Can users of today’s advanced simulation methods for Automotive still learn from sentiments expressed 150 years ago?

On two occasions I have been asked, “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” … I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

So said Charles Babbage, widely considered the father of the computer, way back in 1864.  He was obviously right – but are we still guilty of the confusion he identifies?


Inputs to Automotive simulation

Today’s Automotive industry makes huge investments in simulation to reduce the use of physical prototypes. It’s suggested around 80% of the problems uncovered with physical vehicle prototypes could have been identified earlier in the design process. With prototypes costing around the $1m mark, it’s clear the more issues uncovered within simulation, the better.

Jaguar Land Rover, for example, saved money by releasing their Jaguar EX saloon model without producing a single physical prototype during the aerodynamic engineering process and the company intends to eliminate all physical prototypes from its process by 2020 – only two years from now.

So, simulation is undoubtedly valuable but, remembering Babbage, do we invest enough money and time making sure the inputs to simulation are accurate? In particular, are we sure of the accuracy of one of the most critical inputs: our materials property data?


The materials data challenge

Ensuring the right materials data inputs to simulation is difficult:

  • It’s often hard to find reliable materials data, and much easier for engineers to resort to data they already have, that may not be up-to-date or appropriate for the application
  • Data is stored in disparate silos with no traceability back to the original source, so it’s hard to know how reliable it is
  • Even when good in-house data that could support simulation is available (e.g., from test results), generating the material models for simulation time-consuming and error-prone
  • Materials are often assigned in CAD, but these assignments do not transfer efficiently to CAE, leading to inconsistencies between simulation and design.


How do we keep Babbage happy?

The answer to these problems is to manage materials data systematically and to have a single, accurate source. Then, you need the right tools to generate the materials models for simulation and, finally, these models need to be easily accessed by simulation analysts across the organization.


These are our objectives at Granta with our GRANTA MI:Simulation tools.  But however you tackle this issue, you want to be thinking about how to keep Babbage happy with:

  • Improved confidence – without traceability or context, it’s impossible to have full confidence in your data
  • Time saved – resources are wasted when you aren’t sure which data to use and which is the “right” source
  • No use of obsolete data – the issue of inaccurate data is nullified when there is direct access to controlled, managed material model data
  • Consistency – manage the complex connections between materials datasets and bring them together into one place

Managers involved with material decisions or simulation within the automotive industry may be interested in attending the Automotive Material Intelligence Forum in Munich in May. Consisting of three tracks of information, industry-leading speakers will be discussing Business Processes and PLM, Virtual Product Development, and Materials Challenges.

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