Big data has been called ‘next frontier’ of design, a new world rich with useful insights and forecasts. Data has evolved from a tool for looking at the past to one that can predict the future, Ravi Shankar and Jan Larsson discuss more
By combining physics-based simulations, data mining, statistical modelling and machine learning techniques, predictive engineering analytics can analyse patterns in the data to construct models of how the systems you gathered the data from work.
IoT and sensors are already transforming products and mining the stream of information from products will be critical for maintaining products and designing their replacements.
For many industries, the products they create are no longer purely mechanical; they’re complex devices combining mechanical and electrical controls. That means engineering different systems, and the ways they interface with each other, and with the outside world.
At one level you’re coping with electromechanical controls, at another, you’re creating a design that covers the cooling requirements for the electronics. And in the future, you have to model that as part of a larger system; for instance, systems inside a vehicle will begin to talk to other vehicles and to traffic systems on the roads they travel on.
One consequence of this increasing complexity is that testing during engineering has been routinely supplemented by and, in some cases, even replaced by, simulations that cover multiple systems, and take into account the many different types of physics you need to model all those systems. That’s valuable during design and in acceptance testing too.
Either the physical product design or the demands of the location of the finished product may make it impossible to gather readings from a physical sensor to verify final performance. That’s when a virtual, simulated sensor can augment the information from the physical device and enhance the usefulness of the test.
Beyond tracking requirements
On the other hand, demands for strength, fuel efficiency or simply more efficient manufacturing may mean adopting new types of materials and new production techniques.
Companies who have decades of experience with traditional materials like steel and aluminium have to learn to work with new materials, often using additive manufacturing and combined additive and subtractive manufacturing.
That means going back and doing physical tests and correlating those tests to simulations to understand things as basic as how materials behave at a range of temperatures and what impact that has on the system design.
To address all these demands, companies will need to integrate their testing methods and their simulation methods, and they’ll need to adopt both more simulation – and much more data management, so they can accelerate the speed at which they perform their engineering work.
This goes far beyond tracking requirements, CAD data and test results; engineering data management systems need to store all the engineering work, including simulation and verification, and integrating test, sensor and performance data.
Applying predictive engineering analytics covers exploring the design space efficiently by running multiple simulations with different parameters and analysing the resulting data intelligently, so you can understand key parameters and how they interact. That enables you to optimise a design to achieve robust performance that’s not sensitive to changes in the environment.
Predictive analytics may even move into products, as control systems shift from detecting to forecasting conditions. Today, anti-lock brakes use sensors to detect when the car is beginning to skid. In the future, sophisticated control systems could use on-board cameras to detect that a vehicle driving in the rain is approaching a curve too fast for the road conditions, predicting that it will skid and controlling the vehicle before it does, to handle the curve safely.
The size and scope of the data sets available enables advanced analytics, but this size also has consequences: engineers will be drowning in information unless they take steps to make big data manageable. The number of sensors in products today is only going to increase.
Sensor readings from an aircraft engine or measurements taken from a car on a test track already create huge data sets – too large to use the raw data in simulations, because they’d take too long to process. The scale of the data is going to require intelligent analytics to condense raw streams of readings into data that can usefully be fed into a simulation.
Predictive analytics is already useful in engineering today. You can use it with your simulation portfolio to investigate different architectures early in product development, to help you understand which type of architecture is best suited to meet customers’ needs. You can create 3D simulations and integrate them with those early architecture models. Then you can bring in test data and see how it correlates with the simulations to improve your models.
Combining data from devices with warranty information and data about customer satisfaction in a common data store allows you to use big data analytics to understand the significance of different factors in the design of the device that ultimately have an impact on business success.
These techniques are already being used by many companies to better understand their operations. However, few make the best use of their precious data. By aggregating different data sets into a single system, you can utilise new and emerging technologies as well as your existing tools to get the most out of your information.
Senior marketing director EMEA
Global simulation product marketing