Desktop Engineering Blog

Coping with Large Strain and Large Deformation in FEA

One of the challenges of analysing the performance of large strain materials like rubbers and synthetic elastomers is how the finite element mesh distorts as the part deforms.  You may well start out with a lovely mesh where all your elements meet your quality standards, but as the part distorts the element quality gets worse and worse until it can actually prematurely end the analysis because of excessive distortion, let alone give you poor results.

This is not an uncommon problem. 

In a previous article I discussed using random analysis to predict failure of components in a vibration environment.  Random analysis is a quick way of ensuring that statistically the maximum stress due to a vibration loading will not exceed a set level, but the most common mode of failure in such an environment is not due to one single load spike but due to the summation of damage from all the load cycles – known as fatigue failure.

Classically fatigue failure due to a transient load was performed quasi-statically.  A known load history was combined with stresses from a unit load in the FEA model to create a stress time history.  Rainflow cycle counting was used to evaluate the stress cycles and then damage calculated from these using classical theories like Goodman and summed using Miner’s rule.  There were problems with this method though.

The use of FEA to design ‘optimal’ components has been around for nearly two decades.  In general terms it works by meshing an available volume for a part and then eating away at the space iteratively to leave just those bits of the mesh that are doing work while aiming at a target mass for the part, as in the examples below.

Using this method ‘raw’ it is easy to see how un-manufacturable designs can result, so much effort has been invested by software developers to place manufacturing constraints on the optimisation process to, for example, eliminate voids or undercuts in moulded parts.

PLM was supposed to be a natural evolution, taking advantage of the information associated within 3D data to then flow into all downstream activities.  PLM was created (by Dassault Systemes) around 20 years ago and the process was adopted industry-wide.

As you may be aware the industry standard definition of Product Lifecycle Management (PLM) is the process of managing the entire lifecycle of a product from inception to its retirement. PLM encompasses people, data, processes and business systems that providing the backbone for companies and their extended enterprise.

While nobody doubted the opportunities available in utilizing the information for downstream activities, the overall investment, along with necessary changes in company’s culture, and process curtailed its wider acceptance. Based on the above, PLM become the domain of larger companies (OEM’s).

Another term that is now being thrown into the mix, when trying to understand or determine whether CAD on Cloud is a realistic option is the term "Full Cloud"– what does it mean?

Firstly, it needs to be understood is that modern CAD systems deliver a high level of functionality - which in simple terms means lots of icons, buttons, options and ways of doing things. This means lots of lines of code.

Further, the geometry representations created now can create and visualise, to close engineering accuracy, the geometry of parts and assemblies that truly represent what will be manufactured. All this means that the data being created gets bigger.

In an earlier article I talked in general terms about the benefits of using CAE tools to model the physics of manufacturing processes.  In this article I will show a case study example using welding simulation to decide on the best strategy for welding a two-part swing arm together.

The objective is to compare two different clamping schemes with third iteration that uses tack welds to hold the parts together.

The two parts were made of carbon steel, welded along an ~80mm length using arc welding with an estimate energy input of around 2000J per centimetre. 

The simulation tool used for this analysis uses a state-of-the-art multi-physics finite element programme as the underlying solver, but has a custom user interface that speaks to the manufacturing engineer about his process flow and not about the minutia of an FEA job set up.

At Desktop Engineering, we aim to help our customers find ways of doing design or manufacturing quicker and of higher quality using software technologies. One of the approaches we can use is to use a rule based approach to capture knowledge to allow it to be re-used. Rules are those simple set of instructions, something as simple as a cooking recipe, that one follows that determines an outcome. So it is with engineering. Capture those rules and then reuse them in software and then you have design automation.

All you trekkers out there will know this draws from an immortal phrase uttered by Spock to Capt. James Kirk to express something new outside their current experiences.

I have taken the liberty to extend it to refer to the On-Cloud capabilities of CATIA – it’s something much more than a CAD system.

Over the years CAD has now become such a well-known and used term that it is all encompassing – referring to something that is 2D and free, right through to some of the higher end more expensive solutions such as ProEngineer, NX and CATIA.

To label them all as CAD does a disservice to these capabilities.

Working with long duration transient events in a finite element world can be extremely computationally expensive.  If those events are very long, like the wheel hub forces over the lifespan of a vehicle, then it is impossible to simulate.  One technique to overcome this limitation is to use something called Random Loading, or Random Analysis.

If a time signal can be considered properly random then it can be transformed from the time to the frequency domain and is known as a Power Spectral Density plot, or PSD.  A quick check of randomness is that any section of a time history transformed in this way should give the same outcome as the whole signal.  These PSD’s are best thought of as a statistical representation of the amount of energy in the signal as a function of frequency.

There are three key inputs to any finite element analysis process; the geometry representation as a mesh, the materials data and the loads.  The validity of your decisions made from an analysis depends on capturing all of these accurately. 

Accurate loading can be difficult to obtain.  Some years ago we were involved in a project where the loads were provided by an OEM through a chain of suppliers.  The stress results on the assembly indicated a catastrophic failure and no amount of tinkering with geometry and materials could go any way towards mitigating it.