Design of Experiment (RSM)

Introduction

The DOE training seminar begins with the fundamentals of Design of Experiments (DOE) methods and continues with advanced concepts, principles and requirements. Topics include Anova, Full Factorial Designs, Fractional Factorial Designs, robust designs, the Response Surface Methodology(RSM), reliability DOE and Taguchi Design. We will begin with screening design, process characterization and optimization.

Objectives

1. Participants learn to solve problems, improve yields, achieve robust processes and build models for prediction with Design of Experiments (DOE)
2. Response Surface Methodology (RSM) and Multiple Regression Analysis
3. The training course presents concepts of DOE and RSM and Minitab could be use to help your organization:

• RSM is the extension of DOE
• Analyze experimental results in order to identify the significant factors and evaluate ways to improve and optimize the design.

Methodology

Lecture, Discussion & Case Studies

Course Content

Baselining Data Collection

It is considered passive observation. The process is monitored and recorded without intentional changes or tweaking. In Designed Experiments, the independent variable (Response) is observed. Designed experiments are used to:

• Determine which factors (X’s) have the greatest impact on the response (Y)
• Quantify the effects of the factors (X’s) on the response (Y)
• Prove the factors (X’s) you think are important really do effect the process

DOE Analysis

Analysis of DOE’s includes both graphical and tabular information. It includes Pareto Analysis, Anova, Main Effects, Interactions analysis. It also include Cube Plots, Contour Plots and Optimization Plot, etc.

Response Surface Methodology (RSM)

Response Surface analysis is a type of Designed Experiment that allows investigations of non-linear relationships. It is a tool for fine tuning process optimization once the region of optimal process conditions is known. Using the CCD type RS Design, you will be designing an experiment that test each factor at five levels, and an experiment which can be used to augment a factorial experiment that has been completed. The CCD design will include Factorial points, STAR points, and CENTER Points.

Target Audience

1. Engineering Manager / Executive / Supervisor/Engineers
2. Process Improvement Managers / Process Engineers
3. QC/QA Manager / Executive / Engineers
4. Personnel involved in Quality Control & Improvement projects