Smart Maintenance and using 3D printers
Can parts be 3D printed? How can 3D printing be used as a tool for Smart Maintenance? What is the difference between single- and double-loop learning, and how can it be applied to avoid future operational stoppages? This article provides answers to questions through real-life cases from industry and outlines the differences between traditional maintenance and that which harnesses the potential of digitalisation.
In the demonstrator developed during the Smart Factories project, a SCARA robot picks lenses and places them in a fixture. An actuator then lifts the fixture, with the lens placed in an overhead conveyor as show in the film below.
The fixture is 3D printed in plastic (PLA), and four prototypes were required before the lens fixture satisfied all requirements and functioned as intended. The ability to print 3D prototypes in order to, above all, test functionality was highly effective and saved time.
After three weeks’ operation, there was a breakdown in the factory which caused a one-day stoppage. This occurred because the overhead conveyor missed the hole in the lens fixture. See the image below.
As a result, the entire track belonging to the overhead conveyor was suspended 10 centimetres in the air. The fixture was partially deformed, which affected the positioning between the overhead conveyor and lens fixture. As a result, the lenses could not be delivered. Traditional maintenance would have involved filing the deformation in the fixture and adjusting its position. However, this approach would not have prevented the error from re-occurring, and is an example of single-loop learning. A better way of resolving the issue would have been to address the underlying cause and redesign the fixture, that is, double-loop learning. As the fixture was 3D printed, a number of simple adjustments could be made to the CAD model and a new fixture printed out. The new fixture guides the overhead conveyor into the lens fixture as a result of its funnel-shaped design. Following installation of the new fixture, no new breakdowns have been reported.
The illustration below shows the difference between traditional maintenance, with single-loop learning taking place in the organisation, and smart maintenance, in which double-loop learning is applied. In the case of single-loop learning, the organisation may have learned a method of resolving the error more quickly next time it occurs, but it has not taken the step towards Smart Maintenance through the application of double-loop learning.
The conclusion drawn from our industry scenario is that 3D printing is highly suitable for the manufacture of parts like, as in our case, fixtures. In the vision to eliminate unscheduled operational stoppages which is at the heart of Smart Maintenance, 3D printing is an enabler, in the same way as AI and the IoT.
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