Rethinking Efficiency in University Print Labs: Why Traditional Methods Fall Short

Most advice about 3D print farm efficiency focuses on batching like tasks; whether that be batching prints, scheduling jobs to end at the same time for efficient part removal, or grouping prints with the same filament together. But university labs face a unique challenge - almost every print is different. When print times are unpredictable and requests increase overnight, traditional efficiency methods just don't work.

So how exactly can university labs achieve an efficient operation with this in mind?

Contents

1. The Traditional Efficiency Model
2. Flipping the Script
3. Accessibility and Sustainability
4. Education and Innovation

The Traditional Efficiency Model

Traditional farm efficiency advice looks like this:

• Configure your print farm to have dedicated spaces for different use cases, e.g., group all your R&D printers together in one rack, and your production in another.
• Batch small prints to reduce the number of trips you make to the printers.
• Schedule your prints to minimize downtime (e.g., longer prints can run overnight, time your batches to align with your schedule).
• Check filament levels between prints to avoid mid-print runouts.

The problem with all this advice is it assumes a predictable workflow where you’re printing the same set of parts - but that’s not how university farms are run. Usually, each part printed is unique as student projects are all individual and the only repeats you can really expect are when a previous iteration failed.

On top of that, labs generally run on a first come first serve basis, which means parts get printed in the order of submission. While this keeps things fair for students, it also prevents optimal scheduling and can cause major downtime.

Flipping the Script

Since university farms run so differently from commercial farms at whom traditional advice is aimed, their approach to maximizing efficiency can’t be the same. So how can we approach efficiency in a university print lab environment in a more effective way?

We need to stop trying to reduce the inefficiencies in our current workflow, and take a new approach wherein efficiency is added through adaptable automation.

A university print farm running at peak efficiency would look like this:

• Students submit their files online
• Jobs are sent to the first compatible printer
• Filament on each printer is tracked and displayed on the central dashboard
• Prints are monitored using AI detection
• Student can view print progress
• Prints are automatically removed when done
• Students are notified when their job is complete

This workflow requires no middleman, no downtime, no manual tracking. It’s just an efficient, streamlined operation. Operators can maintain printers preventatively, as even workload distribution means printer wear is more predictable, and can quickly see on the dashboard when filament needs to be changed.

Accessibility and Sustainability

Aside from the obvious time gained from this new workflow, it also creates a more accessible operation, as students can easily submit and check on prints from anywhere, on or off campus. With no need to go in person until their print is ready to pick up, all students are able to stay up to date on their projects.

An efficient workflow with automated load balancing also improves sustainability. With filament tracking and low filament warnings, mid-print filament runouts become a thing of the past meaning fewer print failures and less reprinting. This makes operations more environmentally and fiscally sustainable, as reducing waste helps the planet and means lower filament cost for the university and students. Additionally, AutoFarm^3D™ comes with 24/7 monitoring by QuinlyVision AI failure detection, so failing prints are caught within seconds, automatically stopping potentially catastrophic failures that not only waste filament and take time to clear away, but risk damaging the printer resulting in time consuming repairs.

Education and Innovation

A more efficient and streamlined workflow frees up time to focus on different educational initiatives that bring more value to the students and the university. For example, shifting resources from removing prints to optimizing design and preventing failures through better slicing helps students explore the link between efficiency, productivity and print quality. Building expertise in areas such as slicing provides immediate benefits, improving lab operations and efficiency by avoiding catastrophic print fails and designing unique parts that print successfully. In the long run, having staff and students with better 3D printing skills drives innovation in the wider industry.

Automation and AI is the future of 3D printing, so adopting an AI-driven farm-wide automation solution positions your print lab at the cutting-edge. It allows you to push the boundaries and redefine what efficiency looks like in a university print lab environment. By operating a streamlined and innovative farm, students and operators alike get hands-on experience working with future-forward technology, providing invaluable experience. Plus, with students more educated on the technical aspects of design and slicing, they are able to be more innovative in their own projects.

Conclusion

Traditional print farm efficiency is achieved by meticulously scheduling operator tasks to reduce inefficiency - but university labs need the opposite approach: adding efficiency through automation that adapts to unpredictable demand and unique prints, rather than trying to force an inherently dynamic workflow into a rigid schedule.

Ready to take your print lab to the next level? Book an AutoFarm^3D demo today and find out how it fits into your operation.