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Parameter Optimization Design for Touch Panel Laser Cutting Process
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DOI：10.1109/TASE.2011.2176488.png)
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摘要
En 中文
Cutting is an essential process in touch panel manufacturing, which concerns the effectiveness of whole touch panel manufacturing process and reliability of final application products. An alternative is laser cutting technology, which is expected to eclipse conventional cutting method in terms of cost and quality. The laser cutting quality of touch panel is determined by the appropriation of value settings of several parameters/factors involved in the complex laser cutting process. However, the parameter/factor adjusting method applied in practice mainly relies on the engineer's experience and trial-and-error experiment, which are un-systematic and susceptible to inefficiency and ineffectiveness. This study proposes a procedure for parameter/factor optimization of the touch panel laser cutting process to obtain maximum cutting quality. The proposed optimization procedure is successfully applied in a real case of laser cutting for projected capacitive touch panel, a type of touch panels using capacitive technology; it gradually becomes mainstream in the newly emerged information appliance marketplace. Results reveal that defect rate of the laser cutting process decreases from 32.6% to 0.3% once the proposed procedure on this paper is implemented, and this achievement outperforms other optimization strategies. Note to Practitioners-This paper is motivated by solving the parameter optimization problem of laser cutting process for touch panel manufacturing. Empiricism or trial-and-error experiments are the adopted strategies for tuning related cutting parameters to overcome the disqualification of touch panel cutting process. However, it requires considerable time and cost, and cannot guarantee to find good parameter settings to ensure the cutting quality. This paper suggests a parameter optimization procedure composed of four stages-important parameter identification by significant test, experiment and data collection via design matrix, predicting model construction by neural network, and parameter optimization using the genetic algorithm-to solve this problem. The result is quite satisfied. The proposed procedure can be replicated easily in different application domains. Please do not hesitate to contact us if you have any question about this paper.
Keyword:
Genetic algorithm
laser cutting
neural network
parameter optimization
touch panel
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