Unlocking Additive X.0: AI's Transformative Role in the Next Era of Manufacturing

The Era of Trial and Error: Personal Lessons in the Complexity of LPBF

In my first role as a mechanical engineer, I had the chance to leverage the transformative capabilities of Laser Powder Bed Fusion (LPBF) in the design of complex mud pump components for drilling systems. Although 3D printing technology felt like a savior, enabling designs and engineering feats that were previously impossible, it was a double-edged sword. The process was inherently complex; I often found myself manually recoating or stopping builds due to recoater crashes caused by poor thermal management. We solved these issues by adding extensive support structures, ironically increasing the machining department's workload and making the AM department the subject of jokes. This was the era of Trial and Error in additive manufacturing (AM), a methodology that was costly in terms of labor, materials, and machinery.

The FEA Process Simulation Dilemma: The Mirage of Progress

When I later became a product manager for process simulation software, I was convinced that the power of simulation would be game-changing. However, I underestimated the challenges of transferring CAE technology into a manufacturing environment. Time and cost pressures in manufacturing are unlike those in other sectors, and translating a technology like FEA, matured in aerospace and automotive design, proved far more complex than expected. These predictive systems were no panacea; their ROI often seemed like a distant mirage.

 PID Control: The Incremental Advance with Hidden Costs

The next evolutionary step was PID Control. While providing a semblance of real-time control, these systems were inherently limited. In regulated industries, traceability is paramount, and the fine-tuning required for PID systems often led to application- or machine-specific configurations, complicating scalability and maintenance. The market is unfortunately hard and recent events of leading monitoring technology companies going bankrupted is the proof that the industry is not prioritizing such an approach.

The Rise of AI: The Transformative Leap Forward

Now, we stand at the precipice of a transformative leap with AI. Unlike its predecessors, AI enabled technology like AMAIZE  is disruptive. The category defining products, leveraging Machine Learning algorithms grounded in physics to eliminate the need for specialized hardware and expertise. Engineers will find that the time required to solve complex manufacturing problems will dramatically reduce. AMAIZE illuminates the nature and origins of issues, providing engineers full control over their problem-solving methods. This approach offers much-needed traceability and repeatability, slashing engineering and operational costs by a consistent 50% and enabling unprecedented delivery speeds. This is evolutionally, AI enables engineers to iterate fully digitally without spending a cent on printing and the speed of the prediction is so fast (minutes instead of days) is the secret.

The Horizon: Predictive Reactive Systems in a Real-Time Loop

As we peer into the future, the next evolutionary stage comes into view: Predictive Reactive systems in a Real-Time Loop. These systems promise to combine the predictive power of AI algorithms with real-time adaptability, redefining what's possible in engineering problem-solving. This is the holy grail in the industry, training an AI are enabling the hardware to interact and work together to deliver on a human defined objective function. These system cannot exist without the AI and the closed loop systems, both are available now and I predict that this frontier is not so far-fetched.

Final Thoughts: The Imperative for Transformation

We are at an inflection point. The transition to AI enabled additive manufacturing enabled by commercially available technologies like AMAIZE is not just inevitable; it's imperative for the next wave of innovation and efficiency in additive manufacturing. Organizations that grasp and implement these new methodologies will not only upend existing paradigms but will also redefine the fundamental of costs and profitability in additive manufacturing.

Dr. Omar Fergani

CEO and Founder

1000 Kelvin GmbH