Article tagged Monte Carlo reliability modeling

A mathematical technique that generates random variables (time-to-failure distribution when modeling the reliability of a system) for modeling risk or uncertainty of a system. The approach incorporates all available information and relationships including continuous, discrete, and categorical data. It provides a means to aggregate information that may be discrete, continuous, or the output of deterministic and/or stochastic processes.

by Hemant Urdhwareshe Leave a Comment

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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by Hemant Urdhwareshe 2 Comments

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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by Hemant Urdhwareshe 2 Comments

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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