Design of Experiments (DOE) & Screening Design

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  • Private Class Price is Per Day not Per Pax
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  • HDRF Claimable(SBL)

Private Class from

1800/day

(2 Days)

BENEFITS

Upon completion of this program, participants will be able to:

  • Describe the role & fundamentals of Statistical Design of Experiments
  • Understand the usefulness of DOE
  • Understand the steps on planning & designing an experiment.
  • Select the appropriate factorial design and determine if blocking, center points, and replication are necessary.
  • Perform a 7-step analysis & interpretation on the results of the DOE using Minitab software.
  • Perform model diagnostics to assess the "goodness" of the model

INTRODUCTION

This program introduces the participant to statistical Design of Experiments. Participants will be on how to select the design, how to run the experiment, select the model, perform model diagnostics, and perform analysis and interpretation of the results. The main focus of this program is mainly on the 2-level fractional factorial (screening) experiments and 2-level full factorial experiments.

AUDIENCE

Engineers and senior technicians in manufacturing, process, product, device, test, RND & QA areas that are involved in the improvement, characterization and optimization of manufacturing, engineering processes, product design and services through data collection.

METHODOLOGY

This program involves hands-on activities that require the use of computers. Unless your class is scheduled in a computer lab, please plan to bring your laptop computer to class with Minitab software version 16 installed

Pre-Requisite(s):

  • Knowledge in Basic Statistics or equivalent
  • Knowledge in Measurement System Analysis (MSA)
  • Knowledge in SPC & Process Capability
  • Intermediate level skill in the use of MINITAB Statistical Software
INTRODUCTION TO DESIGN OF EXPERIMENTS (DOE)

Objectives

  • Continuous Improvement & Experimentation
  • Traditional Approaches To Experimentation
  • Statistical Design Of Experiments
  • What Is DOE?
  • History Of DOE
SIMPLE LINEAR REGRESSION

Objectives

  • Simple Linear Regression Model
  • Simple Linear Regression Model Using Minitab
  • Model Fit
  • Parameter Estimates
  • Residual Diagnostics & Assumptions
CONCEPTS OF DESIGN OF EXPERIMENTS

Objectives

  • 4 Basic Objectives Of Experiments
  • Sequential Experimentation Strategy
  • A Simple Process Model & Transfer Function
  • Basic Definitions Of Experimentation
  • Model & Model Coefficients
  • Class Exercise #1
DESIGN OF FULL FACTORIAL EXPERIMENTS
  • Objectives
  • 2K Full Factorial Design
  • Class Exercise #2
  • Manual Generation Of 2K Factorial Design
  • Properties Of 2K Full Factorial Design
  • Fundamental Concept Of 2K Full Factorial Design
    • Coding
    • Standard Order
    • Randomization
    • Blocking
    • Center Points
    • Experimental Error
    • Balanced Design
    • Orthogonal
  • Manual Calculation Of Main Effects & Interaction Effects
  • Information Matrix
  • Class Activity #1- Creating 2K Full Factorial Design In Minitab
  • Class Exercise #3
ANALYSIS OF FULL FACTORIAL EXPERIMENTS IN MINITAB

Objectives

  • 7 Steps Analysis & Interpretation Of Full Factorial Experiments
    • Residual Diagnosis & Assumptions
    • Hierarchy Rule
    • Model Fitting
    • Reduced Model
    • Confirmation Runs
    • Confidence Interval
  • Class Activity #2- Analysis Of 2K Full Factorial Experiment In Minitab
  • Class Exercise #4
DESIGN OF FRACTIONAL FACTORIAL EXPERIMENTS

Objectives

  • What Is Fractional Factorial Experiments?
  • Sequential Experimentation Strategy
  • Confounding Effects
  • Properties Of Fractional Factorial Experiments
  • Defining Relations
  • Confounding Alias Design Resolution
  • Class Exercise #5
  • Design Resolution
  • Higher Order Fractional Factorials
  • Class Exercise #6
  • Fractional Factorial Designs
  • Class Activity #3- Generating Fractional Factorial Design In Minitab
  • Class Exercise #7
ANALYSIS OF FRACTIONAL FACTORIAL EXPERIMENTS IN MINITAB

Objectives

  • 7 Steps Analysis & Interpretation Of Fractional Factorial Experiments
    • Residual Diagnosis & Assumptions
    • Hierarchy Rule
    • Model Fitting
    • Reduced Model
    • Confirmation Runs
    • Confidence Interval
  • Class Activity #4- Analysis Of Fractional Factorial Design In Minitab
  • Class Exercise #8
  • Final Class Exercise: Catapult Simulator (1hr)

Program Summary

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