ADDITIVE MANUFACTURING
DfAM can help to optimise the production of legacy parts
UNLOCKING VALUE I
t is hard to believe that 3D printing has been with us for more than 40 years. Yet, additive manufacturing (AM) which can deliver enormous value, is
still greatly underutilised. When properly implemented, the technology can reduce material waste and energy costs, improve part reliability, decrease lead times, reduce or eliminate the need to carry inventory and optimise the production of legacy parts. However, 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 AM process. Tis is where Artificial Intelligence (AI) and machine learning are now bridging the gap between traditional AM – where most of its value materialises in the form of functional prototypes – and more advanced AM operations.
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Still seen primarily as a prototyping technology, additive manufacturing is getting a boost from AI and machine learning software that can help with AM decision making
“We have upwards of a million
part numbers,” says 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 analyse each one to determine whether AM would either add value or reduce costs.” Companies, like Danfoss, that are looking to automate the process of identifying which parts should be moved to AM are increasingly relying on new industrial 3D printing software that can quickly determine AM feasibility. Te platform that Danfoss utilises helps manufacturers unlock the benefits of industrial 3D printing by providing a technical and cost-saving analysis.
Cost analysis helps to optimise part production
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