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Autonomous self-healing mechanism for a CNC milling machine based on pattern recognition

Author

Listed:
  • Hussein A. Taha

    (Polytechnique de Montréal
    Sohag University)

  • Soumaya Yacout

    (Polytechnique de Montréal)

  • Yasser Shaban

    (Helwan University)

Abstract

A sustainable and reliable machining process is the main goal of seeking machine digitization. Artificial Intelligence (AI), and Cyber-Physical System (CPS) combined with Artificial Intelligence are used for process control. This has become more essential in the case of machining of high-cost aerospace materials and critical product specifications. In this paper, a novel self-healing mechanism was developed to recover a CNC machine from producing parts that do not conform, to surface roughness’s specifications. The machine settings are reconfigured autonomously and online to recover from the effect of tool wear and to keep the surface roughness within the design specifications. The proposed self-healing mechanism is based on a pattern recognition algorithm called Logical Analysis of Data (LAD). This algorithm generates patterns that characterize the out-of-specification state, and provides a corrective setting within the recovery patterns of the within-specification state by using various distance approaches. The developed self-healing mechanism is composed of three modules: CPS model of the CNC machine (module 1), classification into, out of, or within-specification states (module 2), and a self-healing controller (module 3) that is activated if the state of out-of-specification is found by module 2. The three modules are software. The current hardware system of the machine is not altered. The proposed self-healing is applicable and integrable to CNC machines with a wide range of machining parameters of feed rate from 20 mm/min to 750 mm/min and spindle speed from 15,000 RPM to 35,000 RPM. To validate the developed mechanism, a deep learning artificial model was developed on physical data to emulate the CNC milling machine in a CPS simulation environment, and test runs were executed. The proposed self-healing mechanism was evaluated under several simulation runs that covered the ranges of CNC machine settings. The measure of performance of the proposed mechanism is the out-of-specification clearing time. The test runs show that the proposed self-healing mechanism was able to clear the out-of- specification state and to recover the within-specification state in less than five seconds, with the best distance metric approach. The results of the time response for each test run are reported.

Suggested Citation

  • Hussein A. Taha & Soumaya Yacout & Yasser Shaban, 2023. "Autonomous self-healing mechanism for a CNC milling machine based on pattern recognition," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2185-2205, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01913-4
    DOI: 10.1007/s10845-022-01913-4
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    References listed on IDEAS

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    1. Gregory W. Vogl & Brian A. Weiss & Moneer Helu, 2019. "A review of diagnostic and prognostic capabilities and best practices for manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 79-95, January.
    2. Ahmed Elsheikh & Soumaya Yacout & Mohamed-Salah Ouali & Yasser Shaban, 2020. "Failure time prediction using adaptive logical analysis of survival curves and multiple machining signals," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 403-415, February.
    3. Yasser Shaban & Mouhab Meshreki & Soumaya Yacout & Marek Balazinski & Helmi Attia, 2017. "Process control based on pattern recognition for routing carbon fiber reinforced polymer," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 165-179, January.
    4. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    5. Jie Yang & Shaowen Lu & Liangyong Wang, 2020. "Fused magnesia manufacturing process: a survey," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 327-350, February.
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