Artificial Intelligence (AI) has entered the mainstream in many industries, but in advanced manufacturing, its real potential is only just beginning to unfold. For additive manufacturing (AM) in particular, where innovation often comes with complexity, AI offers an opportunity to work smarter, test faster and make better decisions from the outset.
At Atomik AM, we are applying AI and machine learning models to accelerate materials development. This means building tools that help us predict outcomes, spot trends in process data, and guide our research efforts in more meaningful directions.
AI is not here to replace materials scientists. It is here to empower them. It gives our team more insight, better starting points and faster iterations so we can focus on what matters most: high-performing, sustainable parts.
1. Why Use AI in Materials Development?
Materials development in additive manufacturing involves a delicate balance of chemistry, process conditions and end-use requirements. Historically, much of this has relied on trial and error, running hundreds of test builds to identify viable combinations of powder, binder, build settings and post-processing.
This approach is not only time-intensive but also expensive and resource-heavy.
AI and machine learning shift this model by enabling:
- Predictive modelling: Algorithms trained on historical data can forecast how a material will behave under certain conditions
- Optimised parameter sets: Instead of guessing, we can begin testing with data-backed configurations
- Faster iteration cycles: Each test feeds the model, improving predictions and reducing the number of failed prints
By feeding in part geometries, powder characteristics, binder properties and more, we are training systems that can suggest starting points with a much higher chance of success. This reduces waste, saves time and shortens the route from idea to application.
2. How Atomik AM Applies Machine Learning
We are building bespoke machine learning tools that sit within our internal research and development workflow. These tools are focused on practical outcomes, not theoretical models.
Key applications include:
- Binder compatibility prediction: Matching binders with powders based on target green strength, sintering behaviour and thermal characteristics
- Shrinkage estimation: Using part geometry and material data to predict shrinkage and dimensional tolerances
- Surface quality forecasting: Analysing binder saturation, powder morphology and build orientation to predict likely defects
- Adaptive testing loops: Allowing experiments to evolve based on real-time results and adjusted expectations
Importantly, our team remains in full control. These models serve as guidance tools, offering starting parameters, highlighting risk areas and suggesting efficiencies. Final decisions are always grounded in engineering judgement and physical testing.
3. The Risks of AI in Advanced Manufacturing
While AI offers significant potential, it is equally important to acknowledge the risks and limitations that come with it — especially in an industry striving for sustainable impact.
Some of the challenges we are actively managing include:
- Data security and confidentiality: Materials data can be commercially sensitive, so protecting intellectual property and customer-specific formulations is essential
- Energy consumption: Training and running machine learning models require significant computing power. Large datasets and high-performance processing can draw on vast energy resources, particularly when cloud infrastructure and data centres are involved. This environmental impact cannot be ignored.
- Data bias: Models are only as accurate as the data they are trained on. Biases in experimental data or underrepresented scenarios can skew results, leading to poor decisions
- Interpretability: Black-box models may offer predictions without clear reasoning. In manufacturing, interpretability and traceability are essential for safety, quality and regulatory compliance
At Atomik AM, we are committed to responsible innovation. We design our models to be effective and focused, not excessive. By working with experienced AI partners like Generative Minds, we ensure that our tools are not just effective, but also developed with sustainability and ethical practice in mind.
We are also transparent about what AI can and cannot do. It is not a fix-all solution, and it comes with trade-offs. But when used thoughtfully, it becomes a valuable tool to support smarter, more sustainable manufacturing decisions.
4. Partnering with AI Specialists
We do not believe in doing everything alone. Collaborating with AI specialists allows us to build smarter, more reliable tools with real impact.
Our partnership with Generative Minds, a leading data scientist with expertise in applied machine learning, has helped us structure and refine our approach to data modelling. Together, we are creating custom tools tailored to the needs of the manufacturing industry, from data handling to model accuracy.
This collaboration bridges the gap between algorithm and application, giving our engineers what they really need: actionable insight.
The Power of AI
AI is not a trend for us. It is a tool that is actively helping us develop better materials and improve process performance. By combining the predictive power of machine learning with the practical knowledge of our team, we are driving forward a smarter, faster and more sustainable future for additive manufacturing.
If your business is exploring ways to reduce development time, improve material outcomes or eliminate inefficiencies, we would love to talk.
