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How data fuels the move to smart manufacturing

How data fuels the move to smart manufacturing

Fueled by a combination of industrial data and , a new era of smart and flexible manufacturing is underway.

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SwissCognitiveThe technology promises to facilitate an environment where production can be seamlessly calibrated to pump out customized goods while factory floor equipment is maintained proactively to avoid costly downtime.

That was the focus of a recent panel exploring the evolution of smart design and manufacturing at EmTech Next , a virtual event hosted by MIT Technology Review. Presenters said has the potential to touch and transform the entire design and manufacturing continuum, including:

Early-stage ideation.

Custom and flexible production.

Predictive maintenance.

Finished goods.

Machinery out in the field.

The connective tissue underpinning this seamless workflow is computational models and data, with deployed to sort through a sea of options and identity optimal outcomes.

“Connecting models at different scales to the data you get from the real world is especially critical,” said panelist Saigopal Nelaturi, research area manager, principal investigator at PARC. The way forward is to incorporate both models and data together in an framework that’s capable of parsing through large data sets to zero in on optimal designs or outcomes, he explained.

Here are four key tenets to keep in mind as you embark on your smart manufacturing journey:

Let help with design, process planning, and production

Whether the goal is getting to the optimal product design or uncovering the most efficient and cost-effective manufacturing method, the possibilities are endless when you set specific parameters and goals and work backwards from there.

Take conceptual design, for example. Typically an engineer comes up with potential designs for a widget or component based on a set of core requirements, but there are limits to the number of ideas they might explore. Not so in the world of -driven design, where an engineer can specify parameters like cost, weight, and strength targets and let software do the heavy lifting to computationally churn through all the possibilities to come up with a spread of viable candidates.

can work the same magic for the process planning and production methods that go into making that widget. By creating full-scale 3D and behaviorial models of plant floor equipment, teams can leverage -based tools to run through different virtual scenarios and simulations to determine what materials and systems will produce the goods in the most efficient and cost-effective manner.

“Your goal here is the part. You’re starting from raw stock and trying to figure out the best way of making it,” Nelaturi said. “Once the runs its analysis, you come up with an automated process plan that tells you how to orient the part, what materials needs to be cut out, what materials remain, and in which way the part can be built in the most efficient manner.”

Collect data where it lives — on the factory floor

Early on, the push was to connect industrial machines together via the Industrial Internet of Things (IIoT) and let machine-to-machine communications drive insights and automation. However, the complexity of plant floor data and a lack of clarity around goals hobbled a lot of early efforts, according to Matt Wells, vice president of digital product management for GE Digital and a presenter on the EmTech panel. […]

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