Simulation Modeling of Sewing Process
Industries 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.VS enterprises
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. Figure 3 shows the state
transition diagram of the Work 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.
III. MODELING TOOL FOR DEFINING SIMULATION MODELS
We have implemented a modeling tool that manages a
model library including the described Sewing machine model
[9]. Figure 6 shows a simulation scenario organized by using
the proposed modeling tool.
Figure 6 Simulation scenario by using modeling tool
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.
IV. CONCLUSION
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.https://www.vssewingmachine.in/
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