We’ve talked about MBD (model-based definition) on the blog before. Many companies find that using a 3D model (rather than a 2D drawing) as their product/component definition boosts efficiency and reduces engineering time and manufacturing errors.
“Model-based definition is what everybody wants to do right now,” says Rosemary Astheimer, Continuing Lecturer for the Polytechnic Institute at Purdue University. “If you can capture information directly in the 3D model, you can send it downstream to other applications including digital metrology equipment, which can inspect a manufactured part, compare it to the CAD model, and then determine whether it is within the tolerances that were specified.”
But maybe we shouldn’t stop there, says Astheimer. Inspired by the unconventional use of data in the series of Freakonomics books, Astheimer began to wonder what would happen if the engineering community started thinking about MBD data a little differently.
“Typically metrology equipment [roughly, the devices that physically measure manufactured pieces, ensuring they conform to spec] does a pass/fail type check; it determines whether the part meets all of the specifications,” she says. “My thought was, ‘Is there anything else that we can get out of this data?’”
Image: Before joining Purdue in 2014, Rosemary Astheimer spent over 15 years working in the CAD industry.
The truth is, these systems are already collecting a lot of information. However, few people think about how that information could be used to make further improvements to product designs and/or processes.
“What if we figured out a way to filter that digital metrology data to tease out valuable information that could be passed back to engineering so there were fewer rejected parts or to purchasing to make better informed purchasing and partnership decisions?” asks Astheimer.
An MBD approach results in loads of data. What if you could filter that data and use it to improve products and/or processes? Image: Mitch Altman via Flickr
Let’s say you designed a circuit board cover. When manufacturing tests your cover design, the digital metrology equipment might be capturing a high rejection rate on the covers due to issues with the material and fasteners.
By feeding the data from the measuring instruments back into engineering, you might be able to quickly determine that your rejection rate is considerably lower when you use a different material and change tolerances on a hole.
“What we are currently missing is the link back from the inspection instruments into the models,” says Astheimer. “We could be and should be using the data from those instruments to optimize the design including dimensions and tolerances in the models.”
Astheimer provided another example, “If you know how long it takes to manufacture a specific feature, you could determine that if you have a tolerance tighter than x, it’ll take you longer to manufacture the part, which ends up costing more. Or maybe the feature characteristics help you determine what resource to use to produce that part to optimize production time. So, you can potentially look at that data and determine if there are ways to make adjustments to your design and manufacturing procedures.”
According to Astheimer, “The data is there, but it usually doesn’t get analyzed so that it can be sent back to engineering. We’re simply not using it to its full potential.”
MBD is quickly becoming the preferred approach to design as many of the hurdles to creating a single source authority model for every stage of product development are falling away. To learn more about model-based definition, check out the free eBook from PTC. You’ll find out more about the limits of 2D drawings, how MBD simplifies complexity, and where to get started. Download your copy today.