Air Conditioning Heat Exchanger Intelligent Production Line: Design Methodologies and Applications

Abstract

As a key component in modern building environmental control systems, the production quality and performance of multi-split central air conditioning systems directly influence the comfort, energy efficiency, and operational stability of buildings. However, the current manufacturing process primarily relies on a combination of traditional manual labor and automated equipment, resulting in low efficiency, high energy consumption, and limited automation. This paper first presents an optimized design for an intelligent manufacturing production line for multi-split central air conditioning heat exchangers to address these issues. It details the design of key systems for the intelligent production line and ensures continuous production and processing. Additionally, the paper analyzes the production process of the intelligent manufacturing line, with particular emphasis on the mechanism of the heat exchanger tube expansion process. Furthermore, it designs the fixture structure of the transfer robot for each process in the production line and discusses the principles of workpiece positioning and clamping. Utilizing technologies such as sensor networks, PLC, and industrial Ethernet, the system completes the closed-loop process of perception, transmission, analysis, decision-making, and execution within the production line, enabling transparency, fault predictability, and automated management. The results show that the intelligent assembly production line has significantly improved the assembly efficiency, achieving a 300% increase in the daily production capacity of a single line. While enabling the continuous and intelligent production of multi-split central air conditioning heat exchangers.

Keywords

Central air conditioner / Heat exchanger / Production line / Process flow fixture / Intelligent control / Robot

Author Contributions

Conceptualization, Z.Z. and Y.F. (Yuewen Feng); Methodology, Y.H. and X.M.; Software, B.L. and G.L.; Validation, S.C. and G.J.; Formal Analysis, X.S. and Y.Z.; Investigation, X.Y. (Xu Yan), X.Y. (Xianxin Yin) and S.W.; Data Curation, J.W. and Q.B.; Writing—Original Draft Preparation, Y.F. (Yudi Fei); Writing—Review & Editing, C.L.; Funding Acquisition, C.L.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Funding

This study was financially Supported by National Natural Science Foundation of China (Grant Nos.52375447, 52305477 and 52105457), the Shandong Provincial Natural Science Foundation of China (Grant Nos. ZR2023QE057, ZR2024QE100 and ZR2024ME255), the Shandong Provincial Science and Technology SMEs Innovation Capacity Improvement Project (Grant No. 2024TSGC0239), the Special Fund of Taishan Scholars Project, the Shandong Province Youth Science and Technology Talent Support Project (Grant No.SDAST2024QTA043), and the Open Funding of Key Lab of Industrial Fluid Energy Conservation and Pollution Control, Ministry of Education (Grant Nos. CK-2024-0031, CK-2024-0035 and CK-2024-0036).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Dani S, Rahman A, Jin J, Kulkarni A. Cloud-Empowered Data-Centric Paradigm for Smart Manufacturing. Machines 2023, 11, 451. doi:10.3390/machines11040451.

[2] Davis J, Edgar T, Porter J, Bernaden J, Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 2012, 47, 145-156. doi:10.1016/j.compchemeng.2012.06.037.

[3] Alhama Blanco PJ, Abu-Dakka FJ, Abderrahim M. Practical Use of Robot Manipulators as Intelligent Manufacturing Systems. Sensors 2018, 18, 2877. doi:10.3390/s18092877.

[4] Ding K, Lei JY, Felix TSC, Hui J, Zhang F, Wang Y. Hidden Markov model-based autonomous manufacturing task orchestration in smart shop floors. Robot. Comput. Integr. Manuf. 2020, 61, 101845.1-101845.9. doi:10.1016/j.rcim.2019.101845.

[5] Ahmmed MS, Isanaka SP, Liou F. Promoting Synergies to Improve Manufacturing Efficiency in Industrial Material Processing: A Systematic Review of Industry 4.0 and AI. Machines 2024, 12, 681. doi:10.3390/machines12100681.

[6] Errandonea I, Beltrán S, Arrizabalaga S. Digital Twin for maintenance: A literature review. Comput. Ind. 2020, 123, 103316. doi:10.1016/j.compind.2020.103316.

[7] Farbiz F, Habibullah MS, Hamadicharef B, Maszczyk T, Aggarwal S. Knowledge-embedded machine learning and its applications in smart manufacturing. J. Intell. Manuf. 2023, 34, 2889-2906. doi:10.1007/s10845-022-01973-6.

[8] Hu S, Li C, Li B, Yang M, Wang X, Gao T, et al. Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application. Intell. Sustain. Manuf. 2024, 1, 10007. doi:10.35534/ism.2024.10007.

[9] Suleiman DY, Li Q, Li B, Zhang Y, Zhang B, Liu D, et al. Digital Twin and Artificial Intelligence in Machining: A Bibliometric Analysis. Intell. Sustain. Manuf. 2025, 2, 10005. doi:10.70322/ism.2025.10005.

[10] Ryalat M, ElMoaqet H, AlFaouri M. Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0. Appl. Sci. 2023, 13, 2156. doi:10.3390/app13042156.

[11] Wang L, Chen X, Liu Q. A Lightweight Intelligent Manufacturing System Based on Cloud Computing for Plate Production. Mob. Netw. Appl. 2017, 22, 1170-1181. doi:10.1007/s11036-017-0862-5.

[12] Zheng P, Wang H, Sang Z, Zhong RY, Liu Y, Liu C, et al. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 2018, 13, 137-150. doi:10.1007/s11465-018-0499-5.

[13] Mienye ID, Sun Y. A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. IEEE 2022, 10, 99129-99149. doi:10.1109/ACCESS.2022.3207287.

[14] Wang JL, Xu CQ, Zhang J, Zhong R. Big data analytics for intelligent manufacturing systems: A review. J. Manuf. Syst. 2022, 62, 738-752. doi:10.1016/j.jmsy.2021.03.005.

[15] Huang JT, Huang SH, Moghaddam SK, Yuqian Lu Y, Wang G, Yan Y, et al. Deep Reinforcement Learning-Based Dynamic Reconfiguration Planning for Digital Twin-Driven Smart Manufacturing Systems with Reconfigurable Machine Tools. IEEE Trans. Ind. Inform. 2024, 20, 13135-13146. doi:10.1109/TII.2024.3431095.

[16] Huang Z, Fey M, Liu C, Beysel E, Xu X, Brecher C, et al. Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation. Robot. Comput. -Integr. Manuf. 2023, 82, 102545. doi:10.1016/j.rcim.2023.102545.

[17] Çınar ZM, Abdussalam Nuhu A, Zeeshan Q, Korhan O, Asmael M, Safaei B. Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability 2020, 12, 8211. doi:10.3390/su12198211.

[18] Zhang J, Kang X, Ye Z, Liu L, Tao G, Cao H. Development and testing of a wireless smart toolholder with multi-sensor fusion. Front. Mech. Eng. 2023, 18, 55. doi:10.1007/s11465-023-0774-y.

[19] Chen S, Huang Z, Guo H. An End-to-End Deep Learning Method for Dynamic Job Shop Scheduling Problem. Machines 2022, 10, 573. doi:10.3390/machines10070573.

[20] Bi ZM, Liu YF, Krider J, Buckland J, Whiteman A, Beachy D, et al. Real-time force monitoring of smart grippers for Internet of Things (IoT) applications. J. Ind. Inf. Integr. 2018, 11, 19-28. doi:10.1016/j.jii.2018.02.004.

[21] Wu L, Zhang Y, Liu Z, Qin A, Zhang C, Song X, et al. Intelligent ManufacturingFactory: A Bibliometric Analysis of Global Research Hotspots and Progress. Intell. Sustain. Manuf. 2024, 1, 10018. doi:10.70322/ism.2024.10018.

[22] Velandia DMS, Kaur N, Whittow WG, Conway PP, West AA. Towards industrial internet of things: Crankshaft monitoring, traceability and tracking using RFID. Robot. Comput. -Integr. Manuf. 2016, 41, 66-77. doi:10.1016/j.rcim.2016.02.004.

[23] Wang H, Luo H, Ren L, Huo M, Jiang Y, Kaynak O. Data-Driven Design of Distributed Monitoring and Optimization System for Manufacturing Systems. IEEE Trans. Ind. Inform. 2024, 20, 9455-9464. doi:10.1109/TII.2024.3383491.

[24] Ding K, Zhang Y, Chan FTS, Zhang C, Lv J, Liu Q. A cyber-physical production monitoring service system for energy-aware collaborative production monitoring in a smart shop floor. J. Clean. Prod. 2021, 297, 126599. doi:10.1016/j.jclepro.2021.126599.

[25] Meng ZZ, Wu ZP, Gray J. RFID-Based Object-Centric Data Management Framework for Smart Manufacturing Applications. IEEE Internet Things J. 2019, 6, 2706-2716. doi:10.1109/JIOT.2018.2873426.

[26] Wang XQ, Liu MZ, Liu CH, Ling L, Zhang X. Data-driven and Knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing. Expert Syst. Appl. 2023, 234, 121136.1-121136.20. doi:10.1016/j.eswa.2023.121136.

[27] Zhang XY, Ming XG, Bao YG. A flexible smart manufacturing system in mass personalization manufacturing model based on multi-module-platform, multi-virtual-unit, and multi-production-line. Comput. Ind. Eng. 2022, 171, 108379. doi:10.1016/j.cie.2022.108379.

[28] Liu Q, Leng JW, Yan DX, Zhang D, Wei L, Yu A. Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. J. Manuf. Syst. 2021, 58, 52-64. doi:10.1016/j.jmsy.2020.04.012.

[29] Wu QC, Mao YS, Chen JX, Wang C. Application Research of Digital Twin-Driven Ship Intelligent Manufacturing System: Pipe Machining Production Line. J. Mar. Sci. Eng. 2021, 9, 338. doi:10.3390/jmse9030338.

[30] Yang J, Son YH, Lee D, Noh SD. Digital Twin-Based Integrated Assessment of Flexible and Reconfigurable Automotive Part Production Lines. Machines 2022, 10, 75. doi:10.3390/machines10020075.

[31] Leng J, Chen Z, Sha W, Lin Z, Lin J, Liu Q. Digital twins-based flexible operating of open architecture production line for individualized manufacturing. Adv. Eng. Inform. 2022, 53, 101676. doi:10.1016/j.aei.2022.101676.

[32] EL Ghadoui M, Mouchtachi A, Majdoul R. A hybrid optimization approach for intelligent manufacturing in plastic injection molding by using artificial neural network and genetic algorithm. Sci. Rep. 2023, 13, 21817. doi:10.1038/s41598-023-48679-0.

[33] Kim YG, Lee S, Son J, Bae H, Do Chung B. Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system. J. Manuf. Syst. 2020, 57, 440-450. doi:10.1016/j.jmsy.2020.11.004.

[34] Yin H, Huang X, Cao E. A Cloud-Edge-Based Multi-Objective Task Scheduling Approach for Smart Manufacturing Lines. J. Grid. Comput. 2024, 22, 9. doi:10.1007/s10723-023-09723-5.

[35] Ling S, Guo D, Li M, Rong Y, Huang GQ. Heterogeneous demand-capacity synchronization for smart assembly cell line based on artificial intelligence-enabled IIoT. J. Intell. Manuf. 2024, 35, 539-554. doi:10.1007/s10845-022-02050-8.

[36] Yuan D, Tang D, Ni Y, Li X, Gao Z, Yu W, et al. A Forming Line for the Inner Shell of a Central Air Conditioning Unit. CN Patent 114227280A, 25 March 2022.

[37] Wang H, Zhou Z, Feng Y, Zhang Y, Ma X, Li B, et al. Central air conditioning indoor machine intelligent assembly line and coating system design. Manuf. Technol. Mach. Tools 2025, 10, 186-198. doi:10.19287/j.mtmt.1005-2402.2025.10.019.

[38] Saari LM, Kääriäinen J, Ylikerälä M. Maturity Model for the Manufacturing Industry with Case Experiences. Intell. Sustain. Manuf. 2024, 1, 10010. doi:10.35534/ism.2024.10010.

[39] Chen W. Intelligent manufacturing production line data monitoring system for industrial internet of things. Comput. Commun. 2020, 151, 31-41. doi:10.1016/j.comcom.2019.12.035.

[40] Chen L, Zheng Y, Cai Z, Zhu H. Fin Type Heat Exchanger for Central Air Conditioner. CN Patent 222799175U, 25 April 2025.

[41] Fan Z. The Flat Tube Heat Exchanger Tube Expanding Process Integration Research. Master’s Thesis, Wuhan University of Science and Technology, Wuhan, China, 2023.

[42] Tang D, Li D, Peng Y. Optimization to the tube-fin contact status of the tube expansion process. J. Mater. Process. Technol. 2011, 211, 573-577. doi:10.1016/j.jmatprotec.2010.11.010.

[43] Ma DB, Qu MF. Research on on-line Monitoring Method of Automatic Production Line based on Industrial Internet of Things. In Proceedings of the 2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI), Harbin, China, 25-27 December 2020, pp. 492-496.

[44] Ni C, Wang X, Deng X.Radiator Fin Stamping Forming Machine. CN Patent 117505711A, 6 February 2024.

[45] Jie Chu J, Zhu M, You Y, Jin T. Air conditioning fin forming process and mold design. Mold Ind. 2023, 49, 27-31. doi:10.16787/j.cnki.1001-2168.dmi.2023.05.005.

[46] Chen M, Zhang Y, Liu B, Zhou Z, Zhang N, Wang H, et al. Design of Intelligent and Sustainable Manufacturing Production Line for Automobile Wheel Hub. Intell. Sustain. Manuf. 2024, 1, 10003. doi:10.35534/ism.2024.10003.

[47] Liang W. Research on the Optimization of Expansion Welding Process Parameters for Air Conditioning Heat Exchangers. Ph.D. thesis, South China University of Technology, Guangzhou, China, 2020. doi:10.27151/d.cnki.ghnlu.2020.001490.

[48] Xia Q, Li Z, Long X, Long C.Air-conditioning finned tube expanding process of buckling deformation and the relevant parameters study. J. South China Univ. Technol. 2016, 44, 33-39.

[49] Sun Y, Wang S, Zheng H, Wang X, He M. Solubilities of R-1233zd(E) in Three Lubricating Oils and Impact of Oil Retention on the Evaporator Performance. J. Chem. Eng. Data 2024, 69, 1832-1841. doi:10.1021/acs.jced.4c00018.

[50] Vijay KV, Shahin K. Artificial Intelligence and Machine Learning for Sustainable Manufacturing: Current Trends and Future Prospects. Intell. Sustain. Manuf. 2025, 2, 10002. doi:10.70322/ism.2025.10002.

[51] Li Y, Li M, Li Y, Cai W, Yao L. Experimental investigation on the heat transfer performance of falling film evaporation with lubrication oil on horizontal tubes having different structures. Int. J. Therm. Sci. 2021, 160, 106669. doi:10.1016/j.ijthermalsci.2020.106669.

[52] Qian Y, Bian S, Zhao P, Wang T, Hua Y, Liu Y, et al. The investigation of evaporation behavior of lubricating oil droplet at high ambient temperature. Int. Commun. Heat Mass Transf. 2020, 117, 104689. doi:10.1016/j.icheatmasstransfer.2020.104689.

[53] Wang Z, Liu M, Zhao H, Gu K, Fan L, Feng L. Experimental investigation of evaporation and auto-ignition characteristics of lubrication oil droplets. Int. Commun. Heat Mass Transf. 2025, 164, 108802. doi:10.1016/j.icheatmasstransfer.2025.108802.

[54] Qingdao Zhiyan Automation Equipment Co., Ltd. A Clamping Claw Replacement Component for a Palletizing Robot. CN Patent 222986976U, 17 June 2025.

[55] Chen Z, Meng L, Zhou Y, Yu C, Chen B, Meng W. Grasping Device, Air Conditioner Outdoor Unit Production Line and Grasping Method. CN Patent 118769290A, 15 October 2024.

[56] Wan N, Wang Z, Mo R. An intelligent fixture design method based on smart modular fixture unit. Int. J. Adv. Manuf. Technol. 2013, 69, 2629-2649. doi:10.1007/s00170-013-5134-3.

[57] Fu W, Campbell MI. Concurrent fixture design for automated manufacturing process planning. Int. J. Adv. Manuf. Technol. 2015, 76, 375-389. doi:10.1007/s00170-014-6247-z.

[58] Nelaturi S, Rangarajan A, Fritz C, Kurtoglu T. Automated fixture configuration for rapid manufacturing planning. Comput. -Aided Des. 2014, 46, 160-169. doi:10.1016/j.cad.2013.08.028.

[59] Avalle M, Priarone PC, Scattina A. Experimental and numerical characterization of a mechanical expansion process for thin-walled tubes. J. Mater. Process. Technol. 2014, 214, 1143-1152. doi:10.1016/j.jmatprotec.2013.12.011.

[60] Jo C, Hwang S, Kim H. Clamping-Force Control for Electromechanical Brake. IEEE Trans. Veh. Technol. 2010, 59, 3205-3212. doi:10.1109/TVT.2010.2043696.

[61] Li Y, Shim T, Shin DH, Lee S, Jin S. Control System Design for Electromechanical Brake System Using Novel Clamping Force Model and Estimator. IEEE Trans. Veh. Technol. 2021, 70, 8653-8668. doi:10.1109/TVT.2021.3095900.

[62] Jiping L, Faping Z, Jianhua Z, Qian H, Ma N. Quantitative Optimization of Workpiece-fixture System’s Clamping Forces. International Journal of Computational Intelligence Systems 2011, 4, 402-409. doi:10.1080/18756891.2011.9727799.

[63] Sheng J, Zhang Q, Li H, Shen S, Ming R, Jiang J, et al. Digital twin driven intelligent manufacturing for FPCB etching production line. Comput. Ind. Eng. 2023, 186, 109763. doi:10.1016/j.cie.2023.109763.

[64] Li C, Lu P, Zhu W, Zhu H, Zhang X. Intelligent Monitoring Platform and Application for Building Energy Using Information Based on Digital Twin. Energies 2023, 16, 6839. doi:10.3390/en16196839.

[65] Huang X. Intelligent remote monitoring and manufacturing system of production line based on industrial Internet of Things. Comput. Commun. 2020, 150, 421-428. doi:10.1016/j.comcom.2019.12.011.

[66] Gong Q, Zhang Y, Chen G, Wang H. The intelligent production line configuration strategy. Int. J. Prod. Econ. 2025, 285, 109647. doi:10.1016/j.ijpe.2025.109647.

[67] Yin Z, Xu F, Li Y, Fan C, Zhang F, Han G, et al. A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing. Sensors 2022, 22, 1555. doi:10.3390/s22041555.