Design of Experiments, in general, is a useful method for solving problems, optimizing, designing products, and manufacturing and engineering. Design of Experiments is applied for root cause quality analysis, developing optimized and robust designs, and producing analytical and mathematical models to forecast the system behavior.
After going through the Design of Experiments training participants will understand and learn about:
- Learn how to use designed experiments to achieve breakthrough improvements in process efficiency and quality.
- Understand the fundamentals and advantages of Robust DoE.
- Learn how to conduct DoE analysis in the popular statistical analysis program, Minitab.
- Understand how to design and problem-solving teams can apply DOE results to make wise decisions.
- Learn how to determine which Taguchi design to use for your application.
- Understand how to evaluate which process inputs have a significant impact.
Who Should Attend
- Senior Management
- Quality Managers & Engineers
- Quality Control Technicians
- R&D Managers
- Product Engineers
- Process Engineers
- Design Engineers
Benefits of Attending
The design of experiment technique is incredibly powerful when working with new products, new technologies, or when migrating an existing technology into a new application. It is also very helpful for identifying the critical few parameters that will drive the performance of the product, process, or system. By the end of the course, you will know what the keys to a successful DOE analysis are, and you will be able to conduct a Full Factorial DOE and a Fractional Factorial DOE.
Design of Experiments training course offers the following benefits:
- Design experiments to identify the optimum combination to minimize cost and increase quality and productivity.
- Improving process or product “robustness”.
- Analyze and interpret the results from the partial factorial DOE.
- Recognize the main principles and benefits of Robust Design DOE.
- Improve the quality of products by optimizing the variables.
- Reduce material waste due to frequent rejects on the product resulting from quality problems.
- Increase productivity by reducing quality problems.
- Reduce the cost of production due to the ability to detect quality problems faster.
1. Introduction to DOE
– Why Experiment?
– Components of an Experiment
– What are Primary and Surrogate Responses
– What and Why DOE
– Where DOE Fits with other Analysis Methods
2. Randomization and Design
– Introduction Randomization Against Confounding
– Randomizing Other Things
– Performing a Randomization
– Randomization for Inference (The paired t-test, Two-sample T-test Randomization inference, and standard inference)
– Comparing Models: The Analysis of Variance, Mechanics of ANOVA, Why ANOVA Works
3. Factorial Treatment Structure
– Factorial Structure
– Factorial Analysis: Main Effect and Interaction
– Advantages of Factorials
– Visualizing Interaction
– Models with Parameters
– The Analysis of Variance for Balanced Factorials
– General Factorial Models
– Assumptions and Transformations
– Single Replicates
– Pooling Terms into Error
– Contrasts for Factorial Data
4. Factorial Treatment Structure
– Modeling Interaction(Interaction plots, One-cell interaction, Quantitative factors, Tukey one-degree-of-freedom for non-additivity)
– Factorial Treatment for Unbalanced Data, Two-Series Factorials
5. Fractional Factorials
– Why Fraction?
– Fractioning the Two-Series
– Analyzing a 2k−q
– Resolution and Projection
– Confounding a Fractional Factorial
– Sequences of Fractions
– Fractioning the Three-Series
– Problems with Fractional Factorials
– Using Fractional Factorials in Off-Line Quality Control
6. Practical Exercise on Minitab Response Surface Designs
– Visualizing the Response
– First-Order Models
– First-Order Designs
– Analyzing First-Order Data
– Second-Order Models
– Second-Order Designs
– Second-Order Analysis
– Mixture Experiments
– Discussion on Case Study
Feedback From Past Participants
This workshop helped us to understand the difference between factors, levels, and structure within a Design of Experiments.
This workshop had knowledge about how to analyze experimental results to identify the significant factors and evaluate ways to improve and optimize the design.
The trainer guidance has helped us to understand what a DOE is and how to create, execute, and interpret the results.