Research on Sintering End Temperature Control Based on Fuzzy Adaptive PID Control
DOI:
https://doi.org/10.67541/istaer2625Keywords:
Sintering end temperature; Fuzzy adaptive PID; Ziegler-Nichols; Robustness; Dynamic performanceAbstract
The sintering end temperature serves as a critical process parameter for assessing the completion degree of the sintering process. Its control system exhibits significant inertia and pure delay characteristics, making conventional PID controllers struggle to maintain optimal performance under all operating conditions due to fixed parameters. This paper proposes a fuzzy adaptive PID control strategy that only adjusts proportional gain and derivative gain online while maintaining constant integral gain. Using the sintering end temperature as the controlled variable, performance comparisons were conducted with both a conventional PID controller tuned using Ziegler-Nichols method and the proposed approach regarding nominal tracking accuracy, output disturbance suppression capability, and model mismatch robustness. Results demonstrate that the proposed method reduces regulation time by 76.8% under nominal conditions without steady-state error; achieves maximum temperature deviation of merely 17.7% compared to conventional PID when subjected to a 25°C external disturbance; and maintains steady-state error within 0.5°C even under severe mismatch conditions where time constants and pure delay values increase by 33% and 37.5%, respectively. With its simple structure and strong robustness, this method provides an effective reference for field-based control of sintering end temperatures.
References
[1] Solodkyi, I., Bogomol, I., & Loboda, P. (2022). High-speed electron beam sintering of WC-8Co under controlled temperature conditions. International Journal of Refractory Metals and Hard Materials, 102, 105730. https://doi.org/10.1016/j.ijrmhm.2021.105730
[2] Liu, J., Tian, C., Jiang, T., Ricohermoso, E. I., Yu, Z., Ionescu, E., ... & Riedel, R. (2023). Polymer-derived SiOC ceramics: a potential catalyst support controlled by the sintering temperature and carbon content. Journal of the European Ceramic Society, 43(8), 3191-3200. https://doi.org/10.1016/j.jeurceramsoc.2023.02.045
[3] Sun, W., Zhou, H., Tan, X., Wang, K., Ruan, H., Zhang, H., ... & Chen, X. (2018). Adjustable microwave dielectric properties of ZnO–TiO2–ZrO2–Nb2O5 composite ceramics via controlling the raw ZrO2 content and sintering temperature. Journal of Materials Science: Materials in Electronics, 29(14), 12055-12060. https://doi.org/10.1007/s10854-018-9311-x
[4] Chi, Z., Chen, X., Xia, H., Liu, C., & Wang, Z. (2024). An adaptive control system based on spatial–temporal graph convolutional and disentangled baseline-volatility prediction of bellows temperature for iron ore sintering process. Journal of Process Control, 140, 103254. https://doi.org/10.1016/j.jprocont.2024.103254
[5] Fan, W., Peng, Z., Yin, T., Xiang, C., Tang, H., Ye, L., & Rao, M. (2024). Tailoring tactics for preparation of superior thermal insulation materials from blast furnace ferronickel slag: Control of sintering temperature. Process Safety and Environmental Protection, 186, 1242-1252. https://doi.org/10.1016/j.psep.2024.04.041
[6] Erkınay Özdemir, M., Beşkardeş, A., & Hameş, Y. (2024). Intelligent sinter machine speed control system using optimized fuzzy logic controller: An experimental study in iron and steel plant. Arabian Journal for Science and Engineering, 49(12), 16391-16406. https://doi.org/10.1007/s13369-024-08981-z
[7] Li, L., Wang, S., Chen, L., Hou, H., & Zhao, Y. (2024). Improved corrosion resistance of AZ91D through sintering temperature control for second phase precipitation. Materials Characterization, 209, 113690. https://doi.org/10.1016/j.matchar.2024.113690
[8] Wang, H. J., & Bian, J. J. (2024). Tuning the temperature coefficient of the resonant frequency of the AlPO4–BPO4–SiO2 ternaries with TiO2 addition and sintering atmosphere control. Journal of the European Ceramic Society, 44(4), 2144-2149. https://doi.org/10.1016/j.jeurceramsoc.2023.10.031
[9] Choi, S. J., Jang, J. S., Park, H. J., & Kim, I. D. (2017). Optically sintered 2D RuO2 nanosheets: temperature‐controlled NO2 reaction. Advanced Functional Materials, 27(13), 1606026. https://doi.org/10.1002/adfm.201606026
[10] Bulat, L., Novotel'nova, A., Tukmakova, A., Yerezhep, D., Osvenskii, V., Sorokin, A., ... & Ashmontas, S. (2017). Temperature fields control in the process of spark plasma sintering of thermoelectrics. Technical Physics, 62(4). https://doi.org/10.1134/S1063784217040053
[11] Giuntini, D., Chen, I. W., & Olevsky, E. A. (2016). Sintering shape distortions controlled by interface roughness in powder composites. Scripta Materialia, 124, 38-41. https://doi.org/10.1016/j.scriptamat.2016.06.024
[12] Lu, G., Chen, J. L., Yan, Q. S., Tu, Z. X., & Chen, Y. S. (2025). Zero-shrinkage densification of Y₂O₃-based ceramic cores via in-situ Al reaction and sintering temperature control. Ceramics International, 51(24PA), 40719–40734. https://doi.org/10.1016/j.ceramint.2025.06.319
[13] Dai, Y., Chen, N., & Shao, Z. (2025). Hybrid self-learning model for the prediction and control of sintering furnace temperature. Control Engineering Practice, 154, 106159. https://doi.org/10.1016/j.conengprac.2024.106159
[14] Abdelrahman, M., & Starr, T. L. (2016). Quality certification and control of polymer laser sintering: layerwise temperature monitoring using thermal imaging. The International Journal of Advanced Manufacturing Technology, 84(5), 831-842. https://doi.org/10.1007/s00170-015-7524-1
[15] Garrigues, A. R., Wang, L., Del Barco, E., & Nijhuis, C. A. (2016). Electrostatic control over temperature-dependent tunnelling across a single-molecule junction. Nature communications, 7(1), 11595. https://doi.org/10.1038/ncomms11595
[16] Voisin, T., Durand, L., Karnatak, N., Le Gallet, S., Thomas, M., Le Berre, Y., ... & Couret, A. (2013). Temperature control during Spark Plasma Sintering and application to up-scaling and complex shaping. Journal of Materials Processing Technology, 213(2), 269-278. https://doi.org/10.1016/j.jmatprotec.2012.09.023
[17] Molénat, G., Durand, L., Galy, J., & Couret, A. (2010). Temperature control in Spark Plasma sintering: an FEM approach. Journal of Metallurgy, 2010(1), 145431. https://doi.org/10.1155/2010/145431
[18] Morel, C., & Morel, J. Y. (2025, April). Chaos Anticontrol and Switching Frequency Impact on MOSFET Junction Temperature and Lifetime. In Actuators (Vol. 14, No. 5, p. 203). MDPI. https://doi.org/10.3390/act14050203
[19] Chen, C., Yin, S., Liu, J., Sun, C., Zhang, Y., Liu, L., & Zuo, R. (2026). High-Strength, High-Thermal-Conductivity Si3N4 Ceramics via Synergistic Slurry and Sintering Control. Ceramics International. https://doi.org/10.1016/j.ceramint.2026.04.190
[20] Chen, J., Gui, W., Chen, N., Peng, W., Liu, R., Zhou, X., ... & Guo, Y. (2025). An Energy-Efficient Sintering Temperature Multimode Control and Optimization for High-Quality Ternary Cathode Materials. Future Batteries, 5, 100034. https://doi.org/10.1016/j.fub.2025.100034
[21] Mora-Barzaga, G., Inostroza, P., Valencia, F., & Bringa, E. (2026). Ultrafast thermal sintering controls thermal transport in high-entropy alloy nanoparticle junctions. International Journal of Heat and Mass Transfer, 270, 129207. https://doi.org/10.1016/j.ijheatmasstransfer.2026.129207
[22] Chen, J., Gui, W., Chen, N., Li, H. X., Luo, B., & Li, B. (2026). Self-Triggered H∞ temperature field control for the time-delay sintering process of cathode materials via adaptive dynamic programming. Control Engineering Practice, 176, 107142. https://doi.org/10.1016/j.conengprac.2026.107142
[23] Kang, L., Qi, A., Dong, H., Fan, W., Zhang, Z., Jiang, X., & Liu, G. (2026). Boron carbide for HTGR control rods: Sintering, temperature‐dependent properties, and thermal shock resistance. International Journal of Applied Ceramic Technology, 23(1), e70140. https://doi.org/10.1111/ijac.70140
[24] Li, B., Cao, X., Luo, B., Gao, J., Chen, N., Chen, J., ... & Gui, W. (2025). Asymmetric ADP-driven event-triggered optimal control of industrial sintering temperature field with input constraints. Neurocomputing, 131877. https://doi.org/10.1016/j.neucom.2025.131877
[25] Liu, H., Zhao, X., Xu, J., Xia, W., Yue, Q., Yuan, Y., ... & Zhang, Z. (2026). A Strategy for Intermediate-temperature Plasticity Control in Polycrystalline Alloys Based on Equal-Cohesive Temperature. Materials Science and Engineering: A, 150454. https://doi.org/10.1016/j.msea.2026.150454
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Data Availability Statement
The data that support the findings of this study are available upon request from the corresponding authors, Y.H.
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Copyright (c) 2026 Yunkai He, Jinfeng Yang (Author)
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