Application of Computer Simulation to Evaluate Performance Parameters of the Selective Soldering Process
Source abstract
Abstract
The growing complexity of production systems in the technology sector demands advanced tools to ensure efficiency, flexibility, and cost-effectiveness. This study presents the development of a simulation model for a selective soldering line at a technology manufacturing company in Poland, created during an engineering internship. Using FlexSim 24.2 software, the real production process was replicated, including input/output queues, manual insertion (MI) stations, soldering machines, and quality control points. Special emphasis was placed on implementing dynamic process logic via ProcessFlow, enabling detailed modeling of token flow and system behavior. Through experimentation, various configurations were tested to optimize process time and the number of soldering pallets in circulation. The results revealed that reducing pallets from 12 to 8 maintains process continuity while offering cost savings without impacting performance. An intuitive operator panel was also developed, allowing users to adjust parameters and monitor outcomes in real time. The project demonstrates that simulation not only supports operational decision-making and resource planning but also enhances interdisciplinary communication by visually conveying complex workflows. Ultimately, the study confirms that simulation modeling is a powerful and adaptable approach to production optimization, contributing to long-term strategic improvements and innovation in technologically advanced manufacturing environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Manufacturing Process and Optimization · Flexible and Reconfigurable Manufacturing Systems · Digital Transformation in Industry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Civil Engineering and Resource Management, AGH University of Krakow, Mickiewicza Av. 30, 30-059 Cracow, Poland
Funding
AGH University of Krakow
References (38)
Show source-provided references
1. Implementing Smart Factory of Industrie 4.0: An Outlook
2. Jerbi, A. (2021). Discrete-Event Simulation: Concepts and Production in Arena, Wiley.
3. Beaverstock, M., Greenwood, A., and Nordgren, W. (2024). Applied Simulation: Modeling and Analysis Using FlexSim, FlexSim Software Products, Inc.. [5th ed.].
4. Réveillac, J.-M. (2017). FlexSim User Guide: Modeling and Simulation of Logistics Flows, FlexSim.10.1002/9781119368540 ↗
5. Computer simulation of the operation of a longwall complex using the “Process Flow” concept of FlexSim software
6. Simulation in the design and operation of sustainable manufacturing systems: Recent developments and future trends
7. Banks, J., Carson, J.S., Nelson, B.L., and Nicol, D.M. (2010). Discrete-Event System Simulation, Pearson. [5th ed.].
8. Law, A.M., and Kelton, W.D. (2007). Simulation Modeling and Analysis, McGraw-Hill. [4th ed.].
9. Fallah, M.M., and Mahmoudi, A. (2021). System Dynamics Simulation of Lean Production Implementation in Manufacturing Systems. Processes, 9.
10. Forrester, J.W. (1961). Industrial Dynamics, MIT Press.
11. Forrester, J.W. (1969). Urban Dynamics, Pegasus Communications/MIT Press.
12. Forrester, J.W. (1971). World Dynamics, Wright-Allen Press.
13. Epstein, J.M., and Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up, MIT Press.10.7551/mitpress/3374.001.0001 ↗
14. Tutorial on agent-based modelling and simulation10.1057/jos.2010.3 ↗
15. Fishman, G.S. (1996). Monte Carlo: Concepts, Algorithms, and Applications, Springer.10.1007/978-1-4757-2553-7 ↗
16. The Monte Carlo Method10.1080/01621459.1949.10483310 ↗
17. Azad, A.K., Jadeja, A.C., Doppalapudi, A.T., Hassan, N.M.S., Nabi, M.N., and Rauniyar, R. (2024). Design and Simulation of the Biodiesel Process Plant for Sustainable Fuel Production. Sustainability, 16.10.3390/su16083291 ↗
18. Fischer, A., Beiderwellen Bedrikow, A., Tommelein, I.D., Nübel, K., and Fottner, J. (2023). From Activity Recognition to Simulation: The Impact of Granularity on Production Models in Heavy Civil Engineering. Algorithms, 16.10.3390/a16040212 ↗
19. Elbasheer, M., Longo, F., Mirabelli, G., and Solina, V. (2024). Flexible Symbiosis for Simulation Optimization in Production Scheduling: A Design Strategy for Adaptive Decision Support in Industry 5.0. J. Manuf. Mater. Process., 8.10.3390/jmmp8060275 ↗
20. Simulation-based optimization in flexible manufacturing systems: Recent advances and future directions
21. Lewicki, W., Niekurzak, M., and Wróbel, J. (2024). Development of a Simulation Model to Improve the Functioning of Production Processes Using the FlexSim Tool. Appl. Sci., 14.10.3390/app14166957 ↗
22. Pawlak, A., Lewicki, W., and Niekurzak, M. (2024). Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage. Appl. Sci., 14.10.3390/app142310863 ↗
23. Kampa, A., Gołda, G., and Paprocka, I. (2017). Discrete Event Simulation Method as a Tool for Improvement of Manufacturing Systems. Computers, 6.10.3390/computers6010010 ↗
24. A review of computer simulations in underground and open-pit mining
25. Greenwood, A. (2019). Symulacja Stosowana, R.2, InterMarium.
26. Simulation optimization: A review of algorithms and applications
27. Diepstraten, G. (2020). Wave/Selective Soldering. Leadfree Soldering Process Development and Reliability, Wiley.10.1002/9781119482093.ch2 ↗
28. Mokotechnology (2025, June 13). Mastering Selective Soldering: A Comprehensive Guide. Available online: https://www.mokotechnology.com/selective-soldering-guide/.
29. McMaster, S.J., Witham, K.F., Stephenson, M., and Groves, E. (2024). Process Challenges for Selective Soldering: Examining Parameters for Optimal Processing, Conference Coventry University.
30. Reid, H. (2021). Practical Methods for Evaluating and Qualifying Selective Soldering Systems, Hentec Industries/RPS Automation.
31. Optimisation of Selective Soldering Processes Based on AI and Computer Simulation
32. Lim, J.-B., and Jeong, J. (2023). Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices. Appl. Sci., 13.10.20944/preprints202308.0431.v1 ↗
33. FlexSim-Simulated PCB Assembly Line Optimization Using Deep Q-Network
34. Hering, S., Schäuble, N., Buck, T.M., Loretz, B., Rillmann, T., Stieneker, F., and Lehr, C.-M. (2021). Analysis and Optimization of Two Film-Coated Tablet Production Processes by Computer Simulation: A Case Study. Processes, 9.10.3390/pr9010067 ↗
35. Zhao, K., Shi, Q., Zhao, S., Ye, F., and Badran, M. (2024). Filling Process Optimization of a Fully Flexible Machine through Computer Simulation and Advanced Mathematical Modeling. Processes, 12.10.3390/pr12091962 ↗
36. Praktyczne zastosowanie narzędzia FlexSim w modelowaniu symulacyjnym systemów produkcyjnych
37. Benmoussa, O. (2022). Improving Replenishment Flows Using Simulation Results: A Case Study. Logistics, 6.10.3390/logistics6020034 ↗
38. Li, X., and Chen, Y. (2022). FlexSim: 3D Simulation Modelling and Analysis Software, FlexSim Software Products, Inc.