Thursday, June 20, 2019
Sewing Machine combined by camera
The textile specimen we consider
have a size of 1m x 1m. Simultaneously, the spatial accuracy of detected stitch
positions needs to be in the order of 50µm to account for very thin threads. In
a real world scenario, both requirements can only be combined by using a
commercially available camera sequentially scanning individual parts of the
specimen. Afterwards, all of the acquired tiles are composed to obtain a
unified, large RGB image, C(x, y) = (R(x, y), G(x, y), B(x, y))T , where (x, y)
denotes the pixel position. Figure 2 shows the real world system for image
acquisition and quality inspection. A camera is mounted on a gantry robot,
allowing to automatically translate the camera in 3D space. The specimen to
inspect is placed on the floor below. The camera has a sensor size of 2332 x
1752 pixels. Considering the employed lens, this translates to a resolution of
8 pix mm . However, the pixel resolution can be dynamically changed by decreasing
or increasing the camera height over the conveyor plate using the gantry robot.
Once the measurement has been started, the specimen is scanned in equidistant
intervals resulting in a set of tile images which need to be composed. The
image composition is performed using standard image registration techniques
[8].
Mandatory Microcontroller
Microcontroller is a mandatory
requirement because we are using MAX RS485 for master slave purpose; wireless
regenerative transmitter and receiver as well as data also need to save. All
those reasons, we shifted towards the microcontroller. Moreover, our circuit
area is also reduced. We can use delay using microcontroller which is more
reliable.
A. Slave Working Main supply to
comparator circuit is same which elaborate above. Comparator output goes to
microcontroller pin. Controller continually checks the P0.4 pin. When the
output of the comparator will high, then the controller will give signal to
P1.6 pin at the base of the transistor 2222 switch. Transistor gives the signal
to 5V relay and relay will turn on the light. When the lights turn ON, it means
worker start working and starting status is sent to master using MAX485.
Whenever the machine turns off, the output of comparator gets low. But
controller continually gives a signal to relay according to delay. Delay is
adjusted according to desire. When delay finishes, the controller checks the
status of the machine and if the machine still not operating then relay signal
get low and light will turn off. When the lights turn off then controller again
send a signal towards master using RS485.
B. Master Working Master requests
the machine status from slaves after every adjustable time. Slaves send its
status to respective master through serial communication MAX485. Master saves
machine status and its time using RTC (DS307). Then every master sends data
towards the single master on the requested time. Single master sends data to
receiver end using R.F (super regenerative module) transmitter and receiver,
Data receives through R.F receiver and send data to computer through USB
terminal and saves data to excel sheet. If single master didn’t respond then
master save data into its memory then transmit upon ask, similarly, if the
receiver doesn't respond, then the master will save data into its memory.
Receiver circuit receives the complete working
status of masters through single master. For beginning the transmission, master
send the signal to communicate, and then receiver reply back to master that you
can communicate, then receiver receive machine status through R.F receiver and
sends it to computer USB terminal through max232 [15]. If the computer doesn’t
respond, then receiver saves data into its memory. When the system responds,
then the data send to computer through USB terminal and remove data from its
memory. Data will save into excel sheet using the front-end design application. juki usha singer merritt sewing machine price list showroom in chennai
Application part is the interface between
master controller and receiver controller with a computer. It is software which
saves routine data of machines in a text or excel file. Excel File will contain
all machines start time and working hours in a day. One sheet of excel file
will contain complete one-month data of nth machines. One excels file will
contain twelve months of data. The application software will appear in Quick
lunch. Date and time will update through RTC automatically. When the next day
comes, curser will move automatically to next tab. Similarly, when next month
comes data will save into the next sheet automatically. Communication starts
when receiver reply, then master sends the machine data. If the receiver not
reply may be its power off or receiver not responding due to some problem
that’s why master saves data to memory and when receiver replays then master
send it to receiver. Program is running behind the front-end application part.
First time application needs to install. After installing application icon
appears at Quick launch. Application continually checks the USB terminal and
when the data come its save into excel file. Excel file contains the working
time of workers. Rows contain worker number and columns contain the worker
time. One sheet contains one day working data in excel file. When next day
comes data will save into next column. When next month will come, data will
into the next sheet of excel file. Data Rate of transmission is slow, it can
increase using high baud rate transmitter and receiver. Application part is
compatible for windows XP. It can be improved for other windows and operating
systems.
After getting results, now the complete sewing
machine hall station lights and optional fan will automatically turn ON when
machine starts. When workers are not working according to settable time, then
lights will turn off automatically. Master will save the working time of the
machine. Master sends the machine working time to the receiver then the
receiver sends it to the computer through RS232 USB terminal, application
program running there.
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Data Communication in Sewing Technology
Unlike master textile, there is
various energy saving opportunities that exist in every textile plant in a
various way. The main purport is to save the overall unit cost for better
profit margins. Overall electricity consumption and electricity expenditure is
one of the main considerations during costing. We can’t share exact design for
the industrial patient issue of industry. Manual switching control system
installed for the sewing machine but the intermittency is that it’s depend upon
the worker, moreover, it’s lagging the monitoring system.
The slave is connected with
machine and station light. Whenever the worker starts the machine, slave senses
the machine current and turns ON the light and optional fan for a specific
period of a time. ON light status is adjusted through programming. Master
slaves communicate through RS485. The entire master’s takes data from all the
connected slaves and sends it to the single master, afterword single send data
to receiver using a wireless transmitter. A receiver receives the data and
sends it toward the managerial computer. Data gets processed at the receiver
end which sends it to the computer through RS232. If the system is turned off,
then data will be saved in EEPROM. The data gets transmitted, when the computer
turns ON.
Data communication for complete
system is shown in Fig. 2. Master asks for data from Master 1 to Master 13, etc.
among the nth numbers of slaves. After five minutes, master asks to send data
to receiver, receiver replies with D it’s means receiver is ready to accept
data, afterword transmitter send data to a receiver through the R.F.
transmitter and receiver send to towards computer and save it and utilize
whenever it needed.
The basic principle of the
circuit is comparing the current of machine with supply line current by
converting into voltage using lm324 voltage comparator.
When machine starts working then
flux around the supply line change, this change of flux induces some amount of
voltage certainly detection of current give signal to relay to operate turn ON
the light [11, 12] usha 8801e bruce x5 bruce q5 singer silver girl sewing machine
Pros and cons of this current
sensing mechanism
·
There is no wired issue.
·
There is no sensor which can hurdle into work.
·
There is no sensing area issue.
·
There is no cost issue.
·
Easily to install.
·
Analog components will be used, which efficiency
decreases with time.
Module Design Sewing machine draw
a 0.8-amp (tested value) current on working state. So, we compare the current
of the machine with predefine value (0.8A). One leg of C.T connects with supply
and the other one with machine supply. Machines current flow by pushing the
feet on paddle, current is sensed through C.T. Some amount of voltage induces,
phenomenon explains above. Our circuit working in D.C level so we rectify the
current using diode and capacitor. We will do half wave rectification because
using half wave rectifier voltage drop across the diode is 0.7V and if we use full
wave rectifier drop across the diode will be 1.4V. If that is so, voltage drops
across diode increases due to it sensing voltage can’t detect. The reason is
that our current sensor is current transformer and voltage across C.T. is very
low, where very low voltages produced. The measured voltage across the diode is
0.32V at 100Watt load. Polar capacitor connects parallel to diode and C.T for
improving transient response. Resistor connects parallel to capacitor for
improving discharging time of capacitor and its make R.C circuit. We can change
the time using variable resistor. We used lm324 comparator for voltage
comparison. We adjust the 0.25 voltage at inverting input pin 2 using voltage
divider rules for comparison. Sensor (RC circuit) output will go to lm324 pin 3
which would be 0.45 volt. If the value of voltage below adjustable voltages the
output will be zero otherwise one. When sensor output high it will trip the
relay and relay will turn ON the light. But the problem is that it will be for
momentary. So, we use another comparator. The output of one comparator will go
to noninverting input (pin 5) through RC circuit and the diode. Resistor and
capacitor connect parallel with diode and pin 5 of lm324. A resistor is used
for increasing the discharging time of the capacitor and diode use for
resisting back flow of current. When output of one comparator is high the
capacitor will charge and the diode will resist to slow discharge or back flow
of current. Second comparator output will connect to relay and relay will be
higher until the capacitor discharge. We can adjust the timing using RC
circuit.
Textile industry in neighboring country
The textile industry is one of
the major and largest manufacturing industries in Pakistan. Pakistan is the 8th
largest exporter of textile commodities in Asia. This sector contributes 8.5%
to the GDP [1]. In addition, the sector employs about 45% of the total labor
force in the country, which includes the 38% of the manufacturing workers [2].
Moreover, Pakistan is the 4th largest producer of cotton with the third largest
spinning capacity in Asia after China and India and contributes 5% to the
global spinning capacity [3]. At present, there are 1,221 ginning units, 442
spinning units as well as 124 large spinning units and 425 small units which
produce textile [4]. best sewing machine dealers in chennai. Master textile is one of the foremost and leading textile
manufacturers, based in Lahore, Pakistan that is engaged in the export of
quality products such as yarns, garments and fibers. They established a name of
credentials owing to the project commitments, working speed, quality practices
and the overall approach of the company [5].
Most of the textile mills have a
fixed asset base of USD 20.00 million, annual turnover of USD 60.00 million and
2500 employees [5]. These textile mills are facing energy wastage and shortage
problem in their garment section [6]. One of the reason is that sewing machines
are arranged in columns and all station lights continuously are kept ON, even
if just one worker is working and more over they didn’t able to monitor worker
how much they efficiently work. That’s why they want energy efficient control
and monitoring system for their machines [7]. Cost must be considered in that
system [7, 8]. The design proposed in the paper has two major subparts. The
first part involves controlling the station light by knowing the presence of
workers on the machine which shown their working status and performance. The
second part involves sending data towards manager room and then extracting the
data into the excel format. For achieving this target, various different
sensing mechanisms have been tried, but most of them are not working efficiently
and properly, which include Passive infrared (PIR) sensor, R.F. sensor,
ultrasonic sensor etc. [9, 10]. When other conventional sensors were failed to
deliver the results then current sensing mechanism is designed which works on
the sewing machine’s working status through using flux of the machine. On the
other hand, monitoring and sending data towards the manager room is the real
challenge because there were four hundred machines need to synchronize in
addition the data need to send wirelessly. For synchronizing the machine there
is thirty-one masters is made, each master connected to thirty slaves and each
master link to single master, which communicate all the data towards the
manager. Although the data can be transmitted through various ways such as
ZigBee device, wireless super regeneration transmission module, etc., but super
regenerative wireless transmission module is opted for transmission of data.
Solemnly aim of this paper is to present the model design for sewing machine
which ultimately helps in energy saving by turning off the redundant tube
lights. As per our subject industry, master textile, there are four hundred
sewing machines present there and hundreds of stations control through single
breaker. Controlling the station light can save tremendous amount of energy
because most of the stations used are in idle condition and station lights are
turned ON with no reason.
Finding on fabric layers/material
Preliminary assumption testing was conducted to check for normality, linearity, univariate and multivariate outliers, homogeneity of variance-covariance matrices, and multicollinearity, with no serious violations noted. The results of the MANOVA analysis include the F-statistic value, average (M), standard deviation (SD), Wilks’ Lambda, significance level p and partial eta squared. Wilks’ Lambda is one of the most reported statistics. If the associated significance level p is less than 0.05, then it can be concluded that there is a significant difference between groups. Partial eta squared, also known as effect size, shows the proportion of the variance in the dependent variable than can be explained by the independent variable. The guidelines proposed by Cohen [17] have been used in this work: 0.01=small effect, 0.06=moderate effect, 0.14=large effect. When the results for the dependent variables were considered separately, a Bonferroni adjusted alpha level of 0.017 was used. In this case, a significance level p smaller than 0.017 represents a significant difference.
A sewing test rig based on a lockstitch machine has been set up using software previously developed and used in the assessment of the operation of overlock machines. A comprehensive experiment involving materials, adjustments and needle choice is being carried out, of which some results have been shown. The relations between sewing variables are complex and experimentation is extensive due to the number of parameters involved. However, some insights are being gained that promise methods for automatic adjustment and defect detection. These are very desirable functionality for today’s textile industry, dealing with small production orders with varying materials or for technical textiles, where a more quantitative process monitoring can assure more reliable and safe products. It has been shown that the number of plies / material thickness produces statistically significant variations in thread force peaks, as expected. However, the variations are not always as expected. Further experimentation relating the static thread tension adjustment, the materials and the resulting thread forces, related to the evaluation of the seams by experienced sewing technicians and objective tests such as seam strength will shed some light on the principles of correct adjustment. This will allow the development of systems to automatically adapt the machine to new materials and to monitor the process avoiding defects and low quality seams. Other process variables such as needle penetration force and thread consumption will be included in this analysis in future work.
Software App Embedded in Sewing Machines
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. best sewing machine dealers in chennai. 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.
Three similar shirt fabrics with
different mass per unit area were used, namely
• Fabric 1 : 1x1 plain weave ;
100% cotton; 102 g/m2; thickness 0,22 mm
• Fabric 2 : 2x1 twill fabric;
100% cotton; 127 g/m2; thickness 0,23 mm
• Fabric 3 : Mixed structure;
100% cotton; 118 g/m2; thickness 0,23 mm
The machine was set-up as
following:
• Groz-Beckert 134 needle with
round point and size 8;
• 100% corespun polyester thread
with ticket number 120;
• Constant sewing speed of 2000
stitches per minute;
• Stitch length 3,5 mm
• Static thread tensions were
adjusted empirically for the fabric with average weight; no difference in
stitch alance and tightness could be observed sewing the three fabrics with
this adjustment. Adjustment was maintained unchanged throughout the experiment.
For each fabric, strips of fabric
of 10 cm width and 30 cm length were cut. Specimens with two and four layers of
these strips were prepared. On each one of them, 10 seams with 20 stitches each
were performed.
Peak values for each of the three
defined stitch cycle phases (see next section) were extracted by the developed
software. Results were compared between specimens of two and four layers. For
this purpose, the Statistical Package for the Social Sciences (SPSS 20.0) was
used. For each experiment a MANOVA (one-way between-groups multivariate
analysis of variance) was performed. Three dependent values were used: Peak
values of thread force in phase 1, 2 and 3. The independent variable was the
number of layers: 2 or 4. The analysis was carried out following the
recommendations of Pallant [16].
Preliminary assumption testing
was conducted to check for normality, linearity, univariate and multivariate
outliers, homogeneity of variance-covariance matrices, and multicollinearity,
with no serious violations noted. The results of the MANOVA analysis include
the F-statistic value, average (M), standard deviation (SD), Wilks’ Lambda,
significance level p and partial eta squared. Wilks’ Lambda is one of the most
reported statistics. If the associated significance level p is less than 0.05,
then it can be concluded that there is a significant difference between groups.
Partial eta squared, also known as effect size, shows the proportion of the
variance in the dependent variable than can be explained by the independent
variable. The guidelines proposed by Cohen [17] have been used in this work:
0.01=small effect, 0.06=moderate effect, 0.14=large effect. When the results
for the dependent variables were considered separately, a Bonferroni adjusted
alpha level of 0.017 was used. In this case, a significance level p smaller
than 0.017 represents a significant difference.
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]. best sewing machine dealers in chennai. 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. 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. This is
one of the aspects to be studied in this work.
An industrial PFAFF 1183
lockstitch (stitch 301 according to ISO 4915) machine 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.
Controlling Industrial Sewing Machine
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. juki usha singer merritt sewing machine price list showroom in chennai
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. 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].
Textile Stream Smart Manufacturing Innovation
He smart factory is a futuristic
production paradigm that transforms ICT(Information and communication
technology) into a new smart/green/urban production system by integrating the
existing traditional industrial production system.[1-2] Industry 4.0 proposed
by DFKI, is defined as the 4th industrial revolution based on
Internet-of-Things(IoT), cyber-physical systems(CPS), and
Internet-of-Services(IoS). [3-6] In the textile industry, the smart factory is
a factory based on the CPS that incorporates ICT and IoT technology into the
existing production system.[7-8] In order to build a smart factory between
textile and apparel streams, the connectivity of the CPS should be
strengthened. usha 8801e bruce x5 bruce q5 singer silver girl sewing machine
This study focuses on the
construction of a CPS system to realize a smart factory by deriving three
representative processes (fabric, dyeing, sewing) among textile streams. The
data flow of CPS based inter-stream smart manufacturing system. The rectangle
marked with read lines represents the part for detecting and controlling the
sewer data for the smart of the sewing process which is the core of this
research.
Textile stream smart factory CPS
implementation can only be done by linking together the ordering system, design
automation system, product information management system, production
information integration system and production equipment automation.[6] The
interlinkage of high-throughput, high-productivity production systems that
minimize plant-to-plant collaboration and prototype production to accommodate
small-volume and multi-stream requirements between streams, and can be
instantly produced on demand.
SEWING MACHINE SENSING DEVICE DEVELOPMENT FOR SMART FACTORY
A.
Device for checking and indicating the rest of underthread sewing yarn of
sewing machine The sewing work can work in a situation where there is no
under-thread by mistake. This leads to defective products and economic losses.
To solve this problem, there is a need for a device for detecting the remaining
amount of under-thread and transmitting it to an operator. The sensing signal
configuration for system design to detect the residual under-thread amount and
the system configuration diagram to control it by linking it. The orange block
shows the status of the warning lights, the PLC, the touch screen, and the main
brake, while the blue block indicates each sensor and control signal for
control.
Software algorithms were designed to implement
the logic sequence so that if the under-thread is insufficient, the operation
stops immediately.
The configuration of under-thread residual
sensing and display system. Each component of the test apparatus for the
detection of the residual thread volume consists of the lower part of the
sewing machine and the display part. Under-thread residual sensing device was
designed and implemented as primary and secondary sensing parts. The primary
sensing uses a cylinder (CXSM630) for the bobbin and a SMAT-8M sensor for the
FESTO position transmitter.[9] The system is implemented so that the remaining
amount of the bottom thread can be calculated by the data that the cylinder pin
advances and senses the distance gap.
Secondary detection shows the sensing
principle to detect and warn the bobbin rotation state while reducing the
defect caused by the bobbin not rotating when the bobbin is tangled or
defective. To check the bobbin rotation status, the Omron NPN type photo sensor
(E3Z-LL61) checks the rotation of the bobbin with four pairs of black and white
stickers at 45 degrees on the bobbin.
B. Stitch control device and sewing thread
information detection system concept configuration In order to make smart
factory of sewing factory, it is necessary to prevent worker’s mistakes and to
record and confirm the current work. The position where the residual
under-thread detection device and the stitch automatic control device are to be
attached in the sewing machine being used in the sewing factory. In order to
automatically adjust the stitches, the information about the fabric currently
being worked on is entered in advance, so that the number of stitches can be
automatically adjusted.
Detailed data and method of monitoring system
at the upper left part of the figure are explained below. C. Information flow
of the sewing machine detected from sensors for smart sewing process the flow
of information obtained from the parts(under-thread residual detection device,
automatic stitch control device, monitoring system) developed for the sewing process
smartization. The collected information is displayed in the monitoring system,
and it is transmitted to the POP system, the PDM system and the final
customer-linked system, so that the sewing process can be made smart.[10-11]
It is a study to apply smart factory to the
textile industry in this research and development. A study on the smartization
of sewing process among several textile streams was conducted.[12-13] In order
to make the sewing machine smart, we applied the same sewing machine which is
used in the present industrial field and modified the sewing machine. First,
the residual amount of the under-thread was detected to reduce the worker’s
mistake and product defect. Secondly, in the sewing industry where workers are
aging, it is possible to control the automatic stitch number according to the
product type. Next, monitoring of the overall sewing process requires further
work on the presser foot pressure control, tension control, POP(point of
production) system and all monitoring data interlocks.
Sewing Process of Production Schedule in Smart Factory
Industrie 4.0, proposed by DFKI
[1], is defined as the 4th industrial revolution based on Internet-of-Things
(IoT) [2], cyber-physical systems (CPS) [3], and Internet-of-Services (IoS)
[4]. One of the characteristics of Industrie 4.0 is that it includes smart
factories capable of generating customized products for customers. One of the
important issues to implement a smart factory is to complete and deliver customized
products to customers within specified time. For this, we need an efficient
scheduling algorithm [5]. It becomes more and more sophisticated work to
validate a production schedule in factories. Simulation is a tool for
validating a production schedule and changing it if needed. For using
simulation, we need appropriate simulation models. In this paper, we propose a
sewing machine model for simulating sewing process. The proposed sewing machine
model includes sensing, sewing, forwarding and control functions as submodels.
Also, we propose a modeling tool that includes the proposed model. The proposed
modeling tool manages a model library that can be continuously extended for
sewing process simulation. Further, it can automatically generate and build source
codes for simulation models. Therefore, users can easily develop their own
models and simulate them. best sewing machine dealers in chennai
SIMULATION MODEL FOR SEWING PROCESS A. Motivation One of the
distinguishing features of smart factories compared to existing factories is
that the smart factories generate customized products. Other characteristics of
a smart factory are as follows [6-8]. 1) Each product has a unique ID. 2) Each
product passes a different sequence of processes until all required processes
are completed. 3) Products and facilities communicate with each other to
determine each product’s production schedule. Therefore, facilities in the
smart factories should be modeled differently than facilities in existing
factories.
B. Sewing machine model
(Structural model) We defined a sewing machine model for simulating sewing
machine processes. The sewing machine model was defined as a structural model
consisting of four component models (Sensor / Work / Forward / Control). Sensor
model detects raw material or semifinished products arrived at the sewing
machine. Work model performs sewing process for the arrived raw material or
semifinished products. Forward model chooses the next forwarding facility and
passes the processed semi-finished product on the selected next facility. Finally,
Control model governs the whole operations of the sewing machine.
C. Sensor model (Behavioral
model) Sensor model abstracts a sensor module that detects raw material or
semi-finished products arrived at the sewing machine. Sensor model has 4 phases
and moves from one phase to another whenever state transition occurs. The
Sensor model in Init phase stores its current location and moves to the Sensing
phase. In Sensing phase the Sensor model periodically checks whether
semi-finished products has been arrived at the sewing machine. If there is one,
it goes to Detected phase. Otherwise, it goes to Non-detected phase. In
Detected phase the Sensor model outputs the information of arrived product
through the port Out_Sensor_Detection and then returns to the Sensing phase. In
Non-detected phase it returns to the Sensing phase after a predefined time.
D. Work model (Behavioral model)
Work model represents the sewing operation and changes the properties of an
arrived product. Work model has 3 possible phases (Idle, Working, Reporting).
In Idle phase, it waits for a product to arrive at the sewing machine. When an
input is arrived through the port In_Work_Command, the Work model moves to the
Working phase. It changes the properties of the arrived product in Working
phase and outputs the work result through the port Out_Work_Report.
E. Forward model (Behavioral
model) Forward model implements a variable process of a smart factory by
choosing the next forwarding facility for a semifinished product and delivers
the product to the selected facility. It waits until the sewing operation ends
in Idle phase. When an input is arrived through the port In_Forward_Command,
the Forward model goes to the SelectNext phase. In the SelectNext phase it
chooses the next forwarding facility based on the workload of candidate
facilities and then goes to the Forwarding phase. The Forwarding model pass the
semi-finished product to the selected facility and moves to the Report phase.
Finally, it generates the output through the port Out_Forward_Report.
F. Control model (Behavioral
model) Control model manages the other submodels of the Sewing machine model.
Control model has 4 possible phases (Sensing, Working, Forwarding, Logging)
that correspond to an operation cycle (detection, sewing, sending, and
recording) of a sewing machine in a smart factory environment. Control model in
Sensing phase waits for an input from Sensor model and goes to the Working
phase when it receives arrived product information through the port
In_Control_Detection. In Working phase it sends a work command to the Work
model through the port Out_Control_WorkCommand. When the Control model receives
the forwarding result from the Forward model, it goes to the Logging phase. In
Logging phase, the Control model records the processing result and returns to
the Sensing phase.
MODELING TOOL FOR DEFINING SIMULATION MODELS We have implemented a
modeling tool that manages a model library including the described Sewing
machine model. A user can easily add models such as sewing machine to a
simulation scenario by using the proposed modeling tool. Further, the modeling
tool automatically generate and build source codes for the models to be
executed by the simulator. Therefore, the modeling tool support addition,
deletion, modification and reuse of simulation models in the model library.
We should complete and deliver
personalized products to customers within specified time to implement smart
factories. Whether we can complete the customized products in specified time
depends on the production schedule used. Simulation can work as a tool for
validating production schedule and changing it if needed. In the manuscript we
define a Sewing machine model organizing a sewing process and a modeling tool.
The proposed model and modeling tool can be continuously improved and extended.
Wednesday, June 19, 2019
Sewing Machine Industry
Textile Industry
VS Sewing Machine
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