Why 3D Printing Needs To Meet Machine Learning

What could machine learning do to additive manufacturing? [Source: Pixabay]

Artificial intelligence (AI) is revolutionizing countless industries, and its machine learning capabilities are capable of transforming additive manufacturing as well.

When advanced technologies like machine learning and 3D printing meet, it’s most likely to result in smarter and more cost-efficient processes. By combining these two innovations, companies in the additive manufacturing industry can optimize printing while reducing costs.

What Happens When Machine Learning Is Combined With 3D Printing? 

AI’s machine learning and 3D printing are both impressive technologies in their own right, but what happens when machine learning is applied to 3D printing? Simply put, smarter printing. 

Quality Monitoring 

The applications of machine learning are potentially endless. However, one noteworthy application is the automated monitoring of 3D printed parts. Supervised machine learning algorithms can seamlessly monitor the 3D printing process by integrating features such as a camera and image processing. 

Using these tools, machine learning will possess the ability to detect defects during the early stages of the printing process. This capability to spot flaws in the initial stages allows companies to correct the defect as opposed to reprinting parts. 

“Although this technology is still in its infancy, the continuous quality assessment of printed parts is one of the most promising uses for machine learning in this industry,” explains Mandi Kerr, a business blogger at Write my X and 1 Day 2 write. “It could potentially save additive manufacturing operations from unnecessary and expensive reprints.” 

Greater Accuracy 

Another potentially cost-saving application of machine learning in 3D printing is guaranteeing high-precision prints. Researchers from the University of Southern California developed a new machine learning algorithm that improves accuracy called PrintFixer. 

Through machine learning algorithms, the researchers found a way to improve 3D printing accuracy by as much as 50%. In some instances, the improvement in accuracy can be as high as 90%. 

The parts required to print 3D parts are typically very expensive. By reducing the chances of errors and costly reprints, this machine learning algorithm can save companies a substantial amount of money in parts. As a result of the improvement in accuracy, businesses can minimize the chances of errors and decrease the need for highly expensive reprints. 

Since the algorithm reduces the number of drafts necessary to create a part, it can cut down the amount of materials necessary to print a single part as well. This reduces the amount of valuable materials wasted to create a model. 

PrintFixer is also capable of using its printing history to improve the accuracy of future prints. “One of the biggest benefits to machine learning is that it is continuously learning how to improve,” according to Michael Hoskins, a technical writer at Origin Writings and Brit Student. “This means that over time, machine learning algorithms will only continue to increase print accuracy.” 

The potential and implications of this project is revealed by the generous financial backing of many donors. The team is believed to be currently working on the development of an AI model that is capable of predicting even the slightest deviations throughout the printing process. 

The predictive nature of machine learning allows companies to spot and correct errors before they happen. The implications of this for the 3D printing process, which is typically characterized by a large degree of error, and the additive manufacturing industry are astounding. 

Automation

Machine learning might be able to automate a significant portion of the 3D printing workflow. Although humans are still required to supervise machine learning for the most part, some companies are working on a way to increase production capacity by automating several processes. Automating production planning, material selection, and machine utilization can significantly increase productivity. 

Automating certain tasks could also reduce the chances of human error. This can boost the amount of parts printed as well as improve the accuracy of the process. Although this automation technology is still young, it’s definitely one to keep an eye on in upcoming years.

Conclusion  

3D printing is often heralded as the future of manufacturing. It appears as though machine learning is the inevitable future of 3D printing. Combined, the possibilities from these advanced technologies are limitless. Although several machine learning programs for 3D printing are still in its initial stages, their development indicates a bright and AI-based future for printing process.