Modeling and Optimizing Process/Product Behavior using Design of Experiments
Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, but it also inhibits the ability to understand and model how multiple variables interact to jointly affect a response. Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation. Design of Experiments has numerous applications, includingFast and Efficient Problem Solving (root cause determination)Shortening R&D EffortsOptimizing Product DesignsOptimizing Manufacturing ProcessesDeveloping Product or Process SpecificationsImproving Quality and/or ReliabilityThis webinar will review the key concepts behind the Design of Experiments. A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented. Many common types of experiments and their applications are presented. These include experiments appropriate for screening, optimization, mixtures/formulations, etc. Several important techniques in experimental design (such as replication, blocking, and randomization) are introduced. A Case Study involving optimizing a manufacturing process with multiple responses is presented.Learning ObjectivesThis webinar will cover several DOE topics includingStructured Experimentation (DOE)DOE Approach / MethodologyTypes of Experimental Designs and their ApplicationsDOE TechniquesDeveloping Predictive ModelsUsing Models to Develop Optimal SolutionsCase StudyTopic Background Learn a methodology to perform experiments in an optimal fashionReview the common types of experimental designs and important techniquesDevelop predictive models to describe the effects that variables have on one or more responsesUtilize predictive models to develop optimal solutionsWho Should AttendProduct development personnelQuality personnelManufacturing personnelLab personnelR&D personnel
Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, but it also inhibits the ability to understand and model how multiple variables interact to jointly affect a response. Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.
Design of Experiments has numerous applications, including
- Fast and Efficient Problem Solving (root cause determination)
- Shortening R&D Efforts
- Optimizing Product Designs
- Optimizing Manufacturing Processes
- Developing Product or Process Specifications
- Improving Quality and/or Reliability
This webinar will review the key concepts behind the Design of Experiments. A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented. Many common types of experiments and their applications are presented. These include experiments appropriate for screening, optimization, mixtures/formulations, etc. Several important techniques in experimental design (such as replication, blocking, and randomization) are introduced. A Case Study involving optimizing a manufacturing process with multiple responses is presented.
Learning Objectives
This webinar will cover several DOE topics including
- Structured Experimentation (DOE)
- DOE Approach / Methodology
- Types of Experimental Designs and their Applications
- DOE Techniques
- Developing Predictive Models
- Using Models to Develop Optimal Solutions
- Case Study
Topic Background
- Learn a methodology to perform experiments in an optimal fashion
- Review the common types of experimental designs and important techniques
- Develop predictive models to describe the effects that variables have on one or more responses
- Utilize predictive models to develop optimal solutions
Who Should Attend
- Product development personnel
- Quality personnel
- Manufacturing personnel
- Lab personnel
- R&D personnel
