摘要
制造业产业是一个信息化和工业化高度融合的产业。制造业生产过程的每一个环节都会积累大量的数据。而在关于生产产品质量抽测的过程中,当发现质量不佳的产品,若要修正,通常却为时已晚。因此,对生产信息进行数据挖掘,由机器生产参数去预测产品的质量可以及时、全面地知晓生产结果,并且根据预先得到的结果做出对应的决策可以有效地提高产品的质量。XGBoost算法是一种高效准确的回归算法,本文将XGBoost算法应用于制造业质量预测中,从而实现了准确预测产品质量的目的,为制造业生产产品质量预测提供了一种有效的方法。
Manufacturing industry is a highly integrated information and industrialization industry. Every link of the manufacturing process accumulates a lot of data. In the process of detecting the products quality,when finding poor quality products,it is usually late to fix. Realizing data mining for production information,predicting product quality by machine production parameters could master the results of the production in a timely and comprehensive way,therefore make corresponding decisions based on pre-know results to effectively improve product quality. XGBoost algorithm is an efficient and accurate regression algorithm. In this paper,XGBoost algorithm is applied to the manufacturing quality prediction,so as to achieve the purpose of accurately predicting the product quality and provide an effective method for manufacturing product quality prediction.
出处
《智能计算机与应用》
2017年第6期58-60,共3页
Intelligent Computer and Applications
关键词
制造业
大数据
质量预测
回归
manufacturing
big data
quality prediction
regression
