Tuesday, 18 June 2013

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.


Objective
             
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

1. Baselining data collection 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

2. 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.

3. 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



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