Length: 2 Days
Design of Experiments Training, DOE Training for Engineers
Design of Experiments Training, DOE Training for engineers course is designed to teach you both theory and hands-on requirements necessary to run and execute the DOE.
DOE or Design of Experiments is sometimes called a Statistically Designed Experiment. DOE is a considered to be a strategically planned and executed experiment to provide detailed information about the effect on a response variable due to one or more factors: One–Factor–at–a–Time (or OFAT).
DOE in general is a useful method to solving problems, optimizing, designing products, and manufacturing and engineering. In particular, DOE is applied for root cause quality analysis, developing optimized and robust designs, and producing analytical and mathematical models to forecast the system behavior. The DOE training for engineers seminar will provide you a combination of theory, discussion, and practical material to help you feel comfortable and fluent in executing the DOE.
Why TONEX’s Design of Experiments Training? DOE Training Course Description
The DOE training course for engineers will teach you what design of experiment to choose, how to execute the DOE, and how to analyze the DOE results. You also will get a chance to analyze different case studies and analyze them on paper and on the computer.
To sum up, through the DOE training course for engineers, you will gain sufficient knowledge and skills on how to design, perform, and analyze experiments in the industrial scales. You will learn about the principals of DOE and that how it is applied to improve the quality and efficiency of projects.
Audience
The Design of Experiments Training, DOE Training is a 2-day course designed for:
- Quality managers and engineers
- SPC coordinators
- Quality control technicians
- Consultants
- R&D managers, scientists, engineers, and technicians
- Product and process engineers
- Design engineers
Training Objectives
Upon the completion of this seminar, the attendees are able to:
- Develop and apply necessary skills required later to solve, design, or optimize more complex problems or multiphase systems
- Perform a full DOE test matrix, in both randomized and blocked way
- Build a model
- Run a DOE to solve problems
- Run a DOE to optimize a system
- Analyze and interpret the DOE results, using ANOVA or graphical methods whichever is relevant
- Understand and perform analysis for experiments: main and interactive effects, experimental error, normal probability plots, identification of “active” efforts, and residual analysis.
- Recognize what parameters have the most impact on the quality of a product or the productivity of a process
- Set up a partial factorial DOE by applying confounding principal
- Analyze and interpret the results from the partial factorial DOE
- Understand the fundamentals and advantages of Robust DOE
- Decide when a Response Surface DOE needs to be executed
- Pick the relevant Response Surface Design
- Analyze and interpret the results of Response Surface
Course Outline
Overview of Design of Experiments
- What is DOE?
- Elements of an experiment
- Elements of the scientific methodology
- How to incorporate the scientific method into an experiment
- How much data is enough for an experiment?
- Determine various features of the design of experiments method
- Experimental geometry
- Response mapping
- Relationship between the principals of a DOE with the definitions associated with it
- What are the advantages of using DOE compared to conventional experimentation methods
- Steps to design an experiment
- Full Factorial Experiments using Cube Plots
- Minitab introduction
Planning a DOE
- Determining the quality of an experiment
- Defining the objectives of an experiment
- Determining the effective variables
- Determining the weight of each variable
- Identifying, defining, and categorizing independent variables of an experiment
- Eliminating unnecessary variables
- Recognizing additional elements necessary to design an experiment
Problem Solving With DOE
- The process of problem solving in DOE
- Multistep processes DOE to confirm the DOE results
Analyzing The DOE Results
- How to use ANOVA table to test a theory
- How to perform a t-ratio test
Various Categories of DOEs
- Fully randomized design
- Fully randomized block form
- Partially randomized block form
- Latin Square design
Various Categories of DOEs
- Complete factorial design
- Fractional (partial) factorial design
Confounding
- The Confounding Principle
- The advantages and disadvantages of confounding compared to partial factorial experiments
- How confounding can happen in a DOE?
- Generators and ‘Design Resolution’ importance of the “’Alias String’
- How to perform partial factorial experiments using default generators and by specifying generators
The Robust/Taguchi DOE
- Where is Robust/Taguchi relevant?
- How Robust/Taguchi is different?
- Taguchi applications
- How to set up a Taguchi DOE in Minitab
The Response Surface DOE
- Where is Response Surface relevant?
- How Response Surface is different?
- How to set up a Response Surface DOE in Minitab