error Y e s N o Start Fig. 5 Program Flow Chart of the Magnetic Encoder 

Fig.6 shows the photo of the drive control system. With this system several experiments are carried out to test the control performance of the system. Fig. 6 Photo of the Drive Control System 

A. Angle detection of the magnetic encoder Angle calculation module, one the most important part of the magnetic encoder, is used to gain the position and speed signals of the motor serving the drive control system. The main idea of the computational procedure is that the divide the whole interval into six parts and set an Angle as the base value in each small interval. These Angles are 0 degrees, 60 degrees, 120 degrees, 180 degrees, 240 degrees, 300 and degrees. Look up the standard table to get an offset angle value using the AD samples values as the offset address. The actual machinery angle is the sum the offset angle value and the base value of this small interval. In accordance with the method above, getting the actual angle waveform from the magnetic encoder is shown in figure 7. Fig. 7 Actual Angle Waveform The horizontal ordinate is the value of angle changing from 0 to 65535. The vertical coordinate is the digital samples values of the voltages outputted by the three groups Hall elements. As shown in the figure, the results are very good sine curves. But distortion of the shape is still existent in some points. It will have an unfavorable effect on the control performance of the system. 

B. Performance of starting current The control system adopts PM+ of NEC Company as the software design platform. This design platform can not store data online, also can not generate dynamic diagram. So to watch the motor’s dynamic speed waveform directly becomes troublesome. This experiment studies the motor’s start procedure with the starting current waveform. Reference speed is 4000 rpm, starting with the rated load (1.2 N·m). The observation of the starting current curve on the oscillograph is given in Fig.8. Fig.8 Starting Current Curve Fig.8 shows that the current transition process holds about 0.134s. In this period, its magnitude is almost 1.5 times the value of the following steady-state current. When the motor runs stable, the cycle and amplitude of the current keep constant primary. The current curve above indirectly reflects the starting process of the motor and speed control performance of the system. 

C. Performance of steady-state current Fig.9 gives the waveform of a steady-state phase current observed by an oscilloscope. Fig.9 Steady-state Phase Current Curve The figure above shows that the current’s amplitude and frequency are constant. The waveform is of a better sinusoidal property. The motor could go with a steady operation and the speed fluctuation is small. The control system basically achieves the expected control performance. At the same time the current waveform still contains much burr, because the signals of the magnetic encoder are not precise enough and the influence of the disturbance is inevitable in the current detection scene. Therefore, it is very important to enhance the filtering on feedback signals and improve the precision of the magnetic encoder for the future work.

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KEY TECHNIQUES IN MOTOR DRIVE AND CONTROL SVPWM - VS

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A. SVPWM Technique Normally, as Fig.1 shows three-phase PMSM is driven by voltage source. 

 As it is known to all, PMSM becomes more and more popular in the field of the production of electric vehicle nowadays. However, the control performance at low speed of the motor is still influenced by ripple torque because of harmonic wave. Space Vector Pulse Width Modulation (SVPWM) is available to improve the quality of the stator currents and reduce the ripple torque remarkably [9]. 

1) Fundamental Space Voltage Vectors: The reference stator voltage space vector for PMSM can be defined as: 2 2 3 3 2 ( ) 3 j j U U Ue Ue s ab c π π − = +⋅ +⋅ (1) Where, Ua, Ub, Uc are motor phase voltages. In order to simplify the computational process, we plan to introduce - axes shown as Fig.2. So Eq. (1) can be written as Eq. (2). U U jU s = + s s α β (2)

Let’s assume the state of the voltage source inverter is ‘001’. In this case VF1, VF4 and VF6 would be turned on. So that the current would flow into the motor through armature ‘A’ and stream out of the motor from armature ‘B’ and armature ‘C’. And then, we can suppose that the voltage value of the three-phase of the motor equals 2/3U,-1/3U,-1/3U respectively. According to Eq. (1) and Eq. (2), it can be obtained that: 0 2 0 3 U Uj = + (3) Singer sewing machine showroom in chennai Therefore, other Fundamental Space Voltage Vectors can be calculated as the same way. And the states of ‘000’ and ‘111’ are defined as the Zero Vector.  

2) Synthesis of Space Voltage Vectors: Suppose Ux and Ux+60 are two adjacent Fundamental Space Voltage Vectors shown as Fig.4. And then, we can get that˖

In the expression above, t1 and t2 stand for the action time of the two adjacent Fundamental Space Voltage Vectors; while t0 and t7 represent the action time of the Zero Vector. Fig. 4 Synthesis of Space Voltage Vectors. With sine theorem, it can be found that 1 2 60 sin(60 ) sin sin120 x x PWM PWM s t t U U TT U θ θ + = = − (5) Combined with Eq. (4), we can work out that 1 2 60 2 sin(60 ) 3 2 sin 3 s PWM x s PWM x U t T U U t T U θ θ + = − = (6) Where, means the angle between US and Ux. If t1+t2>TPWM, t1=t1TPWM / (t1+t2); and t2=t2 TPWM / (t1+t2). And with Eq. (4), t0 and t7 can be defined as 0 7 12 ( )/2 PWM t t T tt = = −− (7)  

3) Realization of SVPWM: Presently, SVPWM can be realized through the regulation of voltage source inverter. The procedure of SVPWM algorithm has always been divided into three steps including sector judgement, calculation of the action time and distribution of the duty factor. 

Step 1: Sector Judgement. We find u1, u2 and u3 as three unit normal vectors as presented in Fig.5. And with the - axes transform, it can be formed as 1 2 3 1 0 1 3 2 2 1 3 2 2 u u u u u α β = − − − (8) Thus, the formula that used to sector judgement can be described as: 3 21 Sector sign u sign u sign u =++ 4 ()2 () () (9)

Step 2: Calculation of the Action Time. The action time for VF1 to VF6 located in the source voltage inverter was deduced in Ref. [9] and [10]. So it can be computed that: 1 2 2 sin(60 ) 3 2 sin 3 s PWM s PWM U t T U U t T U θ θ = − = (10) 1 2 2 sin 3 2 sin(60 ) 3 s PWM s PWM U t T U U t T U θ θ = = − (11) If the motor is operating in Sector I, II and IV, the action time t1 and t2 can be calculated as Eq. (10), else, the t1 and t2 should be expressed as Eq. (11). In Eq. (10) and Eq. (11), U is Fundamental Space Voltage Vector. https://vssewingmachine.in/ In view of the fact that the range of is located on [0, 60], we must finish the transformation between (the angle between Us and Ux) and (the angle between Us and axes ). And with the purpose of forming a revolving magnetic field as a circle inscribed in the regular hexagon shown as Fig.3, Us can be formulated as: 3 2 U UU s = ⋅ PID (12) Where, UPID is the output value of PID regulator, and 0 UPID 1.  

Step 3: Distribution of Duty Factor. The formula adopted to calculate the duty factor is shown as 12 0 2 0 0 0.5 0.5 0.5 a b c t tt t tt t t t =++ ⋅ =+ ⋅ = ⋅ (13) And ta , tb, tc represent the action time of VF1, VF3 and VF5 of the source voltage inverter. 

B. Measurement of Motor Speed and Motor Position As discussed above, it is known that achieving SVPWM needs the accuracy information about the rotation angle of the motor. 

1) Speed Measurement: Speed measurement is supposed as the first step for position determination. In this case we make use of three HALL sensors which are fixed on the surface of the motor with 120 mechanical degree’s phase delay. It is capable of generating such waves demonstrated in Fig.7 when the motor is running. And from Fig.7, we can also find that each HALL state indicates an operating sector of the motor.

The IC interrupt of MCU will be responded at once only when HALL state has been changing. As the result, the width of HALL signal could be measured accessibility. In IC interrupt subprogram, the switching time of the HALL state will be recorded by timer3 of MCU. And thus, the time span between two adjacent HALL states can be acquired easily as: ( ) ( 1) T timer k timer k HALL = −− (14) According to Eq. (14), the speed of the motor can be illustrated as: deg deg 60 ree HALL ree HALL Speed T HALL P = = (15) Where, HALLdegree means mechanical angle of the sector for the motor, and P stands for pole-pairs of the motor. Here P=3.  

And with the purpose of enhancing the precision of the calculated speed, average digital filter should be introduced as demonstrated in Ref. [3]. 

2) Position Determination: Position determination will be processed minutely in PWM interrupt subprogram. Thus, in the initialization part of the main program, PWM interrupt is activated and frequency of PWM is installed as 10K Hz. So that, the system will response PWM interrupt in every 0.1ms, and we can get the angle between Us and axes as: 360 int( ) 360 PWM PWM p T Speed p T Speed ⋅ ⋅ Θ= ⋅ ⋅ − ⋅ (16) Consequently, in accordance with the table II, the value of can be worked out at last. C. Nonlinear PI Control Algorithm Being simple, robust, effective and applicable to a broad class of systems, PID (Proportiona1-Integral-Derivative) controllers have been the most widely used and well known controllers in the industry for over 50 years [11]. And more than 90% of industrial controllers are based around PID algorithm, particularly at low levels [12]. However, there exist several contradictions between response time and overshoot of the system when referred to traditional linear PID algorithm [13]. In order to cope with the problem above, a kind of nonlinear PID algorithm has been studied for years. Usually, derivative unit may cause system oscillation, and PI control algorithm is chosen for speed regulation of the system so as to eliminate such phenomenon. The parameters for proportional and integral can be defined as: ( ) [1 sec ( ( ))] K p pp p k a b hcek =+ − (17) ( ) sec ( ( )) Ki i ii k a hb c e k = ⋅ (18) Thus, the expression for the nonlinear PI algorithm can be written as the flowing type: ( ) ( )( ( ) ( 1)) ( ) ( ) ( ) ( 1) ( ) PI P i PI PI PI uk K k ek ek K kek u k u k uk Δ = − −+ = − +Δ (19) In the Eq. (16), e(k) is the speed error, uPI(k) is the output, and uPI(k) is the output increment. The 6 parameters listed in Eq. (17) and Eq. (18) for nonlinear PI algorithm can be set as ap=0.8, bp=0.25, cp =1, ai =0.15, bi =0.025, ci =1. On the basis of Eq. (17) to Eq. (19), the simulation model of the algorithm can be created as Fig.8 by MATLAB. 

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MATHEMATICAL MODEL OF THE PMSM & ADRC - VS Sewing Machines

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PMSM is an important category of the electric machines, in which the rotor magnetization is created by permanent magnets attached to the rotor. Many mathematical models have been proposed for different applications, such as the abc-model and the two axis dq-model. Due to the simplicity of the two axis dq-model, it becomes the most widely used model in PMSM engineering controller design. 

The dq-model offers significant convenience for control system design by transforming stationary symmetrical AC variables to DC ones in a rotating reference frame. Based on the dq reference frame theory, the mathematical model of the PMSM can be expressed as the following equations: 1) Circuit equation ( ) d s d r q q d d u R i L i dt di L = − + ω (1) ( ) q s q r d d r f q q u R i L i dt di L = − − ω − ω Ψ (2) 2) Electromagnetic torque equation: [ ( ) ] e p f q d q d q T = n Ψ i − L − L i i (3) 3) Motion equation e L d r T T T dt d J = − − ω (4) 

Where di , qi represent the current of the d-axis and q-axis; d u , uq represent the voltage of the d-axis and q-axis; Ld , Lq represent the inductance of the d-axis and q-axis; Rs is the stator resistance; ωr is the rotor speed; Ψf

is the magnitude of the permanent magnet flux linkage; p n is the number of pole pairs; J is the inertia of the rotor;Te is the electromagnetic torque,TL is the load torque of the motor and Td is the uncertain torque disturbance caused by the external and internal disturbance ;  

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An ADRC consists of three components, a tracking differentiator (TD), an extended state observer (ESO) and a nonlinear state error feedback (NLSEF)[9], as shown in Fig. 1. Fig.1 The structure of ADRC. The function of the n-order TD is to arrange the ideal transient process. It tracks the input V(t) without overshoot and provides the generalized derivatives of the input signal, 1,1 z ,… ,n z1 . The function of (n+1)-order ESO is to observe the state variables 2,1 z , … ,n z2 and estimate the total disturbances 2,n+1 z of the plant. ESO can compensate the entire uncertain external and internal disturbance in real time. The function of NLSEF is to level off the output of the controlled plant and expand the stability region of the whole closed-loop system. 

The control output of NLSEF can be mathematically described by ( ) ( , , ) ( , , ) u0 t = k1 fal ε 1 a δ +…+ k n fal ε n a δ (5) where i ,i ,i z z = 1 − 2 ε (i =1,…n), a ,δ , and i k (i =1,…n) are adjustable parameters. The nonlinear function fal is defined by ⎪⎩ ⎪ ⎨ ⎧ ≤ > = − ε δ ε δ ε ε ε δ ε δ i a i i i a i fal i a 1 / sgn( ) ( , , ) (6) where sgn(x) is a sign function. Thus the actual control for the plant can be expressed as b z u t u t 2,n 1 0 ( ) ( ) + = − (7) According to the equation 4, the rotor speedωr can be described as: T T J T J r e L d = ( − )/ − / • ω (8) Td is considered as the total unknown disturbance of the servo system, which might be estimated and compensated by the ESO. According to the above theory, the ADRC for rotator speed is designed as below. Design the one-order TD as ( , , ) 11 0 0 δ 0 z = −k fal ε a • (9) wher ref = z − n 11 ε , 0 a , δ 0 , 0 k are adjustable parameters. 

Then the ESO can be constructed as ⎪⎩ ⎪ ⎨ ⎧ = − = − + • • ( , , ) ( , , ) ( ) 22 22 1 1 1 21 22 21 1 1 1 ε δ ε δ z k fal a z z k fal a bu t (10) where r ε 1 = z21 −ω , 1 a , δ 1 , 21 k , b, 22 k are adjustable parameters. In this paper, a conventional PI controller is used to replace the NLSEF of the ADRC in the Fig.1, which can enhance the calculating speed of the algorithm and maintain the disturbance rejection advantage of the ADRC. Where ⎪⎩ ⎪ ⎨ ⎧ = − = + ∫ u u z b u k k dt p i / 0 22 0 2 2 ε ε (11) where 2 11 21 ε = z − z , p k and i k are the adjustable parameters of the PI controller. The structural expression of the system control law shows control doesn't attach to the internal parameters of the system, but to the output and the reference input of the system.
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Active Disturbance Rejection Control for PMSM Servo System - VS

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Permanent magnet synchronous motor (PMSM) servo drive system has been widely used for industrial sewing machines. The conventional control method is PID, which has some disadvantages such as large overshoot, bad robustness. In this paper, a servo control of the industrial sewing machine system based on the active disturbance rejection control (ADRC) is proposed, which can arrange the transient process, estimate and compensate the uncertain internal and external disturbance. It can highly enhance the dynamic performances of the system. Based on the Matlab/simulink software, the simulation results of the industrial sewing machine control system proved the effectiveness and robustness of the ADRC control strategy. 

With the rapid development of the world textile industry, the research on industrial sewing machines have been focused on higher precision, more energy-saving, lower cost, higher speed, multiple-function, more intelligence. In order to achieve all these performance indexes, the main problem is to develop an effective servo system. And the PMSM based servo systems have attracted more and more researchers, for which has several inherent advantages compared with other types electric machinery, such as high power density, high power factor, high torque to current ratio, high efficiency, low inertia, rugged construction, easy for maintenance and so on[1,2]. The PID control is one of the early developed control strategies. Due to its simple algorithm, good robustness and reliability, it has been widely used to design position, speed and electric current loop controller in industrial sewing machine servo system. Sewing machine shops in chennai And classic PID control based, such as increment PI control, fuzzy PID control, Neural PID control , multi-segment PI control are also adopted[3,4]. However, the classical control strategies have some disadvantages, such as, large overshoot, long adjustment time and so on. It is difficult for the PMSM servo system to realize high precision control. PMSM servo system is a typical non-linear time-variant control system. 

Many intelligent control methods such as fuzzy control, self-adaptive control, neural network control, sliding mode variable structure control, genetic algorithm control are adopted to solve the problems. Many scholars have done a lot of meaningful researches on these control methods [5,6]. But it still has some difficult to realize. The ADRC can not only arrange the transient process, but also estimate and compensate the total disturbances on the system, which can highly improved the performance of the PMSM servo system. In this paper, ADRC is used as a speed loop regulator in the industrial sewing machine servo control system[7,8]. https://vssewingmachine.in/ https://vssewingmachine.in/ Simulation results indicated that, compared with conventional PID control servo system, the proposed control method has better dynamic performance, and stronger robustness to the system disturbance.  

In this paper, servo control of the industrial sewing machine system based on the ADRC is proposed. Compared with the conventional PID control, it can estimate and compensate the uncertain load torque disturbance caused by the external and internal of the system, which can highly enhance the robustness of the system. Simulation results have proved the effectiveness and feasibility of the ADRC, and the control system has better dynamic performance and robustness. 

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A fuzzy model - VS Sewing Machines

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A fuzzy model reference adaptive controller for regulating the fabric’s tensional forces applied by a robot during the sewing task is developed. The adaptive fuzzy logic controller closes the loop outside the internal scara robot controller. The robot guides a piece of fabric in a conventional industrial sewing machine while its controller maintains a desired constant tensional force applied to the fabric during the sewing process. In each loop, the proposed controller calculates the appropriate robot end-effector displacement. A force sensor mounted on the wrist of the robot manipulator measures the actual force applied to the fabric, and the formulated force error is used in order to adapt the controllers’ parameters. The performance of the controller is investigated experimentally and the results show the effectiveness of the FMRL approach and its wide range of applications.

The robotic sewing system is shown in Fig.1 . A piece of fabric is gripped by a robot manipulator holding the right edge while the opposite left edge of the fabric is moving with unknown velocity by the feed dog mechanism of the sewing machine. During the sewing process the fabric should be kept taut so as to prevent the buckling and ensure qualitative stitches. The desired tensional forces for each fabric are depended on the fabric type and properties [1]. The aim of the presented approach is to develop a flexible controller that could be able to guide a wide range of fabric types without knowing the properties or the model of each fabric. Brother sewing machine showroom in chennai The target of the proposed controller is to apply a desired constant tensional force to the fabric in the begging and during the sewing process, independently of the sewing machine velocity. The force sensor mounted on the wrist of the robot manipulator is used to measure the force applied to the fabric which is the only feedback signal in the outer control loop relative to the internal controller of the robot. The tensile stiffness coefficient of the fabric is non-linear and changes by decreasing the nominal length as the sewing proceeds.

In this paper, an effort to control the tensional force applied to the fabric during the robotized sewing process is presented. An FMRL controller is designed, implemented and evaluated. The application of such a controller in an area with model uncertainties and very noisy signals shows the wide range of this approach. Despite the satisfactory results of the controller, further investigation is needed in order to limit the  oscillations of the controller’s responses. https://vssewingmachine.in/ For future elaboration, a force controller that can determine the position and the velocity of the robot is planned.

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Image Processing Pipeline in Sewing - VS Enterprises

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An image processing pipeline was designed for the thread detection. The complete workflow is visualized in Figure 3. At all times, knowledge about the desired thread pattern model is available.

A. Two Partial Luminance Images The composed camera RGB-image is converted to a singlechannel luminance image L(x, y) = 0.3∗R(x, y)+ 0.59∗G(x, y)+ 0.11∗B(x, y). (1) The conversion is based on the assumption, that the thread appears either brighter or darker than the tissue background. However, it is unknown in advance which appearance is given. Therefore, two partial images are generated. The positive image I+(x, y) only contains pixels brighter than the tissue mean, whereas the negative image I−(x, y) only contains pixels darker than the mean, I+(x, y) = max(0, L(x, y) − m) (2) I−(x, y) = max(0, m − L(x, y)) (3) m = mean(L(x, y)). (4)

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By separation into partial images, the pixels representing the thread will only be visible in one of them. They will always appear as a bright structure. Next to the actual thread pixels, there will also appear spurious pixels from noisy tissue structures. They resemble thread-like structure parts and are stochastically distributed.

B. Frangi Filtering A Frangi filter [3] is applied on both partial images. The filter operation basically consists of a pixel-wise computation of the Hessian-Matrix and a Gaussian-shaped smoothing kernel. It is also known as a vesselness filter and was originally introduced in the context of medical image analysis to emphasize pixels that are embedded in vessel-like structures. However, an elongated and thin appearance is not only characteristic for vessels inside the human body but also for the considered thread within this work. It is therefore natural to adopt the established methodology for the given task. The result are two images, IF r+ and IF r−, with each pixel value containing the probability that it is embedded in a thread-like structure. Best merit sewing machine in velachery

C. Selection of the Thread Image Based on the supplied model prior, an approximate number of expected thread pixels can be estimated. This expectation can be turned into a thresholding operation, that is performed on both images. Since one of the filtered images contains both the thread as well as background noise, while the other image only contains background noise, a robust detection of the thread image is straight forward. The result is a single binary image, wF r(x, y), with a pixel value of 1 indicating a thread pixel.

D. Classification of Pure Thread Pixels The previous step provides a mask for pixels that are embedded in a thread-like, i.e. elongated and thin, structure. Yet, pixels lying not directly on the thread but nearby may be included. Therefore, the mask is refined using an expectation maximization (EM) algorithm [1]. The refinement is no longer performed on the luminance image, but on the RGB-image. As initialization, the tissue RGB values from the background removal step are taken for the tissue mean and covariance values. The thread mean and covariance values are derived from all pixels masked by wF r. The iterative EM algorithm results in a single binary image, wges(x, y), with a pixel value of 1 denoting a pure thread pixel. 

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Monitoring and Control of Industrial Sewing Machines - VS Sewing Machines

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Processing textile materials is generally very difficult due to the flexible nature of the material. In industries using sewing as assembly process, most processes rely on human labor, being difficult or even impossible to automate. The relations between machine configuration and adjustment, material properties, and the resulting product quality are also complex. This paper describes current work using an instrumented lockstitch sewing machine to study the dynamics and variations of one of the important process parameters during high-speed sewing of shirts: thread tensions. The objective is study the principles that may allow for an automatic setting of the machines, quality control and for real-time process control. It has been found that differences in material properties result in measurable features of the thread tension signals acquired. 

The processing of textile products by sewing them together is a very complicated process. This may not be apparent at first glance, but a closer look at the process reveals that, due to the flexible, often extensible nature of the materials, their handling is a procedure that in almost all cases requires human hand. Another important aspect is setting the machines for the great variety of materials used currently. This can only be accomplished by experienced sewing technicians. Machine configuration and adjustment is an empirical, time-consuming process that is more and more significant considering that textile industry has been constantly moving away from massproduction to small orders with varying materials and styles. Machines should be able to set themselves up when the data regarding material properties and desired process parameters is known. During the process, it would be ideal if they could adapt themselves and detect defects or malfunction automatically. This would reduce set-up times, increase flexibility of the machines and increase product quality and process reliability, avoiding defects and rejected products. Research in this direction has been carried out by several investigators, such as Clapp [1], who studied the interface between the machine and the material feeding system, Stylios [2] who proposed the principles of intelligent sewing machines, amongst others. Within our team, previous work has been carried out on thread tensions, material feeding and needle penetration forces in overlock machines [3-5]. Other studies targeted needle and bobbin thread tension measurement on lockstitch machines [8-10]. The sewing process is a cyclic process in which several occurrences take place.

Sewing machine shops in chennai The objective is to interlace thread(s) with each other and through a fabric, for the purpose of joining, finishing, protecting or decorating. Three main “sub”-processes can be identified that ideally should be monitored and/or controlled automatically: -Material feeding. Seams are produced on the fabric with a certain pattern, which is, in the simplest case, a straight line, but may also be a complicated form such as the ones used in embroidery operations. To form these patterns, the material has to be transported-“fed” by a distance that is called the stitch length. Given that industrial machines operate at very high speeds (some of them attaining 10 000 stitches per minute), the dynamics involved is complex and there are very often problems with material deformation and irregular stitch length. Some of these aspects have been addressed in [1-3, 5]; -Needle penetration. Considering again the high sewing speeds that occur, problems with needle penetration can arise due to the mechanical and thermal interaction between needle and fabric. Fabric yarns may be torn by the forces acting during needle penetration or they may fuse due to the high needle penetration produced by friction. Systems to monitor needle penetration forces during the process to detect defects and offline systems to support the choice of needles and fine-tune fabric structures and finishing to avoid these problems, would be of high value to the industry. This kind of approach has been studies by several authors, such as in [4-8].  

-Stitch formation/Thread tensions. The interlacing of the threads itself, which constitutes the actual stitch formation, cannot be dissociated from the processes of material feeding and needle penetration. However, there are two variables directly linked to the thread that most intimately represent it: Thread tensions and thread consumption. The relationships between fabrics, machine set-up and stitch formation in lockstitch machines have already been studied in [9-15]. Methods for defect detection have been developed for overlock machines and presented in [3]. However, an automatic system for setting thread tensions online is still missing. Wang and Ma [15] describe thread tension control in embroidery machines, but the work only tackles the issues associated to the control of the actuator. Setting of the correct references for the controllers to produce a high-quality product in varying conditions is the key issue, and this has to be further tackled. This paper describes current work on the behavior of thread tensions in an industrial lockstitch sewing machine using a new measurement set-up. https://vssewingmachine.in/ Methods previously investigated for monitoring of thread tensions and establishing the correct variable references are being ported and/or re-evaluated. The first step is the study of the relations between material properties and thread tensions. Some aspects are still not clear in this regard. In [13], for instance, the authors state that the thickness of fabric plies does not affect the needle thread tension. 

An industrial PFAFF 1183 lockstitch (stitch 301 according to ISO 4915) machine (Fig.1) has been instrumented with a thread tension sensor (Fig.2) connected to a signal conditioning circuit which in turn plugs to a National Instruments PCI-MIO16E-1 data acquisition board (although often called thread tension, the parameter measured is actually a thread pulling force). The machine’s “synchronizer” (a rotary optical encoder) provides 512 pulses per rotation of the machine, which is used as sample clock for signal acquisition. It is thus possible to determine the exact angle at which each signal sample is acquired, allowing relating the signal directly with the events during the stitch cycle. Signals are thus represented on a continuous angle rather than a time scale, in which the rotation N of the machine corresponds to the angles between 360º·(N-1) and 360º·N. The sensor (custom-designed by Petr Skop) is a cantilever beam with semiconductor strain gauges at the base, configured as a complete Wheatstone bridge. A glass sphere with a rounded slot allows a low-friction interface with the sewing thread. A thread guide with two ceramic O-rings has been designed to guide the thread around the thread sensor. The thread pulling force produces deformation on the cantilever sensor that is picked up by the strain gauges. Thread tension is imposed to sewing threads by a device called a tensioner (partially visible in Fig.2). This device consists of two disks between which the thread passes. A spring holds the two disks together. The pre-tension of this spring can be adjusted and is called in this context static thread tension.  

A software application has been developed in Labview allowing the acquisition and processing of the resulting signals. The signal processing functions of this software have been reported elsewhere [3]. The most important one is splitting the thread tension signals into stitch cycles (each cycle corresponding to one rotation of the machine’s main shaft) and in turn dividing each stitch cycle into phases, which are associated to specific events of stitch formation. For each one of these phases, that will be described later, features such as peak values, power, energy or average of the signal is computed. In the current experimental work, thread force waveforms throughout the stitch cycle are being analysed when varying parameters such as static thread tension adjustment, number of fabric layers, mass per unit area and thickness of fabric, needle size and sewing speed. Both the effect of the machine settings and process variables on the thread tensions, as well as the effect of the material properties are investigated. In this paper, the effect of static thread tension and the influence of the fabric on the dynamic tension signals are analysed. The first step was to observe the resulting thread tension signals and interpret their relation to the stitch formation process. Some trials with the adjustment of the needle thread pre-tensions were made. Afterwards, a more comprehensive experiment was set up to investigate on the influence of the material being sewn.

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Needle eye temperature measurement at different speeds of sewing - VS Sewing Machines

Brother sewing machine showroom in chennai - VS Enterprises

In this article two methods are used to measure the temperature of needle eye during the high speed of sewing and the results are compared for calculating the precision in the measurement,Needle heatup is a big issue for apparel industry especially for automobile industry seat covers where a lot of synthetic materials are used which get damaged by needle heat at high speed of sewing machine,in this article thermocamera and thermocouple are used to calculate the precise temperature of needle eye and then 100% polyester 35x3 Tex thread is taken for sewing,the lockstitch sewing machine is run at 1000rpm,2000rpm and 3000rpm respectively and the both methods are used to obtain the accurate needle temperature and finally thread tensile properties the article is very helpful in predicting the exact temperature of needle in lock stitch machine at different stages of sewing process. 

Keywords—needle eye heat,needle temperature of sewing machine 

Introduction: Industrial sewing is one of the most commonly used manufacturing operations. Its applications can be found in the manufacturing of garments, shoes, furniture, and automobiles, just to name a few. Every day, millions of products, ranging from shirts to automotive airbags, are sewn. Hence, even a small improvement may result in significant corporate benefits. In heavy industry sewing, such as sewing of automobile seat cushions, backs and airbags. It requires not only high productivity, but also high sewing quality (i.e. good appearance and long-lasting stitches). Typically, the material being sewn includes single and multiple plies of synthetic fabric or leather, and sometimes backed with plastics. These materials are much more difficult to sew compared to ordinary sewing applications. In recent years, in order to increase the sewing productivity, high speed sewing has been used extensively. Currently, sewing speeds range from 2,000∼6,000rpm. In heavy industrial sewing, typical sewing speeds range from 1,000∼3,000rpm., needle heating due to the friction between the needle and the fabric is severe. The friction generates heat, part of which is absorbed by the fabric and part by the needle. The heat absorbed by the fabric is spread out along the seam, but the heat absorbed by the needle accumulates. Depending on sewing conditions the maximum needle temperatures range from 100°C∼300°C. This high temperature weakens the thread, since thread tensile strength is a function of temperature [1]. and infrared pyrometer[1,2] These experimental methods are accurate and reliable [2,3]. However, they have a major limitation: they are expensive (e.g. an IR radiometry measurement device costs thousands of dollars) and time-consuming (in order to cover various sewing conditions). Hence, it is inconvenient for shop floor applications.  Brother sewing machine showroom in chennai

Method 

To compare the needle eye temperature by thermocamera and thermocouple following equipments were used Following machines and instruments were used for this experiments 1-Lock stitch machine (Brother, DB2 – B755 – 403A) 2-35x3Tex 100%polyester thread (Hagal,Unipoly)Z twisted TPM=640 3-Thermocople by Omega (TJ36-CAIN-010U-6) 4-thermocouple end wireless device and receiver (MWTC-DK-868) 5-needles(Groz-Becker 90/14) 6-Thermocamera(TMS60) 7-denim fabric (257GSM ,2/1 twill) 8-Fan to cool needle[12Volt DC Fan from ADDA company (Model:AD0912UX-A7BGL) 

Experimental Part 

Temperaure 26 o C and RH 60% The (TJ36-CAIN-010U-6) thermocouple was inserted inside the groove of the needle and attached ,the lock stitch machine was set at 1000,2000 and then 3000 rpm,two layer basic 2/1 twill denim was stitched by 35x3 Tex 100%polyester thread(commonly used for denim stitching) for continous 90 seconds and temperature was recorded for every second by the thermocouple for each speed of the machine,Then thermocamera was adjusted for emmissivity of the needle and found out to be 0.08 for chromium needle at 38 o C and then tested it with callibrated temperature of the needle and found it correct ,the camera was placed 40cm away from the needle and again the machine made to stitch at different speeds and thermocamera took reading at every 10seconds for continous stitching of 90 seconds,The process was repeated for 5 times for both thermocouple and thermocamera. https://vssewingmachine.in/ Results The Fig 1 shows the needle and the size of thermocouple and the groove or needle. 

Measurement by thermocouple is not non-contact way of measuring the needle temperature and filling the needle groove with thermocouple changes its thermal properties but the Thermocouple shows more precise every second meaurement of temperature, ,whereas the thermocamera measurements are with more deviation but still the observations are near to the temperature measured by the thermocouple, cooling of needle by external means can reduce the needle temperature at the stitching process.but the still use of fan reduce the temperature of needle to some extent and only a reduction of 15% was noted in the temperature of needle with the installation of fan,which reduces the temperature conventionally by flowing air to the needle,the thread is damages continously as shown by the SEM pictures by the friction at the needle eye and needle temperatureThis is part of bigger research eventually the impact on sewing thread and frictional forces are claculated.  

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