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A closed-loop intelligent adjustment of process parameters in precise and micro hot-embossing using an in-process optic detection

Author

Listed:
  • Kuo Lu

    (South China University of Technology)

  • Jin Xie

    (South China University of Technology)

  • Risen Wang

    (South China University of Technology)

  • Lei Li

    (South China University of Technology)

  • Wenzhe Li

    (South China University of Technology)

  • Yuning Jiang

    (South China University of Technology)

Abstract

In rapid hot-embossing of microarray products, sensors accuracy drifts, mechanical wears and environmental changes produce the nonlinear relationship between micro-forming accuracy and process parameters. Generally, the process parameters need to be adjusted according to ex-situ detection and on-spot experiences, leading to inefficiency. Therefore, an in-process optic detection of micro-forming heights is proposed to closed-loop control the micro-forming accuracy on macro hot-embossed surface. On the base of ex-situ detection data, the in-process detected data are related to micro-forming heights to adjust hot-embossing parameters by intelligent algorithms. The objective is to resolve the uncertainty during precision micro-forming. First, an optic detection was developed to recognize the micro-forming heights on macroscopic workpiece surface in real-time; then artificial neural networks and Naïve Bayes method were adopted to select the initial process parameters; next, the correction algorithm was modeled to perform fine adjustment instead of on-spot experiences, based on the recognized forming heights; finally, this system was applied to the hot-embossing of microprism arrays on light-guide plates. It is shown that the illuminance ratio is related to the hot-embossed microstructure heights. This may be used to in-process detect the micro-forming heights on macro workpiece surface. For the neural networks trained with process parameters, the RBF eliminates nonlinearity-caused local minimization better than the BP. For ambiguous process data, the Naïve Bayes method updates incomplete process parameter database more precisely and timely than neural networks. As a result, the micro-forming height may be controlled within the allowable error band under unstable hot-embossing situations.

Suggested Citation

  • Kuo Lu & Jin Xie & Risen Wang & Lei Li & Wenzhe Li & Yuning Jiang, 2022. "A closed-loop intelligent adjustment of process parameters in precise and micro hot-embossing using an in-process optic detection," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2341-2355, December.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01799-8
    DOI: 10.1007/s10845-021-01799-8
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    References listed on IDEAS

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    1. Young Min Song & Yizhu Xie & Viktor Malyarchuk & Jianliang Xiao & Inhwa Jung & Ki-Joong Choi & Zhuangjian Liu & Hyunsung Park & Chaofeng Lu & Rak-Hwan Kim & Rui Li & Kenneth B. Crozier & Yonggang Huan, 2013. "Digital cameras with designs inspired by the arthropod eye," Nature, Nature, vol. 497(7447), pages 95-99, May.
    2. PoTsang B. Huang, 2016. "An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 689-700, June.
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