This guest post on Innovation Intelligence is written by Renu Gandhi, Vice President of Business Development at Matereality, LLC, developer of Workgroup Material DatabasePro. Matereality is a member of the Altair Partner Alliance.
Success in the virtual product development environment relies heavily on seamless connectivity of reliable digital data. In building product life cycle management systems, much emphasis has been placed on geometries and FEA interconnectivity. Materials form an essential core of any new product design, and yet, their quantitative behaviors, while a requirement, are not as well connected to the virtual product models. “Only by more closely integrating materials data with CAE tools can we hope to more accurately predict the behavior and performance of a part or a system under a variety of conditions” – an old quote from Industry Analyst Amy Rowell.
Product innovation success requires accurate material testing as well as a centralized data repository for ready and seamless access. The realization is not new. However, there have been technological challenges. The sentiment now is “I will not allow any material data input into a product design that has not met the specifications and review of our enterprise. I don’t need data from many different materials, I need the right material data.” The risks associated with wrong material data inputs in product design are too high, introducing the need to bring in all data under the enterprises’ control, with connectivity to all virtual product development. This is now quite feasible.
DatapointLabs (www.datapointLabs.com) and its affiliate Matereality (www.matereality.com) work to strengthen the material core of manufacturing enterprises with expert material testing and software for productivity enhancement and PLM integration. Some of the software features include enhanced material data visualization, CAE material parameter conversion, and management. The goal is to provide fast, easy-to-use services that foster in-depth understanding of purpose-specific material behavior, eliminate typical risks associated with generic data, and accelerate the pace of product development.