Time to Forget Metal AM Build Simulation with an AI CoPilot for 'First Time Right' 3D Printing?
Time to Forget Metal AM Build Simulation with an AI CoPilot for 'First Time Right' 3D Printing?
With AMaize from 1000 Kelvin
Developing machine parameters for metal laser powder bed systems has been a colossal pain for everyone involved. It's not just time-consuming and pricey, but with everyone jealously guarding their 'secret sauce', it puts a damper on metal AM's adoption and ability to scale.
Once a company (or single engineer) finally nails their parameters for a given machine and metal, they then get to wrestle with geometry-specific quirks. These surprises often need specialty simulation to predict and sidestep thermal meltdowns and cut down on catastrophic build failures.
Enter 1000 Kelvin, swaggering in with a promise to skip the tedious trial-and-error AND the simulation song and dance, using hardware and geometry-agnostic machine learning with their AI-driven copilot AMaize aims to churn out ‘first time right’ parts without (as much) of the usual drama.
Co-founder and CEO Omar Fergani will be discussing their AMaize software at CDFAM in Berlin, in advance of which we asked hime a few questions about what this software can do, what data is required to drive it, and what previously simulation dependent engineers need to do to harness its powers.
Could you begin by introducing 1000 Kelvin and telling us about your software?
1000 Kelvin is an AI software company that enables engineers in Additive Manufacturing (AM) to get qualified parts to market faster at lower cost.
1000 Kelvin’s AMAIZE software uses AI models to predict print challenges and correct them directly in the print file, increasing the number of prints that are perfect on the first try.
Process simulation for metal AM has advanced considerably in the last 5-7 years. How does the integration of AI/ML technologies complement or compete with these advancements, and what does AI/ML offer that simulation cannot?
I respectfully disagree, the FEA-based simulation product has not been adopted by the end user as we had hoped, and except for some enhancements to the meshing technology, it has not advanced technically, and there are strong reasons for this.
First, I believe the underlying methods used for these simulations are not truly physics-driven; they are based on an extensive and difficult-to-control calibration procedure due to the nature of the inherent strain method.
Furthermore, most of the simulations focus on macro-level aspects such as distortions and do not predict process-related challenges at the level of the melt pool and track. Computationally, this is not feasible using FEA.
Finally, once you have your prediction, FEA technology cannot provide optimizations and actionable solutions. Most engineers are able to conduct a few physical trials to gain the same insights.
Leveraging AI takes the approach to new heights. With our AMAIZE prediction, we can perform detailed analyses of the print file containing all vectors and process parameters. Moreover, we provide a platform for optimization of the job file. Lastly, thanks to hyper-fast computing and integration with machine OEMs, you can download your file. These capabilities allow engineers to save a significant amount of time and solve their problems swiftly. This is a game-changer, without any domain for comparison.
Could you discuss the origins of your initial training data concerning machine parameters and outcomes, specify what data a customer needs to provide to utilize your software effectively, and explain the measures you take to guarantee that proprietary data provided by customers remains confidential and is not shared with others?
We don’t use customer data for our training. Our models are geometry and machine-independent, but material-dependent.
Customers need to provide AMAIZE with their print file that they will use on the machine (e.g., .SLM, .CLI, Openjz, etc.). We perform our inference and optimization on the print file.
We offer the highest level of IT security and work collaboratively with our customers’ IT departments to meet their requirements. For example, we can deploy on GovCloud to be ITAR compliant.
We exclusively use AWS and deploy on servers as specified by the customer, typically in Germany and the USA. Additionally, for some OEMs, we have implemented additional encryption on the print files.
For instance, when you analyze and correct a print file on AMAIZE, you will specify the machine on which the file must be executed; hence, it cannot be opened on other machines.
Could you provide some real-world examples illustrating how your software has been applied, including specific applications, and detail the time and cost savings achieved through its use?
We have observed our solution being adopted for multiple applications:
AMAIZE is aiding the energy sector in printing complex spare parts that were originally intended to be cast, specifically valves and impellers, without any modification for additive manufacturing (MfAM). The significant impact for our customers is that they have increased the number of spare parts available with a much shorter lead time compared to casting (reduced from months to days, thanks to AMAIZE).
In the automotive industry, we have clients utilizing AMAIZE for printing functional prototypes with far fewer supports (resulting in a 55% reduction for turbocharger housings), aiding in the elimination of post-processing machining and reducing their time to market for development projects. This has become a critical metric in the automotive sector today.
Contract manufacturers are leveraging our solution to expedite their engineering processes and reduce non-recurring engineering (NRE) costs by up to 90% by utilizing our digital iteration workflow, as opposed to dealing with multiple print failures and iterations on the machines. Ultimately, they are not just cutting costs directly but also improving their delivery times, ensuring customer satisfaction, and fostering repeat business.
These are just a few examples that we find exhilarating because they go beyond the simple math of cost-saving. From our perspective, these applications are driving significant growth within our industry.
What indicators should a customer look for to determine when it’s time to move beyond trial and error or simulation methods and start using AMaize?
“I sometimes decline business opportunities because I anticipate that the complexity of the part will require too much engineering and iteration effort.”
“I avoid using FEA simulation because it is too complex and time-consuming.”
“My business is expanding, and I am struggling to recruit application engineers who have the necessary experience.”
“I find that all these digital technologies are expensive, difficult to use, and often overpromise.”
“I have solid business cases, but the cost per part is high, and I need to find a way to reduce it.”
These statements reflect common concerns we aim to address with AMAIZE.
In manufacturing, the most significant recent advancement was the introduction of computer numerical control (CNC), which led to a significant increase in productivity and improved quality and yield.
Finally, what do you hope attendees will take away from your presentation at CDFAM in Berlin, and what are you looking to gain from participating in the event?
Firstly, I am extremely excited to attend CDFAM in my hometown of Berlin. This event is outstanding, with a selection of excellent speakers and a nice venue.
I anticipate a deep and open exchange and collaboration. I hope to effectively communicate that AI represents a tremendous opportunity for our industry and that we can harness its potential during the engineering phases to design and produce sustainable products.
Register to attend CDFAM Berlin to connect with Omar and other experts on the adoption of AI for design, engineering and manufacturing.