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Hemant Urdhwareshe — New Contributor

Author of the Institute of Quality & Reliability article series. This author's archive lists contributions to articles and episodes.

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DOE-1: Introduction to Design of Experiments

DOE-1: Introduction to Design of Experiments

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.

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Warranty Data Analysis on Minitab

Warranty Data Analysis on Minitab

I am happy to share my next video on ‘Warranty Data Analysis using Minitab Software’. The video explains the popular Nevada format of warranty data collection and how to convert it in to suitable tabular format so that the same can be analysed using Minitab or other software. In this video, we explain the various types of failure data when Nevada format is used. In some of our previous videos, we have already discussed various types of life data i.e. complete, right censored, left censored and interval censored. When all these types of data are present at the same time, we call such data as multiply censored. Such type of data can be analysed on Minitab using the commands for arbitrarily censored data.

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Case Study in Tolerance Design of a Spring using Monte Carlo Simulation

Case Study in Tolerance Design of a Spring using Monte Carlo Simulation

One of the weak areas in designing parts is deciding tolerances of various parts. We have shared a video of statistical tolerancing for assembly of parts. Many viewers have expressed that we should also post a video of application of Monte Carlo Simulation for tolerance design when there is a transfer function that relates the input parameters to an output variable. We therefore present in this video an application example of designing tolerance for a helical spring using Monte Carlo Simulation. The video explains this procedure step-by-step using Simular software. I have used Simular software to demonstrate this with a practical example of spring. Simular is a free software (emailware) which can be downloaded from https://www.simularsoft.com.ar/. However, one can use other software such as Crystal Ball, @Risk etc. for such analysis. Tolerance design is usually an essential step in Design for Six Sigma (DFSS) projects.

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Life Data Analysis of Right Censored data using Minitab Software

Life Data Analysis of Right Censored data using Minitab Software

I am happy to share my next video on ‘Life Data Analysis of Right Censored Data using Minitab Software’ as many viewers had requested! In this video, we revisit the types of failure data and explain procedure to analyse right censored data in Minitab software with an example.

The procedure is predominantly in three steps:

  • 1. Identify the distribution that best fits our data
  • 2. Estimate parameters of the selected distribution
  • 3. Estimate reliability or probability of failure at specified time(s)

The procedure is explained in detail using an application example of camshaft failure data. In the video, I have also explained how to estimate expected number of failures by 100000 Kilometres and also the 95% upper bound which is the worst-case scenario. I am sure, you will find this video interesting and useful for practical application of Life Data Analysis! Your feedback on the video is welcome!

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Statistical Tolerancing using Monte Carlo Simulation

Statistical Tolerancing using Monte Carlo Simulation

One of the weak areas in designing parts is deciding tolerances of various parts. Most engineers are familiar with Arithmetic Tolerance stack up analysis wherein they check impact of maximum and minimum values of various tolerances on assembly of parts. However, this can often result in high manufacturing cost. Thus, it may be more appropriate to analyse tolerances using statistical tolerance stack up approach. This can be performed using Monte Carlo Simulation. In one of the previous videos, I had shown how to predict reliability using Monte Carlo simulation. In this video, I will explain how to perform statistical tolerance stack up analysis using Monte Carlo Simulation. I have used Simular software to demonstrate this with a practical example. Simular is a free software (emailware) which can be downloaded from https://www.simularsoft.com.ar/. However, one can use other software such as Crystal Ball, @Risk etc. for such analysis. Statistical tolerance stack up is usually an integral part of Design for Six Sigma (DFSS) projects. I hope viewers will find this video useful. Feedback is welcome!

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Reliability Prediction using Monte Carlo Simulation

Reliability Prediction using Monte Carlo Simulation

In the last video on stress-strength interference, we have seen the analytical method. This has limitations and often cannot be used in real life problems in reliability prediction. For example, velocity of windmill may have Weibull or lognormal distribution, elevators may have particular application load cycles which can only be modelled using empirical distributions. In such situations, we need to use Monte Carlo Simulation using various other distributions. I will discuss and explain this technique in this video.

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Reliability Prediction using Stress Strength Interference (Analytical Method)

Reliability Prediction using Stress Strength Interference (Analytical Method)

Often, products fail, and we don’t understand why! One of the reasons why such failures occur is not giving consideration for variation in load or stress levels. For example, potholes and speed breakers can create excessive stress on automobile suspensions; or larger number of clothes washed in a machine more often than the designer has considered; or a bus used in public transport would carry varying number of passengers! Designers often do not realize that the materials used in the product will also have variations! For example, wall thicknesses of castings will vary; or chemical composition of steel will vary from lot to lot! Thus, designers need to address the variations in usage patterns and variations in the materials used in the product as these can seriously affect reliability of systems! In my recently uploaded video, I have discussed how to predict reliability when load (stress) and strength of the part both vary and can be modelled using normal distribution. The video will also be very useful to those who wish to take ASQ Certified Reliability Engineer exam and all those who wish to learn the basics of reliability in design. This is the first video where the stress and strength can be modelled using normal distribution. In the next video related to this topic, I will explain reliability prediction using Monte Carlo Simulation.

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Weibull Probability Plotting of Complete Data

Weibull Probability Plotting of Complete Data

This video explains step-by-step procedure for probability plotting of failure data. Probability plotting is a technique used to determine whether given data of failures follows a distribution. The video also explains various types of failure data: complete, right censored, left censored and interval censored data. Viewers can try Weibull Probability Plotting to estimate shape parameter beta and scale parameter Eta. The video also shows how to estimate median rank for complete data.

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Life Data Analysis of Complete Data Using Minitab

Life Data Analysis of Complete Data Using Minitab

In this video, Hemant Urdhwareshe discusses how to analyse complete data of failures to identify distribution, identify distribution parameters, estimate reliability at a specified time using Minitab (version 19) software. The file used in the video can be downloaded here: Reliability Data.xls.zip.

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Correlation and Regression on Casio fx991-MS Calculator and Excel

Correlation and Regression on Casio fx991-MS Calculator and Excel

Dear Quality and Reliability Professionals, this video is created to understand and learn how to use Casio fx991-MS calculator for performing correlation and regression with a simple application example that viewers can try. The video will be useful for all those who want to prepare for exams by American Society for Quality (ASQ).

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