MODELLING AND SIMULATION
“While topology and parameter optimisation tools are required to suggest additive geometries, they can also come up with wild shapes that work from a mathematical point of view but may not be feasible in the real world”
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Machines learning Artificial intelligence (AI) is a rapidly growing area for design tool engineers and scientists, which is being integrated into workflows across multiple industries. There is much machine learning can
achieve in this space – but we first need to manage the disparate skills required to run successful AI-enabled simulations. Jos Martin, director of engineering at MathWorks, explained: ‘Engineers and scientists can greatly influence the success of an integrated AI project because of their inherent engineering knowledge of the data and domain. This is a significant advantage over a data scientist who is not as familiar with the domain area.’ ‘However, traditional AI tools often
require deep programming skills familiar to computer scientists or data scientists, but engineers and scientists need tools that allow less time writing code, more time exploring innovative design ideas, and more quickly improving the effectiveness of those AI models.’ A lack of high-quality data is another barrier to success for many fledgeling AI projects, according to Martin, who added: ‘Often engineers and scientists don’t have enough data that incorporates rare events with high enough frequency to effectively train the AI model to properly handle them.’ More data is also required for robust
training in new areas, such as predictive modelling, where the availability of failure data, in particular, is essential. Martin explained: ‘Failure data is a crucial part of teaching algorithms to recognise the warning signs that trigger just-in-time maintenance. However, failure data is often not readily available, and producing failure data is time- consuming and expensive. However, it is
30 Scientific Computing World Summer 2020
By using a dynamic simulation tool, such as MapleSim, engineers can import their CAD models, add actuators, and get high quality simulation data for motor sizing, loading profiles, cycle time reductions, and more
possible to easily and cheaply simulate failure data and train models using that data to recognise warning signs from operational data.’ Using MATLAB and Simulink, specialist engineers can generate sample failure data, which is then labelled and used to build an AI model to accurately predict the remaining useful life of the equipment in question, according to Martin. Virtual commissioning is another
growing technique in the industrial automation and machine design sector, according to Chris Harduwar, vice president of automation at Maplesoft. ‘Typical machine commissioning involves hooking prototypes up to control hardware, and this is often when engineers and integrators find a variety of performance issues they need to address – this process can iterate for weeks or months,’ he explained. ‘With virtual commissioning, the engineers developing control code can use machine simulation models as virtual test platforms for validating their control code.’
This can help customers ‘reduce
costs and time to market tremendously,’ according to Harduwar, who said:
‘Customers can use MapleSim to create a functional, dynamic model of their machine, which can be used to help size motors, optimise cycle times, and so on. This model can be exported for use in a variety of different automation tools, so when the engineer wants to test their control strategies, they can get realtime feedback, and even see a 3D visualisation of their machine in action.’ Harduwar added: ‘While new
techniques like virtual commissioning require internal skills development, they continue to offer benefits that can’t be denied by competitive organisations. To make sure that we can keep pace with the increased demand for simulation- based design processes, we’re constantly adding new usability features to our simulation tool MapleSim, and we’re offering streamlined options for project consulting that allows companies to benefit from these new techniques while learning these skills at the same time.’
Maplesoft recently worked with a
customer to validate their motor sizing choices for a new injection moulding machine. They also needed to test their control strategies virtually, since physical machine testing posed a high risk of
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