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Extension data mining method for improving product manufacturing quality

机译:扩展数据挖掘方法,提高产品制造质量

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It is more and more important to improve the market competitiveness of enterprises by providing high-quality products meeting the market demand. However, the product manufacturing process of most manufacturers is not perfect, and a variety of product quality management methods are mixed with each other, which leads to the urgent need to reduce the nonconforming product rate produced by many production lines and the inability to accurately capture the reasons why the good rate is not up to standard. Customer satisfaction gradually decreases with the development of personalized times. The rapid accumulation of big data in the Internet environment has brought new opportunities for the study of the details of the production process.Extenics uses formal models to describe the production situation and operation mode of each process in the manufacturing process.This paper uses the extension data mining method to determine the factors that affect the product’s qualified rate by collecting the data on each process of the production line, and by extension transformation, the qualified rate of the product is obviously improved.The simulation results show that this kind of extension data mining method provides a new idea for enterprises to improve the product manufacturing quality and product qualification rate, which is helpful for enterprises to realize a more perfect quality management system.
机译:通过提供满足市场需求的高质量产品来提高企业的市场竞争力变得越来越重要。然而,大多数制造商的产品制造过程并不完美,并且各种产品质量管理方法相互混合,导致迫切需要减少许多生产线产生的不合格品率以及无法准确捕获合格率达不到标准的原因。随着个性化时代的发展,客户满意度逐渐降低。 Internet环境中大数据的快速积累为研究生产过程的细节提供了新的机会。可扩展性使用形式化模型来描述制造过程中每个过程的生产情况和操作模式。通过数据挖掘的方法,通过收集生产线各工序的数据,确定影响产品合格率的因素,并通过扩展转换,明显提高了产品合格率。仿真结果表明,这种扩展数据挖掘方法为企业提高产品制造质量和产品合格率提供了新思路,有助于企业实现更加完善的质量管理体系。

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