--- title: "5" source: "5.docx" converted: "2026-10-01 10:26:07 +0800" converter: "MarkForge (mdconvert)" engine: "MarkItDown" --- Factory Simulation Construction Method and Implementation of Intelligent Manufacturing Zheng Bin1, Bai Yan[[1]](#footnote-1)\*, Yang Soo Siang3, Tan Ming Keng4, Zhang Jing Song5 1,3,4,5Faculty of Engineering, University Malaysia Sabah, Kota Kinabalu, Sabah,88400, Malaysia. 2\*Faculty of Mechanical Engineering, Beihua University, Jilin, 132013, China. | | | | | --- | --- | --- | | ARTICLE INFO | | ABSTRACT | | *Article history:* Received 28 March 2025 Revised 2 May 2025 Accepted 7 May 2025 Online first Published 1 September 2025 | | Construction methods and implementation of factory simulation of intelligent manufacturing discusses the key construction methods and realization ways of factory simulation in the field of intelligent manufacturing. As the core of the digital twin industry, it plays an important role in optimizing production and improving resource utilization. This paper expounds the construction method of factory simulation, including digital modeling, simulation parameter setting, advanced algorithm application and real-time data fusion. At the same time, it points out the challenges of model complexity and data accuracy in the simulation construction process and proposes corresponding solutions. This paper also takes Plant Simulation software as an example to analyze the implementation steps of factory simulation, such as model building, parameter configuration and process planning, emphasizing its value in optimizing factory layout, improving production process, and enhancing the scientific nature of decision-making. This paper comprehensively shows the key technical points and future development trend of factory simulation and provides theoretical support and practical guidance for the technological progress and industrial upgrading of intelligent manufacturing industry. | | *Keywords:* Factory Simulation Digital Twin Intelligent Manufacturing Simulation Modeling Plant Simulation *DOI:* [10.24191/jcrinn.v10i2](https://doi.org/10.24191/jcrinn.v10i2.522).522 | | 1. Introduction With the rapid development of science and technology, intelligent manufacturing technology has become an indispensable part of industrial production (Tao et al., 2018). The use of automation, the Internet of things, artificial intelligence and other technologies can realize the production automation and intelligence, reduce human interference, and then improve the production efficiency and product quality (Li et al., 2017; Chander et al., 2022; Soori et al., 2023). As an important component of intelligent manufacturing, the construction method and implementation of digital twin factory involve a number of key fields, including digital modeling, virtual simulation, real-time monitoring, data analysis and optimization, cross-system integration and other (Liu et al., 2024; Segovia & Garcia-Alfaro, 2022). Through the digital modeling technology in this paper, the actual factory parts of the accurate digital processing, including production equipment, logistics system, personnel operation, etc., to establish a complete digital twin model. In the digital twin model, the production process of the factory is simulated through the simulation technology, including the operation of the production equipment, product manufacturing and logistics and other links. At the same time, through the analysis and processing of the collected data, the digital twin model is optimized and adjusted to improve the production efficiency and product quality of the factory. This paper aims to improve the production efficiency of manufacturing industry, reduce operating costs, and optimize the allocation of resources. Therefore, the construction and development of intelligent digital twin factories promotes the transformation of industrial manufacturing to intelligent and digital, helps to enhance the core competitiveness of enterprises and accelerate the development process of industrial intelligence (Li et al., 2022; Wang & Luo, 2021). 1. Intelligent production line overall scheme design **2.1 Process design** This paper studies the firebrick detection line of an enterprise in Dalian, China. The firebrick detection line has realized the production operation, which covers the whole life cycle of firebrick from warehouse to finished products (Wahab, 2022). In order to meet the requirements of the product production process, the overall process includes the raw material delivery, transportation, mold release and cutting, steam maintenance, palletizing packaging and other links. Its complete operation process is shown in Fig. 1. ![](data:image/x-emf;base64...) Fig. 1. Production line operation flow diagram **2.2 Intelligent production line planning scheme** The intelligent production line planning scheme is composed of layered design of equipment control layer, network layer, industrial cloud platform layer and application system layer (Qu et al., 2016; Wu et al., 2022). The equipment control layer is responsible for the overall planning, such as raw material control, valve monitoring, and data collection and remote control. The network layer is divided according to the hierarchical mode of the communication Internet industry, including the design of network, communication, cloud, and key basic capabilities (Peng et al., 2015). Network infrastructure mainly includes extranet, equipment, logistics tracking and energy monitoring. In terms of network form, it scientifically coordinates the planning of high-speed fiber network, wireless network and Iot sensing network; in the regional dimension level, the construction of high-speed fiber network and 4G / 4G + mobile communication network always follows the principle of comprehensive coverage of one network, and continuously promotes the commercial construction of 5G network (Stallings, 2015; Wen et al., 2021). The flow block diagram of the intelligent factory is shown in Fig. 2. ![](data:image/x-emf;base64...) Fig. 2. Smart factory flow block diagram 1. Hardware structure design of intelligent production line According to the required functions, the automatic detection line is designed for conveying system, central alignment system, separation system, 3D online measuring system, unloading system, marking and printing system, cardboard coating and installation system, and control system. As shown in Fig. 3. Overall design scheme diagram is the preliminary composition of firebrick automatic detection line. The delivery direction is shown in the arrow direction in Fig. 3. ![](data:image/x-emf;base64...) Fig. 3. Overall design scheme diagram **3.1 Conveying system** The conveying system runs through the whole brick detection line, which is the basis for all functional requirements of refractory bricks in the whole testing process. The conveying system mainly includes two types: the belt conveyor at the front end of the automatic detection line and the roller conveyor. As shown in Fig. 4 belt conveyor diagram and 5 roller conveyor diagrams are belt conveyor and roller conveyor respectively. ![](data:image/x-emf;base64...) Fig. 4. Schematic diagram of the belt conveyor **![](data:image/x-emf;base64...)** Fig. 5. Schematic diagram of the roller conveyor **3.2 Central alignment system, laser measurement system, separation system and unloading system** Fig. 6 central alignment system, laser measuring system, separation system and unloading system mechanical diagram, know the belt conveyor will brick into roller conveyor, into the central alignment system, by the system of the deviation of the brick, the mechanical alignment of the function of the motor gear, driven by the gear two relative rack movement, continue to transport forward into the separation system, the separation system is in a fixed position out baffle, let the brick stay, the function of the cylinder drive linear slide reciprocating movement (The diagram of the central alignment system structure and the separation system structure shown in Fig. 7) continue to move forward to the laser measurement system for data measurement and comparison. In the discharge system, the expansion cylinder will push out the unqualified refractory brick; the qualified one for alignment and separation will continue to be transported forward to the next station. ![](data:image/x-emf;base64...) Fig. 6. Mechanical diagram of central alignment system, laser measuring system, separation system and unloading system ![](data:image/x-emf;base64...) Fig. 7. Structure diagram of central alignment system and brief diagram of separation system **3.3 Line-marking, 3D online measurement and printing system** As shown in the schematic diagram of the marking, measurement and printing system in Fig. 8, the refractory brick qualified by laser measurement enters the second central alignment system, and the central alignment system pairs again. Then enter the marking, 3D online measurement and printing system. ![](data:image/x-emf;base64...) Fig. 8. Line-marking, measuring, and printing system **3.4 Brick bonding and cardboard mounting system** As shown in Fig. 9, the printed refractory brick is transported to the brick coating system by the roller conveyor, and the automatic coating equipment in the brick coating system is coated. The coating position and printing position are vertical end surface. the position relationship of brick printing and glue coating. The graphic arrow shows the direction of delivery. The finished bricks continue to the sticker system, attaching standard cardboard to the end of the bricks. ![](data:image/x-emf;base64...) Fig. 9. Cardboard coating and installation and brick printing and coating position relationship 1. Software production line system design This chapter studies two main parts: sorting and storage. First, the construction of the factory simulation environment based on Plant simulation software, then the design and construction of the raw material production unit and the raw material processing unit, and finally, the construction of the raw material storage unit. **4.1 Factory simulation environment construction based on plant simulation** *4.1.1 The idea of the scene building* Before designing the production line model of the intelligent factory, the digital twin scene of the production line must be built according to the actual production process. The production line studied in this study needs to meet the sorting and transportation functions. Select the required model equipment from the digital twin model library and set up the production line in the Plant simulation 3 D modeling and simulation platform. *4.1.2 Scene building platform* Plant Simulation Is a software platform for the development of 3 D modeling and simulation display of digital twin systems, which can be used for the simulation and optimization of factories, production lines and production logistics processes. As part of the Siemens digital software Tecnomatix, it includes tools such as Process Designer, Process Simulation, and Plant Simulation. It is one of the key points of Siemens' digital manufacturing strategy. As a software that can simulate factories, production lines and logistics, Plant Simulation can quantify and verify all aspects of the production system, such as workshop layout, production logistics design and production capacity, and explore the optimization direction according to the simulation results, which can verify the effect of the implementation of the program before the implementation of the plan. **4.2 Simulation module design** *4.2.1 Overall module design* The 2D / 3D layout diagram of the simulation model is as follows: ![图片10](data:image/png;base64...) Fig. 10. Simulation model 2D layout diagram ![图片11](data:image/png;base64...) Fig. 11. Simulation model 3D layout diagram In this model, the AGV trolley is generated from the Source on the left, and a buffer Buffer connects with the rail Track of the transport robot PickAndPlace, with two sensors on the rail Track. At the buffer zone Buffer, when the AGV car triggers the sensor, the car stops and loads the material, and the car continues to move when reaching the cargo capacity. ![图片12](data:image/png;base64...) Fig. 12. buffer Sensor controls At the transport robot PickAndPlace, when the AGV car triggers the sensor, the car stops, the transport robot transfers the material to the production line from the AGV car, and the AGV car material is empty when the car continues to move. ![图片13](data:image/png;base64...) Fig. 13. PickAndPlace Sensor controls *4.2.2 Design of the transport and sorting area module* A transport robot PickAndPlace is placed on the right side of the AGV track, and a sensor is set at the AGV track. The function is to stop when the car triggers the sensor, and the transport robot transfers the material. When the material carried by the car is empty, the car continues to move along the track. ![图片15](data:image/png;base64...) ![图片16](data:image/png;base64...) Fig. 14. Model layout Fig. 15. Manufacturing zone model layout A conveyor belt is placed next to the transport robot, and then a conveyor belt that can distinguish different materials and has a sorting function. The conveyor belts are connected to the other three ends. *4.2.3 Design of the raw material processing unit* Place a buffer behind each conveyor to prevent material from piling up on the belt. Two stations are placed after each buffer for material processing, with a distance between the two stations. A pool of workers is placed, in which ordinary workers and skilled workers with yellow circles are set up. The function of ordinary workers is to transfer materials from the first station to the next station. One ordinary worker is set between each two stations, and a total of three ordinary workers are required in this model. Ordinary worker A1 transfers material from Station1 to Station4; ordinary worker A2 transfers material from Station2 to Station5, and ordinary worker A3 transfers material from Station3 to Station6. The technical workers with the yellow circle are responsible for the maintenance of the smart factory. ![图片17](data:image/png;base64...) Fig. 16. Worker pool creation table Changing the value in the table above can change the number of workers and setting the Capacity (yield) value in WorkerSpace can carry multiple human jobs in a Workspace, that is, the number of Worker that can stay on the specified Workplace at any time. **4.3 Raw material storage unit design** The HBW (High Bay Warehouse) elevated cargo stereo warehouse in the Plant simulation toolbox is used to simulate the logistics and storage facilities in the actual factory. There are three components in this library object, namely WMS, roadway stacker and library location addressing control. These three components need to be used together to be effective. That is, each library component must be dragged to the modeling window to realize the operation and debugging of the three-dimensional warehouse. ![图片18](data:image/png;base64...) Fig. 17. Components in the HBW library Under the interface of management, some very simple warehousing and warehousing strategies can be implemented, namely one by one, Random, Predefined rocks and XYZ ranges. The key control information is the userSetTarget, which enables the scheduling of material storage. The important thing is a word, rooot.WMS.placeIntoStock, let WMS decide to put a material into the three-dimensional warehouse. This is the starting method of the stereo library, by which all other actions are triggered. ![图片19](data:image/png;base64...) Fig. 18. userSetTarget the algorithm There are four warehousing and warehousing strategies in plant simulation software: One by one: One place after another. Store the material from low to high, from left to right. ![图片20](data:image/png;base64...) Fig. 19. One by one source code Random: That is, the randomly ground. Store the material in the stereo. ![图片21](data:image/png;base64...) Fig. 20. Random source code Predefined: That is, the predefined. Store the material in an empty position close to the shortest path of the palletizing. ![图片22](data:image/png;base64...) Fig. 21. Predefined source code XYZ ranges, namely the XYZ range. Store an item in the first free position in all the rack channels. ![图片23](data:image/png;base64...) Fig. 22. XYZ ranges source code 1. Factory simulation operation results **5.1 Overall factory simulation** The overall 3D diagram is shown in the figure below: ![图片24](data:image/png;base64...) Fig. 23. Factory simulation overall 3D diagram **5.2 Raw material production unit** Fire brick raw materials through the conveyor belt transport to raw materials station for processing, processing by ordinary workers to the next station processing, processing through the conveyor belt successively through the spray machine and dryer spraying and drying, then by the conveyor belt transport successively successively through the paint machine and dryer for coating and drying. ![图片25](data:image/png;base64...) Fig. 24. Raw material production unit **5.3 Raw material processing unit** After the raw materials of refractory brick are sprayed with code and coating, they are transported by the conveyor belt to each station step by step. After the processing is completed, it will be transported to the transfer and sorting unit for packaging. Before the packaging, the workers will conduct manual sampling inspection on the processed raw materials to ensure the product quality. ![图片26](data:image/png;base64...) Fig. 25. Raw material processing unit ![图片27](data:image/png;base64...) Fig. 26. Manual sampling inspection **5.4 Raw material storage unit** After packing the refractory bricks for stacking, they are transported to the three-dimensional warehouse for storage and transportation. ![图片28](data:image/png;base64...) Fig. 27. Raw material storage unit **5.5 Computational results** Through the operation of the factory simulation, the conditions in the factory simulation, such as the processing situation, the station failure and maintenance situation of each station, are drawn into a diagram, and each part of the factory simulation is directly displayed. ![图片29](data:image/png;base64...) Fig. 28. Workplace diagram 1. Conclusions Through simulation operation and debugging, the paper further optimizes the factory production line model, and the functions of material processing quantity, resource statistics and worker service statistics are added to make the simulation results closer to the actual operation of the factory. At the same time, this paper also verifies the advantages of three-dimensional warehouse in material storage and scheduling through the results of three-dimensional warehouse operation. Despite the achievements made in this paper, there are still some improvements. First, the model should be further optimized to improve the accuracy and real-time performance of the simulation. Secondly, more simulation application scenarios should be explored, such as production line balance, equipment fault simulation, etc., to more comprehensively evaluate and optimize the production efficiency of the plant. In the future research, it is expected to further expand the application scope of simulation technology and provide more support and help for industrial production. At the same time, we also hope to make more breakthroughs in the research and innovation of simulation technology and contribute more strength to the intelligence and automation of industrial production. 1. Acknowledgements/Funding I would like to express my gratitude to the Faculty of Engineering of the University of Sabah in Malaysia and the Faculty of Mechanical Engineering of Beihua University in China for their support and assistance in this research. At the same time, I would also like to express my special thanks to all the participants and supporters throughout the research stage. 1. Conflict of interest statement The authors agree that this research was conducted in the absence of any self-benefits, commercial or financial conflicts and declare the absence of conflicting interests with the funders. 1. 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A survey of intelligent network slicing management for industrial IoT: Integrated approaches for smart transportation, smart energy, and smart factory. *IEEE Communications Surveys & Tutorials*, 24(2), 1175-1211. | | | | --- | --- | | ![A picture containing text, clipart Description automatically generated](data:image/png;base64...) | © 2025 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | 1. 2\* Corresponding author. *E-mail address*: baiyan@beihua.edu.cn [↑](#footnote-ref-1)