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Evaluation model and algorithm of intelligent manufacturing system based on pattern recognition and big data


With the development of the big data era, the manufacturing industry is focusing on integrating the technologies related to the evolving intelligent industry. Intelligent manufacturing is the latest form of advanced manufacturing development. In order to pay attention to the design and evaluation status of manufacturing system, we understand the connotation of manufacturing system from the perspective of actual manufacturing production and sales. In this paper, the evaluation model and algorithm of Intelligent Manufacturing System Based on pattern recognition and big data are proposed. In the process of studying the information transmission of intelligent manufacturing system, the evaluation hierarchy algorithm of intelligent control system is improved with reference to several modeling methods. Finally, an intelligent manufacturing system suitable for processing big data is designed. In the experiment in workshop A, with the help of computer simulation of artificial intelligent activities, the intelligent manufacturing system analyzes the processing and production data, obtains the dynamic requirements of products in the machining workshop, and provides judgment basis for the construction of dynamic units in enterprises. The experimental results show that after the change of the production organization model using the seasonal change forecasting method, the introduction of the smart manufacturing system, the sales production of this shop has been increasing, and in the 20th month has increased seven times more than the initial, while the overall situation of the predicted sales and actual sales and costs, the superiority of using the smart manufacturing evaluation model is derived.

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Data sharing does not apply to this article because no data set was generated or analyzed during the current research period.


  • Adewuyi AA, Hargrove LJ, Kuiken TA (2016) An analysis of intrinsic and extrinsic hand muscle EMG for improved pattern recognition control. IEEE Trans Neural Syst Rehab Eng 24(4):485–494

    Article  Google Scholar 

  • Aldosary A, Rawa M, Ali ZM et al (2021) Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm. Neural Comput Appl 33:10005–10020

    Article  Google Scholar 

  • Bai Y (2017) Modeling analysis of intelligent manufacturing system based on SDN. Concurr Pract Exp 29(24):42701–42707

    Google Scholar 

  • Cao X (2016) Self-regulation and cross-regulation of pattern-recognition receptor signalling in health and disease. Nat Rev Immunol 16(1):35–37

    Article  MathSciNet  Google Scholar 

  • Cheng X, Ystein E, Hetron M (2016) De Novo transcriptome analysis shows that SAV-3 infection upregulates pattern recognition receptors of the endosomal toll-like and RIG-I-like receptor signaling pathways in macrophage/dendritic like to-cells. Viruses 8(4):114–117

    Article  Google Scholar 

  • Donno D, Boggia R, Zunin P et al (2016) Phytochemical fingerprint and chemometrics for natural food preparation pattern recognition: an innovative technique in food supplement quality control. J Food Sci Technol 53(2):1071–1083

    Article  Google Scholar 

  • Guo D, Lyu Z, Wei W, Zhong RY, Rong Y, Huang GQ (2022) Synchronization of production and delivery with time windows in fixed-position assembly islands under graduation intelligent manufacturing system. Robot Comput Integr Manuf 73:102236

    Article  Google Scholar 

  • Jokic A, Petrovic M, Miljkovic Z (2022) Semantic segmentation based stereo visual servoing of nonholonomic mobile robot in intelligent manufacturing environment. Expert Syst Appl 190:116203

    Article  Google Scholar 

  • Kaur D, Patiyal S, Sharma N, Sadullah Usmani S, Raghava GPS (2019) PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands. Database J Biol Databases Curation 2019:76

    Google Scholar 

  • Lachapelle ER, Ferguson SA (2017) Snow–pack structure: stability analyzed by pattern-recognition techniques. J Glaciol 26(94):506–511

    Article  Google Scholar 

  • Lin B, Wang G, Chen Z et al (2017) Intelligent manufacturing executing system of heat treatment based on internet of things. Heat Treat Metals 42(3):195–197

    Google Scholar 

  • Maffezzoni P, Bahr B, Zhang Z et al (2017) Oscillator array models for associative memory and pattern recognition. IEEE Trans Circuits Syst I Regular Pap 62(6):1591–1598

    Article  MathSciNet  MATH  Google Scholar 

  • Meng G, Cong W, Zhu C (2016) Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Process 7273:92–104

    Google Scholar 

  • Pacaux-Lemoine MP, Trentesaux D, Rey GZ et al (2017) Designing intelligent manufacturing systems through human-machine cooperation principles: a human-centered approach. Comput Ind Eng 111:581–595

    Article  Google Scholar 

  • Paeschke A, Possehl A, Klingel K et al (2016) The immunoproteasome controls the availability of the cardioprotective pattern recognition molecule Pentraxin3. Eur J Immunol 46(3):619–633

    Article  Google Scholar 

  • Qian J, Zi B, Wang D et al (2017) The design and development of an omni-directional mobile robot oriented to an intelligent manufacturing system. Sensors 17(9):2073–2075

    Article  Google Scholar 

  • Sansone M, Fusco R, Pepino A et al (2016) Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. J Healthc Eng 4(4):465–504

    Article  Google Scholar 

  • Sierra-Perez J, Torres-Arredondo MA, Gueemes A (2016) Damage and nonlinearities detection in wind turbine blades based on strain field pattern recognition. FBGs, OBR and strain gauges comparison. Compos Struct 135:156–166

    Article  Google Scholar 

  • Uchikawa E, Lethier M, Malet H et al (2016) Structural analysis of dsRNA binding to anti-viral pattern recognition receptors LGP2 and MDA5. Mol Cell 62(4):586–602

    Article  Google Scholar 

  • Vasudevan H, Kottur V, Raina AA (2019) Lecture notes in mechanical engineering proceedings of international conference on intelligent manufacturing and automation(ICIMA 2018)||Experimental performance and analysis of domestic refrigeration system using nano-refrigerants 35):389–399

  • Wang J, Zhang L, Duan L et al (2017) A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. J Intell Manuf 28(5):1125–1137

    Article  Google Scholar 

  • Wang B, Li B, Yang J et al (2019) Simulation and monitoring of a 6R industrial robot for intelligent manufacturing. Harbin Gongcheng Daxue Xuebao J Harbin Eng Univ 40(2):365–373

    Google Scholar 

  • Wang M, Zhang Z, Li K et al (2020) Research on key technologies of fault diagnosis and early warning for high-end equipment based on intelligent manufacturing and Internet of Things. Int J Adv Manuf Technol 107(3):1039–1048

    Article  Google Scholar 

  • Zhang Y, Zhou G, Jin J et al (2016) Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition. Neurocomputing 225:103–110

    Article  Google Scholar 

  • Zhu K, Joshi S, Wang QG et al (2019) Guest editorial special section on big data analytics in intelligent manufacturing. IEEE Trans Ind Inf 15(4):2382–2385

    Article  Google Scholar 

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This research is supported by School Level Talent Fund of Hefei University in 2020 (20RC12), Major scientific and technological projects of Anhui Province (201903a05020033), Anhui Provincial Natural Science Foundation (1908085QF270), the Support Program Project for Excellent Youth Talent in Higher Education of Anhui Province (gxyq2020065).

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YG, QQ and WZ involved in writing and editing. Yun Wei and Wei Li involved in data analysis.

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Correspondence to Weitang Zhang.

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Communicated by Deepak kumar Jain.

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Guo, Y., Qin, Q., Zhang, W. et al. Evaluation model and algorithm of intelligent manufacturing system based on pattern recognition and big data. Soft Comput 27, 4195–4208 (2023).

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  • Pattern recognition
  • Big data
  • Intelligent manufacturing system
  • Evaluation model