This paper was produced for the 2019 NAFEMS World Congress in Quebec Canada
As product designs become increasingly complex, competitive pressures drive new levels of innovation, and measuring in-service product performance creates new business models, companies are making significant investments in simulation to avoid incurring the cost of traditional testing approaches.
Consequently, simulation is increasingly embedded in product design and development processes. Already a standard engineering activity across many industries, the adoption of the Digital Twin is forcing companies to consider how models of products behave, how they connect to one another throughout the lifecycle, and to data and decisions relating to the physical product. What about materials? How confident are simulation analysts in the materials data that they use as input? How is this data linked to the other varied definitions of the material throughout an enterprise (e.g., for CAD, aesthetics, or compliance)? What opportunities arise if businesses can capitalise upon the materials data from all their products? And what are the risks if they do not do so? We will review what leading engineering enterprises are doing to ensure traceability and connectivity for materials data in order to guarantee robust, consistent simulation of parts and products.
The same principles of connectivity and traceability can be applied to the simulation and design of the materials themselves, through approaches such as Integrated Computational Materials Engineering (ICME). Indeed, with technologies such as Additive Manufacturing and Composites, modelling of the material and of the product increasingly overlap. The industrial vision for the development of computational materials science over the next 20 years sees significant advances in integrated multiscale modelling and simulation of materials and systems. This requires: the robust and traceable capture of simulation data, pedigree, and metadata; integration of simulation data alongside materials test data, enabling validation and calibration; the visualization of complex data, application of artificial intelligence, and quantification of uncertainties; and secure sharing of intellectual property within and between organisations. This presentation reflects on these requirements and how they might be met at an organizational and industry level.