Danfoss
Assembly Line
Unlocking the Value Potential of Additive Manufacturing
Transitioning to AM requires not only a change in mindset but more importantly, the ability to quickly and easily identify which parts are best suited for the additive manufacturing process. This is where AI and machine learning are now bridging the gap between traditional AM –where most of its value materializes in the form of functional prototypes – and more advanced additive manufacturing operations. “We have upwards of a million part numbers,” said Werner Stapela, head of global additive design and manufacturing at Danfoss – an international leader in drives, HVAC and power management systems. “So, it would be impossible for us to manually analyze each one to determine whether additive manufacturing would either add value or reduce costs.”
“We have been utilizing 3D printing for decades, mostly for prototyping, but the Castor3D software allows us to focus on our end components and more specifically the costs associated with that,” added Stapela. The software’s algorithm and machine learning can scan thousands of parts at once by analyzing CAD files. It evaluates five factors: materials, CAD geometry, costs, lead time and strength testing to identify suitable parts for AM. The software can also make design for additive manufacturing (DfAM) suggestions regarding part consolidation and weight reduction opportunities.