What Is Digital Twin Technology, and Why Is It So Important?


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Siemens’ 12-part webinar series “Implementing Digital Twin Best Practices From Design Through Manufacturing” is an excellent series designed to educate the electronic manufacturing engineer on the progress of using a digital thread to improve products and performance. This is a complement to Oren Manor’s free eBook The Printed Circuit Assembler’s Guide to…Advanced Manufacturing in the Digital Age published by I-Connect007 [1].

Digital twin is the latest name for predictive engineering [1] models—a term that has been used in industry journals since 1990. In essence, a digital twin is a computer program that creates a virtual model of a process, product, or service. It takes real-world data about a physical object or system as inputs and produces as outputs predictions or simulations of how that physical object or system will be affected by those inputs. This allows the analysis of data and monitoring of systems, enabling engineers to head off problems before they occur, prevent downtime, develop new opportunities, and even plan for the future by using simulations. Digital twin technology has moved beyond manufacturing and into the converging worlds of the Internet of Things, artificial intelligence, and data analytics. Building a digital twin is complex, and there is no standardized platform for doing so yet [2] (Figure 1).

DT is the current description of the inventions of Product Design for Manufacture and Assembly first proposed by Professors Peter Dewhurst and Gottfried Boothroyd in the early 1980s [3]. Their DFMA was developed for the mechanical design of products with predictive analysis and models to score how they would perform in manufacturing. The Siemens series carries the concept into electronics manufacturing. A tutorial on DFMA is available through I-Connect007 (read Part 1) [4].

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Figure 1: A roadmap showing how product data is used to create a digital twin. (Source: Machine Design, March 2019)

Jay Gorajia, director of Siemens’ global digital manufacturing services, does an outstanding job of taking the attendee through the digital data thread common to all arenas in electrical assembly. Jay introduces the competitive environment that now requires the implementation of DT to be competitive. He explores what other webinars will cover and teach. In addition, he positions the Siemens product line for electronics assembly, with the initial digital data thread being ODB++Design that feeds Siemens’ design verification, manufacturing planning, process engineering, manufacturing analytics, production scheduling, and production execution products.

Jay explains in detail how these products and the integrated data stream provide the performance improvements, quality, and productivity required to succeed in the global market. He starts with the important aspects of optimizing data flows and introduces the three enhanced ODB++ data formats: design, process, and manufacturing. These are compared to the legacy data standard of Gerber, illustrating how ODB++Design is the start of an intelligent data stream for fabricators and assemblers (Figure 2).

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Figure 2: The newly defined digital thread provides the data continuity from design through assembly. (Source: Siemens)

The webinars continue with new product introduction (NPI) and its contribution to design for manufacturability (DFM). As seen in Figure 3, the Siemens software tools support product realization, verification, and implementation.

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Figure 3: The six arenas of electronic product DFM realization. (Source: Siemens)

Jay continues with the description of how DFM improves all the component parts. The Valor Part Library validates parts from its database and supplies the footprints for design, inspection, and assembly test. The DFT test strategy is captured early in the design process and provides for the all-important physical layout of test points to complement boundary scan, BIST, AOI, X-ray, and functional testing.

What’s just as important is the portability of the design between different machines, sites, and systems. The Teamcenter software creates one database that allows for implementation flexibility and portability, working in conjunction with the Valor Process Preparation software and a single source for equipment translations including assembly, test, and inspection’s single DB. This leads to the implementation of the smart factory.

Production execution software (Opcenter Execution Suite) provides intelligent data acquisition from industry-standard data formats like GEM, CFX, TCP, XML, and CAMx to the integrated ODB++ manufacturing. Here, the data is normalized so that it can be fed to different coordination and analysis software for materials management, inventory control of warehouse, shop-floor of parts, and optimum changeover of products. The data is used for analysis and display of key analytics important for a smooth flow and interactive supply chain. Ultimately, this leads to MES optimization and enterprise connectivity.

Each webinar discusses other facets of the Valor software set: Valor NPI, Valor Parts Library, Valor DFT and DPMO analysis, Teamcenter, Valor Process Preparation, Opcenter Execution Electronics IoT and ODB++ manufacturing, Valor’s Analytics Tool combined with Valor Material Management Solution, Opcenter Execution Intelligence, and the Opcenter Electronics Suite. The final webinar discusses the key advantages of an integrated approach when Siemens data solutions are incorporated into manufacturing execution systems and enterprise systems.

To view Siemens’ on-demand webinar, click here.

References

  1. Oren Manor, The PCB Designers Guide to…Advanced Manufacturing in the Digital Age, I-Connect007.
  2. Happy Holden, “Digital Twin Drives Design in the Smart Factory,” PCB007 Magazine, June 2019, pp. 10–17.
  3. G. Boothroyd and P. Dewhurst, Product Design for Assembly, Department of Mechanical Engineering, University of Massachusetts, Amherst, Massachusetts, 1983.
  4. Happy Holden, “Happy’s Essential Skills: Design for Manufacturing and Assembly, Part 1,” I-Connect007, June 29, 2016.

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