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.
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Acknowledgements
This work was jointly supported by the National Natural Science Foundation of China (51975219), the Science and Technology Planning Project of Guangdong Province (2020A0505100003), the Natural Science Foundation of Guangdong Province (2020A1515010807) and the Fundamental Research Funds for the Central Universities.
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Lu, K., Xie, J., Wang, R. et al. A closed-loop intelligent adjustment of process parameters in precise and micro hot-embossing using an in-process optic detection. J Intell Manuf 33, 2341–2355 (2022). https://doi.org/10.1007/s10845-021-01799-8
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DOI: https://doi.org/10.1007/s10845-021-01799-8