4 Ways Excel is Failing Your Tolerance Analysis

Excel is a useful tool for a lot of applications, and can be used in a pinch for stack analysis, but it misses a lot of important features.

Excel can be used a for a lot of equation based applications. Many companies rely on it for their tolerance analysis; doing 1D stacks and stack-up analysis using Excel macros and Visual Basic code. For basic cases, this works great and can be a fast and effective way to get answers quickly, but as soon as products, models and requirements become more complex, Excel falls flat.

Integrated CAD tools give more accurate analysis results by incorporating additional influences

This means that your Excel answers might be misleading you, and can cause some embarrassing, and expensive, problems later on.

In what ways is Excel failing?

Here are 4 ways Excel is failing your tolerance analysis:

1. No 3D Influence

It is rare that all the parts in your product line up into a perfect linear line. Most often, there are parts arranged around your stack-up, and these part's tolerances will influence your linear stack. This 3 dimensional influence can cause more variation than you'd expect, and a 1D stack-up in Excel is not going to account for this.

Even with a linear stack-up, you can have angularity that will cause additional variation, or variation and issues unaccounted for in your Excel stack. These 3 dimensional issues are related to the geometry of the parts, and require a CAD-based approach to properly simulate and validate.

Not all scenarios can be calculated with an Excel 1D stack-up

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Let's Talk Worst Case Tolerance Analysis

Worst Case Tolerance Analysis is important to many industries. Used for critical measures, Worst Case has an important place in tolerance analysis.

Part 4: 3DCS V7.5 – 3DCS V7.5 – How and Why to Use Worst Case Analysis in 3DCS

Thu, Apr 26, 2018 11:00 AM - 12:00 PM EDT -- Learn how to handle Worst Case

How does 3DCS handle Worst Case?

Let's first review what Worst Case is in this context.

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Your Monte Carlo Analysis Results Give Actionable Information - How Do You Share and Use That Data in a Meaningful Way?

Monte Carlo Analysis, using Monte Carlo simulation with tolerance variables and assembly processes, provides a lot of powerful outputs. Determining percent out of specification, critical features to quality, contributing parts and tolerances to out-of-spec scenarios, and more. These outputs can be used to reduce non-conformance in production, and thereby reduce scrap, rework and overall manufacturing costs.

As anyone who has worked in the simulation environment knows, that's the goal. However, getting there isn't always easy. Most teams responsible for creating simulations then provide that data to managers, quality professionals, plant managers, designers and other teams that have to use that data to make changes and decisions.

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