Predictive engineering analytics (PEA) is a development approach for the manufacturing industry that helps with the design of complex products (for example, products that include smart systems). It concerns the introduction of new software tools, the integration between those, and a refinement of simulation and testing processes to improve collaboration between analysis teams that handle different applications. This is combined with intelligent reporting and data analytics. The objective is to let simulation drive the design, to predict product behavior rather than to react on issues which may arise, and to install a process that lets design continue after product delivery.
Industry needs
In a classic development approach, manufacturers deliver discrete product generations. Before bringing those to market, they use extensive verification and validation processes, usually by combining several simulation and testing technologies. But this approach has several shortcomings when looking at how products are evolving. Manufacturers in the automotive industry, the aerospace industry, the marine industry or any other mechanical industry all share similar challenges: they have to re-invent the way they design to be able to deliver what their customers want and buy today.
Complex products that include smart systems
Products include, besides the mechanics, ever more electronics, software and control systems. Those help to increase performance for several characteristics, such as safety, comfort, fuel economy and many more. Designing such products using a classic approach, is usually ineffective. A modern development process should be able to predict the behavior of the complete system for all functional requirements and including physical aspects from the very beginning of the design cycle.
The use of new materials and manufacturing methods
To achieve reduced costs or fuel economy, manufacturers need to continually consider adopting new materials and corresponding manufacturing methods. That makes product development more complex, as engineers cannot rely on their decades of experience anymore, like they did when working with traditional materials, such as steel and aluminium, and traditional manufacturing methods, such as casting. New materials such as composites, behave differently when it comes to structural behavior, thermal behavior, fatigue behavior or noise insulation for example, and require dedicated modeling.
On top of that, as design engineers do not always know all manufacturing complexities that come with using these new materials, it is possible that the "product as manufactured" is different from the "product as designed". Of course all changes need to be tracked, and possibly even an extra validation iteration needs to be done after manufacturing.
Product development continues after delivery
Today's products include many sensors that allow them to communicate with each other, and to send feedback to the manufacturer. Based on this information, manufacturers can send software updates to continue optimizing behavior, or to adapt to a changing operational environment. Products will create the internet of things, and manufacturers should be part of it. A product "as designed" is never finished, so development should continue when the product is in use. This evolution is also referred to as Industry 4.0, or the fourth industrial revolution. It challenges design teams, as they need to react quickly and make behavioral predictions based on an enormous amount of data.
The inclusion of predictive functionality
The ultimate intelligence a product can have, is that it remembers the individual behavior of its operator, and takes that into consideration. In this way, it can for example anticipate certain actions, predict failure or maintenance, or optimize energy consumption in a self-regulating manner. That requires a predictive model inside the product itself, or accessible via cloud. This one should run very fast and should behave exactly the same as the actual product. It requires the creation of a digital twin: a replica of the product that remains in-sync over its entire product lifecycle.