Hemant Urdhwareshe — New Contributor

In this video, Hemant Urdhwareshe explains basic concepts of Fractional Factorial Design, Confounding or Aliasing and Resolution of designs. View earlier videos in this series: Introduction, Application, Coded and Uncoded Values, and a Case study.

Hemant is a Fellow of American Society for Quality (ASQ) and is certified by ASQ as Six Sigma Master Black Belt (CMBB), Black Belt (CSSBB), Reliability Engineer (CRE), Quality Engineer (CQE) and Manager of Quality and Organizational Excellence (CMQ/OE).

The video would be useful to all those who wish to learn about use of fractional factorial designs to reduce number of runs in experiments. It will be also be greatly useful for preparing ASQ certification exams!

Hemant Urdhwareshe, Director of Institute of Quality and Reliability presents case study to maximize fatigue strength of crankshaft using design of experiments using SigmaXL Template of Full Factorial Design with three factors each at two levels.

Hemant is Fellow of ASQ and possesses five certifications from ASQ including Six Sigma Master Black Belt and Reliability Engineer. It is recommended that viewers first watch the previous videos DOE-1, DOE-2 and DOE-3 before watching this video. SigmaXL trial version can be downloaded from www.sigmaxl.com.

I am happy to share my third video on Design of Experiments (DOE-3). This is the third video in our series on Design of Experiments. It is recommended that viewers should watch the first two videos (doe-1-introduction-to-design-of-experiments and DOE-2: DOE Case Study of Spot Welding) before watching this video for better learning and understanding.

In this video, we have explained how to convert uncoded values of factor-levels to coded values and vice versa. We have also explained how to create a linear regression equation from the results of a full factorial two-level experiment.

Dear Friends, we hope you have seen our first video on Introduction to Design of Experiments DOE)! Here is my second video on DOE! I have illustrated a case study of DOE application to Spot Welding process! The Case study is about application of Full Factorial Design with three factors, each at two levels. The video assumes that viewers have seen our first video on Introduction to DOE.

Dear Friends, this video is created to provide a simple introduction to Design of Experiments (DOE). DOE is a proven statistical tool and is known to be superior to the conventional approach of One-Factor-at-a-Time of OFAT! In OFAT, one tries to optimize the settings of various factors one by one. But this has a major limitation of not getting information about interaction of the factors! DOE helps us to estimate effects of various factors along with their interactions! This is the first in the series of videos that we wish to create and share in near future. Our future videos on DOE will include practical application examples of DOE, analysis and interpretation, optimization using DOE, Fractional Factorial Designs. We will also introduce later to Response Surface Designs.