Design of Experiments ( DOE ) Training for Engineers

Design of Experiments Training, DOE Training for Engineers.  The 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.

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.

Who should join DOE training ?

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

Learn more:

Design of Experiments Training