AI is Playing an Integral Role in Additive Manufacturing

Additive manufacturing, also known as 3D printing, is the process by which a computer creates three-dimensional objects by adding layer upon layer of material. AM has an incredible number of benefits over traditional manufacturing, namely the reduced tooling costs and production time. Additive manufacturing is quickly becoming accessible to smaller businesses, who are finding the cost of entry much more affordable than traditional manufacturing.

Additive manufacturing is responsible for the production of parts in a wide variety of industries, from the aerospace industry to the automotive industry. Even healthcare is being aided by AM’s ability to help doctors create models of cancerous organs that are specific to each individual patient.

This allows surgeons to give more informed pre-operative assessments and guides them during surgery. There’s no limit to the potential of additive manufacturing, and AI is quickly paving the way for its continued growth.

But one of the difficulties of additive manufacturing is the process of dialing in the different variables of your design. When 3D printing, you have to factor in variables such as speed, material, and layer thickness.

This can make it difficult to maintain consistency and find a reliable process for producing the part that you need. Most users find their process through repeated trial and error, but this time-consuming method is quickly being replaced through the use of artificial intelligence and machine learning.

AI is being used by businesses such as Markforged to aid the design process and make additive manufacturing more dependable. Markforged has developed a new tool called Blacksmith that uses AI, 3D scan data and design tools to compare the design of the product with the actual 3D-printed product. This tool then makes automatic adjustments to the design in order to improve the next iteration of the product.

By using AI and 3D scanning technology, Blacksmith is able to refine the scope of what the scanner captures. Blacksmith can focus in on precisely what the scanner should look for and capture only the data relevant to your needs. It can even combat process drift by learning to account for the differences between machines. This allows additive manufacturers to quickly dial in parts and decrease production time, which streamlines the manufacturing process .

Smaller companies have begun using AI to aid additive manufacturing as well. The Denver-based machine shop Faustson Tool began exploring AI as a way to remain relevant to their customers in the aerospace and defense sectors.

In order to make additive manufacturing tenable for them, they ended up partnering with other businesses, academic and public institutions to create the Alliance for the Development of Additive Processing Technologies (ADAPT) at the Colorado School of Mines.

The ADAPT Center uses AI technology to understand the interior composition of 3D-printed parts. They seek to reduce the amount of trial and error involved in additive manufacturing by finding the best machine parameters and choices with the use of AI.

Machine learning in particular is helping them to determine the necessary measurements and additive builds are needed to create a better model. It’s also helping them to determine what the most important findings are in their data so that they can apply these findings on a broader scale.

Machine learning uses algorithms to find the usable patternsin the data that the people working the experiment can then explore.

While human intervention is needed to analyze the patterns that machine learning reveals, the people interpreting the findings are greatly aided by AI’s ability to quickly and accurately detect these patterns.

Senvol is another company that uses AI and machine learning to help with additive manufacturing. Based out of New York, Senvol is a company that’s designed to provide data to help other companies implement additive manufacturing in their business.

They’ve created a machine learning system called “Senvol ML” that aims to reduce the amount of testing for 3D metal printing. Because metal 3D printing has taken manufacturing by storm, and because it involves a number of variables that make testing a costly expense, Senvol’s new system is more important than ever.

The way it works is by analyzing four aspects of the data: process parameter data, process signature data, material property data, and mechanical performance data.

Senvol ML then uses this data to create a mathematical model of the scenario, and then gives the user a prediction of the four aspects of the data.

Once the initial tests are done, Senvol ML can then predict different scenarios to help the user find the correct printing parameters.

By using data from a smaller number of tests to predict a wider range of testing, the AI technology can bypass the need for endless tests and find the solution in a much faster way .

Universities have also joined in on the research into AI’s use in additive manufacturing.

A team of researchers at Carnegie Mellon University created an automated method for identifying metal AM powders. While humans were only able to correctly sort powder images with an accuracy of 50%, their method was able to accurately identify 95% of powders. This ability to identify metal AM powders could give manufacturers the ability to qualify materials much more rapidly, as well as look for any changes in between batches.

There are incredibly minute differences between metal AM powders that would take humans an inordinate amount of time to identify manually. With the help of artificial intelligence to do the brunt work, humans can have more time to develop better parts and improve their product. AI takes away the busy work of 3D printing and helps researches find new advances in additive manufacturing’s potential.

Although many companies have yet to take advantage of artificial intelligence in 3D printing, the amount of research being done on the topic is staggering. AI tools are continually being developed to aid businesses that use additive manufacturing. As more research is done, these tools will become even more widely available.

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Please fill out the form to submit your order.

Upon successful payment, you will receive an email with a Non-Disclosure Agreement (NDA) and a questionnaire regarding your product idea.

Your privacy and security are paramount to us, so rest assured that your information will be handled with the utmost confidentiality.

Step 1: Fill in your contact and billing details.
Step 2: Review your order summary.
Step 3: Submit payment.

After your payment is processed, please check your email for the NDA and questionnaire. Completing these documents promptly will allow us to start your Prior Art Search without delay.

If you have any questions or need assistance with your order, please don’t hesitate to contact us.

318-200-0526 | hello@lanpdt.com

Please fill out the form to submit your order.

Upon successful payment, you will receive an email with a Non-Disclosure Agreement (NDA) and a questionnaire regarding your product idea.

Your privacy and security are paramount to us, so rest assured that your information will be handled with the utmost confidentiality.

Step 1: Fill in your contact and billing details.
Step 2: Review your order summary.
Step 3: Submit payment.

After your payment is processed, please check your email for the NDA and questionnaire. Completing these documents promptly will allow us to start your Prior Art Search without delay.

If you have any questions or need assistance with your order, please don’t hesitate to contact us.

318-200-0526 | hello@lanpdt.com

Additive manufacturing, also known as 3D printing, creates three-dimensional objects by adding layers of material. It offers benefits over traditional manufacturing, such as reduced tooling costs and production time. This technology is becoming more accessible to smaller businesses, making the cost of entry more affordable. It is used in diverse industries like aerospace, automotive, and healthcare.

AI addresses the difficulty of dialing in variables like speed, material, and layer thickness, which can hinder consistency. It replaces time-consuming trial and error. Companies like Markforged use AI tools to compare designs with printed products, making automatic adjustments for improved iterations. This helps maintain consistency and find reliable processes for part production.

Blacksmith is a tool developed by Markforged that uses AI, 3D scan data, and design tools. It compares a product's design with its actual 3D-printed version. Blacksmith then automatically adjusts the design for future iterations, refines scanner scope, and accounts for machine differences. This helps additive manufacturers quickly dial in parts and decrease production time.

Companies like Senvol and ADAPT use machine learning to optimize additive manufacturing. Senvol ML analyzes process parameter, signature, material property, and mechanical performance data to create predictive models, reducing testing for metal 3D printing. ADAPT uses AI to understand part composition and determine optimal machine parameters and choices, reducing trial and error.

Yes, AI can significantly improve the identification of metal additive manufacturing powders. Researchers at Carnegie Mellon University created an automated method that accurately identified 95% of powders. Humans achieved only 50% accuracy. This AI capability allows manufacturers to qualify materials much faster and detect changes between batches, overcoming minute differences challenging for human manual identification.

Additive manufacturing produces enormous amounts of process data per build — layer images, melt-pool signals, thermal history — and very few humans can interpret it in real time. That is where machine learning earns its place. The claims worth examining are the narrow ones: fewer failed builds, faster parameter development, and defects caught during the build instead of at inspection.

Caption: benefit ranges are vendor and case-study reported; they vary widely with material, machine and part geometry.

On client programs, additive is a means to an end — validating geometry quickly through rapid prototyping, then deciding with product engineering whether the production part stays additive or moves to a conventional process.