The Unique Challenges for Quality Assurance in Additive Manufacturing

Posted: 2025-1-1

Source: TCT Magazine

https://www.tct...-manufacturing/

When baking a cake, what you put in isn’t the same as what you get out. Biting into soft golden sponge doesn’t taste like egg or flour or sugar; it tastes like cake – or it should if you follow the recipe. Additive manufacturing is a little bit like that. You may know your ingredients at the start – the material, the design, the oven you’re using – but there’s chemistry and physics happening in between, a recipe, that causes the part to become something new. It depends heavily on precision. There’s variability – the potential for porosities, warping, surface irregularities – that makes quality and inspection of 3D printed parts a challenge, and as the technology’s adoption in production applications grows, that challenge is only deepening.

In a recent issue of the International Journal of AI for Materials and Design, founded by Professor Wai Yee Yeong, a paper on ‘Machine learning techniques for quality assurance in additive manufacturing processes’ penned by Surajit Mondal and Shankha Shubhra Goswami, stated that "despite its transformative potential, AM poses unique challenges, particularly in the realm of quality assurance." As opposed to the established quality control methods of traditional manufacturing processes – visual inspection, dimensional inspection, non-destructive testing (NDT), for example – "the dynamic and additive nature of AM introduces new complexities and uncertainties that traditional quality assurance methods may struggle to address effectively."

Read the full article at TCT Magazine.