Simulation Modeling of Sewing Process for Evaluation of Production Schedule in Smart Factory

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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.


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. 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.

Forward model consists of 4 possible phases (Idle, SelectNext, Forwarding, Reporting). 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.


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.

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.