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Digital Engineering: Special Industry Focus – Digital Twins

By March 30, 2022July 17th, 2023No Comments

Digital Engineering released a Special Industry Focus issue on how digital twins, Internet of Things (IoT), and Industry 4.0 technologies impact design processes and how that will affect how organizations approach product lifecycle management.  In this issue, Jonathan Scott, our chief architect, provides insight into two significant technologies.

Digital Twin Benefits

Digital Twin technology can help product designers in extracting vital information from both domains to improve development and production.

“Product designers make many choices during product development, some of which are driven by product requirements, but many of which are arbitrary—just based on the designer’s preferences, says Jonathan Scott.  For each of those arbitrary decisions, if valuable information exists to influence the engineer’s decisions—say from manufacturing simulation— the design can be enhanced based on that data. Information from the digital process twin can also be used to provide that added data to the designer at the right time—when they are making key decisions and moving the product design forward,” he adds.

One such example is when a development team adjusts material thickness to achieve a particular appearance or to reduce weight. In manufacturing, however, these same choices can cause problems in assembly or as material moves on conveyorized systems. The ability to quantify the impact of downtime and related defects can provide valuable feedback to the product design team.

Cloud PLM and DaaS

Cloud-based PLM provides the flexibility and accessibility needed for digital transformation, but there remains a disconnect between SaaS software and proprietary legacy applications. As a result, businesses require an effective means of facilitating data access and integration among enterprise software systems.

This is where data services, or data-as-a-service (DaaS), come into play. These web services consist of collections of small, independent functions that automate the work of locating heterogeneously stored data, providing developers with simple programmatic tools to find, extract and share data.

“Many domain-specific tools, like PLM and other engineering-centric tools, have highly specialized data models that delineate the nuances of a data set,” says Jonathan Scott, chief architect at Razorleaf. “For example, PLM might understand that a component is a configuration or variant of another component, but someone consuming a data service of a company’s item catalog does not care about this detail. DaaS can simplify these details and remove complex authorization chains— who has access to what and when—to make it easier to see the data allowed for publication.”

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