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Model-Based Definition: A Seven-Point Summary

Interoperability is key to avoiding the manual steps and hand-offs that Industry 4.0 hopes to eliminate

Published: Monday, December 17, 2018 - 12:01

Annalise Suzuki, director of technology and engagement at software provider Elysium Inc., spoke to Quality Digest about the importance of model-based definitions (MBD) for data quality, validation, and engineering change management. With the increase of digital 3D models in the manufacturing workflow, companies are appreciating their value for speeding product development, improving quality and performance, and allowing for greater automation. Here, Suzuki answers seven questions about the model-based enterprise (MDE)’s current and future role in industry.

Quality Digest: What is model-based definition (MBD), and why is it important for advanced manufacturing?
Annalise Suzuki: Model-based definition refers to the reliance by engineering and manufacturing on a 3D model to contain all the necessary information to design, manufacture, inspect, and archive a product. With MBD, the 3D model becomes the single source of authority on a product’s design—rather than a 2D drawing-driven process that is often separated from full 3D content. Such modeling supports an all-digital environment that includes 3D product manufacturing information (PMI), tolerancing, and metadata and attributes (e.g., part numbers for ERP and material specifications for procurement) for feeding both early design and downstream activities. MBD is the central foundation for achieving Industry 4.0 and digitalization initiatives, which will speed product development, improve quality and performance, and allow for greater automation.

QD: What role is data exchange and interoperability playing in the advanced manufacturing movement of MBD, Industry 4.0, and IoT?
AS: Data exchange and interoperability are playing a crucial role now that the digital 3D model is becoming the sole source of geometry and definition for a multitude of disciplines and processes—which must all be able to consume the same product information consistently to get accurate results. The same authoritative 3D model that has gone through design engineering must be leveraged uniformly for simulation, analysis, inspection, and manufacturing processes. Each of these disciplines draws off all the rich and detailed information possible within a 3D model. During both development and production, if any important data are lost during transfer, or even misinterpreted, workflow is then interrupted, and automated approaches fail. Interoperability is key to avoiding the manual steps and hand-offs that Industry 4.0 hopes to eliminate. But data exchange, as we’ll call it, must be of high enough quality to ensure that data are seamlessly read and utilized, as intended, by all parties. Interoperability will ultimately be the enabler advancing industry 4.0 initiatives—or the hurdle delaying or breaking that vision.

QD: What are the challenges of an integrated all-digital manufacturing world with fewer manual interventions? What are the benefits?
AS: The challenges are everywhere for all-digital manufacturing initiatives. Processes today are heavily dependent on manual behavior; getting tools to automate consistently and accurately is an area that needs to mature. Standard interoperability formats, such as STEP AP242 and QIF, need more implementations to reach maturity. While one software may write out a STEP AP242 file, if the next doesn’t read it, or there are flaws, the process is broken. OEMs and their suppliers need to work together to overcome the mutual challenges of sharing the right data in the best way. If implemented successfully, benefits might differ between organizations and even departments, but overall improved control over information quality will allow for faster inspection times, higher quality products, and more traceability to how products were finally configured and created. There is no doubt that feedback from inspection of products back to design and engineering will be better-enabled through digitalization. Those advantages ought to carry into the next life cycle of products.

QD: Industry understands Six Sigma quality programs for manufacturing hardware. What about a Six Sigma mindset for interconnected CAD/CAE/CAM and PLM software that sends its data to production?
AS: Quality-enabling software tools are imperative to successful deployment of MBD and to fast, accurate production starts. Six Sigma is a concept and measurement that can extend beyond the hardware and sensor solutions we know and accept today in manufacturing. CAD/CAE/CAM and PLM software are equally inherent to geometry, as are the attributes of a manufactured metal cylinder or fixture—only one is digital and the other physical. In a perfect world, they mirror each other. This is where the emerging term “digital twin” derives from. The “as designed” and “as manufactured” versions match exactly, leading to statistically high quality and, importantly, the ability to repeat the one production instance over and over again. The mindset of production Six Sigma can extend to use of quality software to measure and improve design fidelity. The software tools are available to replicate the same performance. The net result is efficiency, cost savings, and customer satisfaction.

QD: How does quality control in the digital manufacturing enterprise ensure better results from CMM/Laser inspections and for precision machining and process work cells? How does industry improve the information loop from CAD to CNC, for example?
AS: From my perspective this link between digital and hardware quality systems is still evolving in terms of their integration, application, and as it relates to a continuous feedback loop. But the short answer is that the geometry flaws that CMM and laser inspections search for are the same that today’s advanced checking software is enabled to find in the digital phase. These flaws include broken edges, misaligned surfaces and intersections, hole location errors, and missing features—things that can interfere with precision machines and work cells. Solving such discrepancies before CMM inspection and production has obvious benefits. Hopefully, soon, production measurement will serve to primarily check machine settings and material changes rather than spot tolerance design errors or poor manufacturing instructions embedded in CAD. End-to-end automation depends on upfront digital efforts to ensure that the type of quality standards, long associated with factory floor operations and practices, are captured throughout.

QD: How can manufacturers use product data quality (PDQ) software to meet their individual standards for modeling and supply-chain requirements?
AS: PDQ software is essential for efficient MBD/E that extends from the OEM to the supplier. It is a complete, automated checking system for healing; repair; reconciling different versions of CAD software and creating a master model; migrating clean, up-to-date data for archiving and immediate use; and ultimately, meeting a rigid certification for sending uniform information between the OEM and supply chain. Full PDQ software is what allows manufacturers to offer a guarantee that inspected parts meet original requirements. Nissan, for example, has such an approach with its internal departments and supply chain. Controlling quality this way early in the design process is absolutely critical for optimization of data and trouble-free reuse downstream. The less that’s managed at its proper stage, the worse the problems are later for quality, processing speed, and final throughput. A solid PDQ checking, reporting, and process implementation, however, seems to be an under-realized value today due to the fact that divisions act in silos, and the results of poor-quality data simply become someone else’s problem. Yet, if an organization can control quality checks holistically, there are a lot of benefits that can be reaped as an enterprise. The truth is, the time spent to manually find, fix, and deal with errors in product data is enormous. But often those stakeholders that contribute to the cause of problems don’t see the direct consequences themselves. This delays recognition. Fortunately, it’s technically possible now to tailor checks and even healing systematically according to internal standards, customer requirements, and even for specialized software that must consume data later. When a company can reach this level of optimization and fidelity in its data development, it is a long step ahead of the norm.

QD: What trends do you see in model-based definition and the model-based enterprise (MBD/E)?
AS: As projects unfold, one specific trend I’ve seen among leaders is an effort to include all areas of an organization early in the stages of MBD/E initiatives—such as procurement—as their purchasing processes may need to evolve around 3D authoritative data sources, and they ultimately hold power to enable or disable or delay activity. Currently, the technical hurdles to MBD/E are equally matched with “people” hurdles. Creating new work cultures to understand digitalized change management and automation can be very difficult in large organizations, and people need to understand why they’re being asked to change, and what’s in it for them.

I’ve also noticed advancements coming from quality departments, such as their embracing process outlines and some adoption of quality information framework (QIF). There are pockets of projects underway in many industries and a lot of small successes taking place as well as lessons learned. I see that each organization approaches MBE slightly differently based on their products, processes, and culture. Aerospace has a longer view of things because of the decades that some aircraft platforms must be maintained with always accurate, and yet continuously evolving, CAD technology. In automotive, the challenges of data exchange fall by default to the Tier One suppliers that are at the hub of information movement between OEMs and Tier Two and Tier Three sub-suppliers. I also see that various engineering software providers are working quickly to get up to speed on new exchange standards. But true success here needs to be achieved in open and collaborative settings, in partnership with industry, so that both can succeed and reach mutual goals that will stand up in production-level implementations. It will be a very iterative cycle between industry, their supply chains, and support from contributors and software providers to reach the maturity required for building truly reliable Industry 4.0 processes. Nevertheless, results produce results. Data interoperability and PDQ automated approaches will show gains that will drive industry to deeper implementation of digital-based programs and products.

 

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