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

Kuo Lu, Jin Xie (), Risen Wang, Lei Li, Wenzhe Li and Yuning Jiang
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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

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 8, No 10, 2355 pages

Abstract: 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.

Keywords: Intelligent control; Naïve Bayes method; Micro hot-embossing; In-process detection (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-021-01799-8

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