Problems for designers in tolerance stackup analysis

[abhisuri]

Hi all,

I am writing a program for tolerance stackup analysis, and I wanted to know kind of problems designers face with respect to existing softwares currently.

For example, I found out that using GD&T and monte carlo simulations, many ASME 14.5 rules cannot be incorporated. Also the major problems with these packages are that the results depend on the expertise of the user, not just the GD&T specifications.

So what are other problems that need to be addressed.

Thanks a lot!

 

[Mike_In_Plano]

Hello,

I work for a high volume manufacturer, so yield prediction is important. Most guesses can be made RSS, but final work must be based on something more substantial, like Monti-Carlo.
The single largest difficulty we face is having component data. Distributions are hard to come by, and we've spent a lot of time sampling and characterizing.
A tool to aid with the statistics of sampling would make things easier.

Best Regards,

Mike

 

[abhisuri]

Hey Mike,

Thanks a lot for replying!

I guess the best way for yield prediction is Monte Carlo. I will surely incorporate that in my program.

Can you please elaborate the difficulty you are specifying regarding component data? I mean can you give me an example of the problem you are stating, regarding sampling?

As far as distributions are concerned, I guess what we do now is try out various combinations to see which one fits the best. Another alternative is there could be a code which looks at all the data and decide the distribution from its own. Mostly there is a standard machine learning algorithm for it. I'll find out more about this.

Thanks again!

Regards,

Abhi

 

[Mike_In_Plano]

Yes,

Time and again, I've written MathCad routines or Excel files to find fits. When designing filters, this is especially handy. I also have a Nelder-Meade optimizer which calls upon a Spice package to solve complex relationships.

However, the most fundamental, day-to-day tools are based upon statistical modeling of the components. Given insight into the probability density function for any given parameter, the Monti-Carlo method can give a fair approximation of yield data.

In reality, it has two shortcomings:(1) suppliers are resistant to admitting distributions, and (2) errors in the modeling typically give biased results such that a correction often needs to be made.

Collecting a database of component PDFs is the most expensive aspect of using these techniques.

- Mike

 

[alphy]

I understand the importance of tolerance stackup analysis in the design process and the challenges that designers face in this area. One of the main problems with existing software for tolerance stackup analysis is the limited incorporation of ASME 14.5 rules. This can lead to inaccurate results and potential errors in design. Additionally, the reliance on user expertise rather than just GD&T specifications can also lead to discrepancies in the analysis. Another issue that designers face is the lack of flexibility in the software, making it difficult to incorporate complex designs or changes in the manufacturing process. Furthermore, the time and effort required to input all the necessary data and perform the analysis can be a hindrance for designers. It is important for software developers to address these issues and continuously improve their programs to provide more accurate and efficient solutions for tolerance stackup analysis.