实验设计培训

J. Antony, Tzu‐Yao Chou, Sid Ghosh
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引用次数: 20

摘要

许多工业工程师进行一次一因素(OFAT)实验,以检查过程改进的情况和解决问题的活动。然而,OFAT实验可能被证明是低效和不可靠的,导致错误的最优条件。此外,它们通常主要由“试错”组成,依靠运气、直觉、猜测和经验来取得成功。实验设计(DOE)采用另一种更结构化的方法。DOE是一种强大的技术,用于发现对过程/产品/系统最重要的一组过程或设计变量,然后帮助实验者确定应该在什么水平上设置/保持这些变量以优化性能。为了证明设计实验比传统的OFAT方法更强大,作者使用了一个简单的弹射实验。他们认为,这样的实验可以作为培训工程师和管理人员的有力武器,这些工程师和管理人员可能会被更“预先”的统计方法吓倒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Training for design of experiments
Many industrial engineers perform one‐factor‐at‐a‐time (OFAT) experiments to examine situations of process improvement and for problem‐solving activities. However, OFAT experiments can prove to be inefficient and unreliable, leading to false optimal conditions. Moreover, they often consist largely of “trial and error”, relying on luck, intuition, guesswork and experience for their success. Design of experiments (DOE) takes an alternative, more structured approach. DOE is a powerful technique for discovering a set of process or design variables which are most important to the process/product/system and then assisting experimenters to determine at what levels these variables should be set/kept to optimise performance. In order to demonstrate the power of designed experiments over the traditional OFAT approach, the authors use a simple catapult experiment. They suggest that such an experiment could act as a powerful weapon in the training of engineers and managers who might be intimidated by a more “up front” statistical approach.
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