Practical Design of Experiment (DoE) for Engineers and Researchers
About This Course
This course, Practical Design of Experiment (DoE) for Engineers and Researchers, equips participants with essential skills to design, analyze, and optimize experiments for improved process performance. Participants will gain a thorough understanding of DoE fundamentals, factorial experiments, and how to apply ANOVA to assess the significance of variables. By learning how to identify key factors affecting performance, participants will be able to confidently select appropriate DoE projects and execute them with precision.
The course covers advanced topics like fractional factorial designs, screening methods, and modeling techniques such as Taguchi and Response Surface Methodology (RSM). By the end of the course, learners will be able to evaluate the effectiveness of their DoE projects and make data-driven recommendations for continuous process improvement.
What You'll Learn
LO1: Analyze process interactions to characterize key factors influencing performance in Design of Experiments (DoE).
LO2: Select suitable factorial DoE projects by evaluating relevant performance metrics.
LO3: Define the scope and execute fractional factorial DoE projects using problem-solving techniques.
LO4: Evaluate the effectiveness of DoE projects and recommend follow-up actions
Course Outline:
Topic 1: Fundamentals of Design of Experiment
• Introduction to Design of Experiment (DoE)
• Dependent and Independent variables
• Purpose of DoE
• Stages of DoE
• Factor, Level and Treatment
• Introduction to single factor experiments
• One-Way Analysis of Variance (ANOVA)
• Decomposition of the Sum of Squares
Topic 2: Factorial DoE
• Introduction to Factorial DoE
• Main Effects and Interactions between factors
• Why using Factorial DoE
• Two-Factors Two-Levels (2^2) DoE
• Regression equation for 2^2 DoE
• 2^2 experiment with Interactions
• Regression model for 2^2 DoE with Interactions
• Analysis of Variance (ANOVA) of 2^2 DoE
• Adding the third factor – 2^3 DoE
• ANOVA of 2^3 DoE
• Regression model for 2^3 DoE
• General 2^k DoE
• Analysis procedure of any 2^k DoE
• Blocking a replicated design
• Analysis a 2^k DoE with blocks as replicates
• Confounding a 2^k DoE in blocks
Topic 3: Fractional Factorial DoE
• Introduction to Fractional Factorial DoE
• One-Half fraction designs
• Confounding in partial factorial design
• Design resolution
• ANOVA of fractional DoE
• One-Quarter fraction designs
Topic 4: Screening, Modeling and Optimizing DoE (
• Screening designs
• Plackett Burman design
• Taguchi design
• Response Surface Method (RSM)
• Central Composite Design (CCD)
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum Polytechnic Diploma
• Basic programming skill, preferably Python.
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.