主要课程 Courses

试验设计 Design of Experiment

Course: Design of Experiment (DOE)


时长/Duration: 4 day

语言/Language: CN/EN 


Course Introduction

Genichi Taguchi, a famous Japanese quality expert, said, "An engineer who does not understand Design of Experiment is only half an engineer".

Modern Design of Experiment is never the "One Factor At A Time" method that most of our engineers know or often use, which is also called OFAT in English, but a scientific experiment method based on statistics that combines ANOVA and regression methods. This Design of Experiment (DOE) has been widely and effectively applied in mechanical, material, metallurgical, chemical, environmental, medical and other industries.

This course will teach the basic full factorial design, partial factorial design, response surface methodology, robust parameter design, and mixture design, etc., which are needed in production, so that students can better understand and use these tools, then combine with practical exercises to achieve the purpose of learning to use.



日本著名质量专家田口玄一说:“不懂试验设计的工程师只能算半个工程师”。现代试验设计方法绝不是我们大部份工程师认知或常常使用的“一次一因子法”,英文称为OFAT(One Factor At Times),而是结合了方差分析和回归方法的基于统计学的科学的试验方法。而这种试验设计方法在机械、材料、冶金、化工、环境、医学等行业得到了广泛且有效的应用。



Target audience

Technicians, engineers, and management personnel in the field of quality, production and technology.






At the end of the course, students will master the modern Design of Experiment method knowledge and tools for production, including but not limited to the following contents:

• Statistics basics of DOE

• Full factorial design

• Fractional factorial design

• Response surface methodology

• Robust parameter design(incl. Taguchi and Montgomery)

• Mixture design

• Minitab practice for all above method



• 试验设计基础统计 
• 全因子试验设计
• 部分因子试验设计
• 响应曲面设计
• 稳健参数设计(包括田口方法、蒙哥马利方法等)
• 混料设计
• 针对试验设计的蒙特卡洛模拟
• 以上所有方法的Minitab实战操作


Course Outline

Day 1: Basic Statistics for Experimental Design
◦ Standard deviation, normal distribution, t-distribution, F-distribution
◦ Hypothesis testing
◦ Analysis of variance (ANOVA)
◦ Simple linear regression
◦ Introduction and demonstration based on Minitab

Day 2: Full factorial experimental design
◦ Experimental design terminology
◦ Three principles of experimental design
◦ Data analysis process for full factorial experimental design
◦ Case study and practical exercise

• Day 3: Factorial experimental design and response surface methodology
◦ Additional knowledge in full factorial experimental design
◦ Partial factorial design process and case studies
◦ Basic flow of response surface design with case studies

• Day 4: Robust parameter design and mixture design
◦ Taguchi's three design principles
◦ Taguchi's product table model and case study
◦ Montgomery's combinatorial table model with case studies
◦ Mixing design and case study
◦ Monte Carlo simulation


• 第一天:试验设计基础统计

◦ 标准差,正态分布, t分布, F分布
◦ 假设检验
◦ 方差分析
◦ 简单线性回归
◦ Minitab统计分析软件讲解与演示

◦ 试验设计术语
◦ 试验设计三大原则
◦ 全因子试验设计数据分析流程
◦ 实操与案例讲解

◦ 全因子试验设计中的补充知识
◦ 部分因子试验设计流程与案例讲解
◦ 响应曲面设计基本流程与案例讲解

◦ 田口的三次设计原理
◦ 田口方法的乘积表模型与案例讲解
◦ 蒙哥马利的组合表模型与案例讲解
◦ 混料设计与案例讲解
◦ 蒙特卡洛模拟



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