Harnessing artificial intelligence (AI) and machine learning is the latest trends in practically every industry.
3D printing and additive manufacturing are no exception. AI is already making its mark on 3D printing with improved design processes and streamlined workflows.
But how far can this technology stretch?
Is it possible that we may see entirely AI-driven 3D printers that function with minimal operator interference?
This article explores where AI 3D printing stands now and what the future may have in store.
Artificial Intelligence — A Brief Explanation
Considering how much AI gets talked about in industry and media, it’s often woefully poorly understood. One reason for it could be that people confuse the entirely theoretical possibilities of AI with what it can actually do right now. Another factor adding to the confusion is that there’s no single technology called “artificial intelligence.”
AI covers an immense range of computer processes, related to such disparate technologies as automation, neural networks, machine perception (like facial recognition), and chatbots. Yet, at the root of all these processes is machine learning.
Simply explained, machine learning describes technologies that allow computers to process enormous data sets to develop the ability to draw constantly improving logical conclusions and judgments from them.
In other words, AI 3D printing allows computers to “learn” in similar patterns as humans to solve problems. This learning process can manifest in many different ways, from an online chatbot helping customers find what they’re looking for to a CNC machine automatically correcting mistakes in tool paths.
How Can AI 3D Printing Benefit?
But what can AI do for 3D printing? Although the technology is still in its relative infancy and advancing rapidly, it’s already impacting 3D printing in various ways.
Here are the five most common ways AI 3D printing benefit the operators and businesses.
1. Improved Workflow Efficiency
A 3D printer operator can’t just send a CAD file to the machine and hit start. Successful 3D printing requires the operator to fine-tune the printer’s settings based on the part and materials, which is time-consuming and limits productivity.
Software companies like 3DPrinterOS are developing AI-based solutions to automate print preparation processes. By analysing a sufficient number of print processes, AI 3D printing can learn to automatically print settings for optimal results. While it isn’t yet possible to make the machines entirely independent, this solution can deliver significant time and cost savings and allow engineers to focus on more significant tasks.
AI can help improve other workflows in running a 3D printing bureau. For example, the technology can help automate order processing by monitoring machine availability and print complexity.
2. Enhanced Design Processes
Just because a certain part has always had a certain geometry, it doesn’t mean it’s the ideal solution. AI can crunch through design iterations digitally to discover ideal geometries to maximize part strength, weight, and performance.
Companies such as SolidWorks are working on AI-powered analysis and simulation software solutions that can help 3D printer operators improve their parts through novel geometries. With the ability to process dozens or even hundreds of design iterations in a matter of hours helps shorten lead times and time-to-market for new products.
3. Better Quality Control
Many 3D printing errors could be rectified with swift action, but you can’t expect an engineer to stare at the print process for hours on end in case something goes wrong. AI, on the other hand, can keep a sharp eye on the printing process without ever dozing off.
AI-driven machine vision systems can monitor the printing process continuously and compare it to the 3D CAD model, while also analysing printer settings and parameters such as temperature and print speed. If the system notices any deviation or flaws, it can instantly adjust relevant settings to correct the print.
Such solutions can deliver significant time and cost savings by avoiding ruined prints and material waste. Some 3D printer manufacturers, like Bambu Labs (coming soon to Solid Print3D), are already including AI vision systems in their machines. We’re sure to see them become more common in the future.
4. Novel Material Solutions
Choosing the right material for 3D printed parts can be difficult — not to mention developing new materials. AI 3D printing can help operators and material manufacturers develop new solutions by quickly analysing immense data sets of material properties and application requirements.
For example, a 3D printer operator could upload a CAD model to an AI system and input the required physical properties for the final part. The AI can then quickly run through a huge material library to discover the optimal material option. Similarly, AI can help material manufacturers discover novel alloys and composite materials.
5. Generative Design
3D scanners are versatile and flexible machines. They are useful for quickly scanning everything from small body parts, such as a single foot or even a finger, to an entire torso. Even full-body scanning is perfectly possible.
The technology is useful for more than just scanning patients, though. It can help medical appliance manufacturers digitise equipment and parts to cut costs and lead times in product development.
Current Status of AI and Additive Manufacturing
As we’ve seen from the above examples, AI and 3D printing are working together to make additive manufacturing more efficient and accessible. AI-driven quality control, automation, and simulation solutions already make 3D printing more cost-efficient by automating tedious, time-consuming processes.
Yet, AI solutions are still far from perfect. They currently suffer from multiple limitations, specifically in the areas of implementation, data set availability, and legal and ethical considerations.
First, many 3D printers and other related systems were developed before the current AI trend emerged. Businesses operating machines and software that are even a few years old may find it difficult to mesh AI processes into their workflows. They may have to invest in new hardware and software and train their staff to use them, which will require large amounts of funds small businesses, in particular, may not have.
Second, training any AI to carry out tasks depends on large amounts of data, which is often not available for additive manufacturing systems. 3D printing is a highly complex process, and the lack of freely available data limits the speed and efficiency of AI development.
Last, but certainly not least, we run into legal and ethical dilemmas some AI technologies are already grappling with. One of the most significant ones is the question of intellectual property rights.
For example, the U.S. Copyright Office has already ruled that images created entirely through AI are not eligible for copyright protection. Let’s imagine a company developing an aeroplane component through generative design. Can the business claim rights to the part’s design?
We also have to consider whether advancing AI processes will replace human workers. Should manufacturing businesses slash their workforce if AI can perform more or less the same tasks?
What Does the Future Hold?
So, is AI 3D printing possible? Yes — as we’ve seen, it’s already here.
But as I’ve said, the technology is still young and its impact will only grow in the future.
It already offers significant benefits to additive manufacturers. As the systems grow more sophisticated, they will only get better and expand to tasks we may currently consider impossible to automate through machinery.
Generative 3D design, in particular, is bound to change things for 3D printing companies. We can’t yet know whether generating parts entirely through AI processes will ever be possible, but creating even a basic structure for a part can greatly speed up product development.
Picture a situation where an engineer can feed an AI a part’s purpose, material requirements, and possibly a previous, less-efficient example. The AI could then create the basic outline of the part, with the engineer only having to determine places for screw holes and other such details.
Another area where we’re likely to see significant strides is smart 3D printers. As I mentioned, printer manufacturers are already implementing quality control and error reduction technologies into their latest models. More and more brands are bound to introduce these features if they wish to remain competitive in the advancing marketplace.
It’s impossible to tell how far AI 3D printing can go and which developments end up being commercially viable. Yet, the technology is already here and it will make 3D printers smarter than ever before.
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