ANSYS and PTC have announced a solution that will enable ANSYS software to be added to applications built on the ThingWorx Industrial Internet of Things (IoT) platform developed by PTC.
PTC is a US-based, software company that specialises in PLM and IOT, software aimed at the IOT and AR markets
The connection will integrate intelligent digital simulation models with products as they exist and operate in the real world. This will open up new opportunities for companies to create value by enabling them to optimise operations and maintenance and to integrate them into their product development processes.
‘Obtaining value from the data generated by connected products is one of the primary reasons companies invest in the Industrial IoT,’ said Catherine Kniker, chief revenue officer, Platform Business, PTC. ‘Simulation technology combined with machine learning can help Industrial IoT solution builders identify and make sense of the data needed to improve product design and performance – and develop next-generation products.’
The IoT is making it possible to create a digital twin that combines real-time data about a physical product with the organisation’s digital information about the product. Simulating digital twins provides advanced intelligence and insight into a product’s behaviours.
The combination of the ThingWorx platform capabilities with ANSYS simulation models is intended to enable companies to deploy applications that can analyse current operating conditions, identify and diagnose operations issues, predict future operating conditions, and improve product performance.
‘Many of our customers are looking to digital twins to disrupt their industries by drastically lowering their operating and maintenance costs and by marketing their products as optimised services in real time,’ said Eric Bantegnie, ANSYS general manager. 'By leveraging the solution that ANSYS and PTC will bring to the market, our customers will bring powerful capabilities to new Industrial IoT applications.’
Simulation helps companies understand situations that may occur – such as a product failure – as early as the design phase. When faced with limited access to historical data, companies can leverage simulation models to generate an initial ‘as designed’ set of expected outcomes or product performance.
The results of these simulations act as a source of data that can be used for supervised machine learning and predictive modelling. The ongoing synergy between real-world performance, simulation, and machine learning helps companies make sense of data that can lead to predictive models and a more insightful feedback loop, enabling them to improve product design and modelling.
