Your Reliability Engineering Professional Development Site
Institute of Quality and Reliability (IQR) was established in Nov. 2005, with primary objective to help Industries for improving their quality and business performance. IQR also supports individuals aspiring to improve their knowledge and skills in Quality and Reliability.
This article series (videos) is dedicated to all those who want to learn and contribute to quality and reliability improvement to make this world better! Videos are created by Hemant Urdhwareshe and team. Hemant is a Fellow of American Society for Quality (ASQ) and is certified by ASQ as Six Sigma Master Black Belt, Reliability Engineer (CRE), Quality Manager (CMQ/OE) and Quality Engineer (CQE). He has 42 years of experience and wants to share his knowledge and experience with those who wish to acquire it!
In this video, Hemant Urdhwareshe explains how to use Hazard Plotting to estimate parameters of Weibull Distribution using Excel with an application case study. Hemant also explains related mathematical concepts in Hazard plotting. Hemant is a Fellow of ASQ, and is certified by ASQ as CRE, CMBB, CSSBB, CQE and CMQ/OE. Viewers may like to watch our previous video on Weibull Probability Plotting.
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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!
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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.
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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.
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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.
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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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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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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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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:
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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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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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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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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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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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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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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