Optimizing Reflow Soldering Oven Temperature Control: A Mathematical Approach with First-Order Modelling and PI Controller Design

Öz

This paper presents a comprehensive analysis of the identification and control of a reflow soldering oven, with the primary goal of developing a Proportional-Integral (PI) controller to ensure precise temperature regulation and high-quality solder joints in circuit board manufacturing. The study begins by examining the generic thermal profile of a reflow soldering oven, offering insights into its temperature dynamics and control requirements. A mathematical model of the oven is derived using a first-order transfer function with transport delay, based on system response analysis. This model serves as the basis for designing and tuning the PI controller. The design process involves a systematic approach, including model validation and performance analysis under various operating conditions. The optimization of temperature control focuses on minimizing overshoot, compensating for steady-state errors, and ensuring robust responses to disturbances. Comprehensive simulations are conducted to evaluate the system's performance and stability, taking into account potential disturbances, noise, and time delays. The effectiveness of the developed PI controller is validated through comparisons with a simple Proportional (P) controller, and its accuracy is verified using the pole placement method. The results demonstrate significant improvements in temperature control, highlighting the controller's ability to precisely maintain desired thermal profiles.

Anahtar Kelimeler

• Reflow soldering oven temperature control
• First-order systems
• PI controller tuning
• Pole placement method
• Transfer function modelling

Kaynakça

1. Åström KJ, Murray R. 2021. Feedback systems: an introduction for scientists and engineers. Princeton Univ Press, pp: 13.
2. Bhayo MA, Mirsaeidi S, Koondhar MA, Chandio S, Tunio MA, Allasi HL, Azız MJA, Idris NRN. 2023. An experimental hybrid control approach for wind turbine emulator. IEEE Access, 11: 58064-58077.
3. Birs I, Muresan C, Nascu I, Ionescu C. 2019. A survey of recent advances in fractional order control for time delay systems. IEEE Access, 7: 30951-30965.
4. Borase RP, Maghade DK, Sondkar SY, Pawar SN. 2021. A review of PID control, tuning methods and applications. Int J Dyn Control, 9: 818-827.
5. Chen Q, Li N, Feng W. 2021. Model predictive control optimization for rapid response and energy efficiency based on the state-space model of a radiant floor heating system. Energy Build, 238: 110832.
6. Chen R, Yuan Y, Thomson D. 2021. A review of mathematical modelling techniques for advanced rotorcraft configurations. Prog Aerosp Sci, 120: 100681.
7. Chen X. 2023. Temperature control in electric furnaces: Methods, applications, and challenges. J Phys: Conf Ser, 2649(1): 012032.
8. Cheng S, Huang CM, Pecht M. 2017. A review of lead-free solders for electronics applications. Microelectron Reliab, 75: 77-95.

9. Davidson J, Ringwood JV. 2017. Mathematical modelling of mooring systems for wave energy converters-A review. Energies, 10(5): 666.
10. Deb D, Patel R, Balas VE. 2020. A review of control-oriented bioelectrochemical mathematical models of microbial fuel cells. Processes, 8(5): 583.
11. Gu A, Johnson I, Goel K, Saab K, Dao T, Rudra A, Ré C. 2021. Combining recurrent, convolutional, and continuous-time models with linear state space layers. Adv neural inf process syst, 34: 572-585.
12. Hannan MA, Islam NN, Mohamed A, Lipu MSH, Ker PJ, Rashid MM, Shareef H. 2018. Artificial intelligent based damping controller optimization for the multi-machine power system: A review. IEEE Access, 6: 39574-39594.
13. Hou Z, Chi R, Gao H. 2016. An overview of dynamic-linearization-based data-driven control and applications. IEEE Trans Ind Electron, 64(5): 4076-4090.
14. Jamil AA, Tu WF, Ali SW, Terriche Y, Guerrero JM. 2022. Fractional-order PID controllers for temperature control: A review. Energies, 15(10): 3800.
15. Karamanakos P, Liegmann E, Geyer T, Kennel R. 2020. Model predictive control of power electronic systems: Methods, results, and challenges. IEEE Open J Ind Appl, 1: 95-114.
16. Lashab A, Sera D, Guerrero JM, Mathe L, Bouzid A. 2018. Discrete model-predictive-control-based maximum power point tracking for PV systems: Overview and evaluation. IEEE Trans Power Electron, 33(8): 7273-7287.
17. Li S, Peissig J. 2020. Measurement of head-related transfer functions: A review. Appl Sci, 10(14): 5014.
18. Mihalič F, Truntič M, Hren A. 2022. Hardware-in-the-loop simulations: A historical overview of engineering challenges. Electronics, 11(15): 2462.
19. Minchala-Avila LI, Garza-Castañón LE, Vargas-Martínez A, Zhang Y. 2015. A review of optimal control techniques applied to the energy management and control of microgrids. Procedia Comp Sci, 52, 780-787.
20. Nise NS. 2020. Control systems engineering. John Wiley Sons, pp: 2-17.
21. Said M, Salleh NA, Nazeri MFM, Akbulut H, Kheawhom S, Mohamad AA. 2023. Microwave hybrid heating for lead-free solder: A review. J Mater Res Tech, 26: 6220-6243.
22. Silvas E, Hofman T, Murgovski N, Etman LP, Steinbuch M. 2016. Review of optimization strategies for system-level design in hybrid electric vehicles. Trans Vehic Technol, 66(1): 57-70.
23. Susanto T, Setiawan MB, Jayadi A, Rossi, F, Hamdhi A, Sembiring JP. 2021. Application of unmanned aircraft PID control system for roll, pitch and yaw stability on fixed wings. Int Conf Comp Sci, Inf Techn Electric Eng, October 27-28, Banyuwangi, Indonesia, pp: 186-190.
24. Tsui CC. 2022. Robust control system design: advanced state space techniques. CRC Press, pp: 7.
25. Yanarateş C, Zhou Z, Altan A. 2024. Investigating the impact of discretization techniques on real-time digital control of DC-DC boost converters: A comprehensive analysis. Heliyon, 10(20): e39591.
26. Zia MF, Nasir M, Elbouchikhi E, Benbouzid M, Vasquez JC, Guerrero JM. 2022. Energy management system for a hybrid PV-wind-tidal-battery-based islanded DC microgrid: Modeling and experimental validation. Renewable Sustainable Energy Rev, 159: 112093.