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首页> 外文期刊>International Journal of Production Research >A Prediction Of The Dielectric Constant Of Multi-layerceramic Capacitors Using The Mega-trend-diffusion Technique In powder Pilot Runs: Case Study
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A Prediction Of The Dielectric Constant Of Multi-layerceramic Capacitors Using The Mega-trend-diffusion Technique In powder Pilot Runs: Case Study

机译：粉末中试中大趋势扩散技术对多层陶瓷电容器介电常数的预测

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摘要

The fast progress of technology has made a product's life cycle shorter and shorter. Transferring experience gained from pilot runs to mass production rapidly has thus become an important issue for enterprises. Neural networks are one of the learning models widely applied to implement this task. However, neural networks generally require a great amount of training data to establish the learning model, which is difficult to collect in the early stages of a manufacturing system. Therefore, in this paper, for cases when the collected data is insufficient, a procedure proposed by Li et al. (Li, D.C., Wu, C.S., Tsai, T.I. and Lin, Y.S. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Comput. & Oper. Res., 2009, 34, 966-982), called mega-trend-diffusion, is applied to make the forecast more precise. This paper takes the multi-layer ceramic capacitor as an object of study, and applies the procedure to the pilot runs of production to create a robust model of the process to shorten the lead-time for mass production. The results reveal that it is possible to rapidly develop a model of production with limited data from pilot runs.
机译：技术的飞速发展使得产品的生命周期越来越短。因此，迅速将从试运行中获得的经验转移到批量生产中已成为企业的重要问题。神经网络是广泛用于执行此任务的学习模型之一。然而，神经网络通常需要大量的训练数据来建立学习模型，这在制造系统的早期阶段很难收集。因此，在本文中，对于收集的数据不足的情况，Li等人提出了一种程序。 （Li，DC，Wu，CS，Tsai，TI和Lin，YS在小数据集学习中使用大趋势扩散和人工样本，以获取早期灵活的制造系统调度知识。Computer。＆Oper。Res。，2009，34， 966-982）（称为大趋势扩散）被用于使预测更加精确。本文以多层陶瓷电容器为研究对象，并将该程序应用于试生产，以创建一个可靠的过程模型，以缩短批量生产的交货时间。结果表明，有可能使用有限的试运行数据快速开发生产模型。

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