When you print a document, inkjet printers deploy tiny dots of ink to create text and images. Laser additive manufacturing (LAM) works on a similar principle, but in three dimensions. LAM builds objects from the bottom up, printing one precise metal layer at a time according to a digital blueprint.
There’s a downside to the metal powders traditionally used in LAM, however; they're not very eco-friendly, according to Chaolin Tan, a Senior Scientist at A*STAR’s Singapore Institute of Manufacturing Technology (SIMTech).
“LAM-processed commercial metallic materials usually need extensive post-heat treatments (PHT) to achieve good mechanical performance, which releases carbon dioxide and consumes a lot of time and energy,” said Tan.
To address this, Tan and collaborators from the City University of Hong Kong, China, and Pennsylvania State University, US, searched for more sustainable LAM building blocks. They aimed to harness the intrinsic heat treatment (IHT) effect found in LAM printing processes, which naturally strengthens the materials used as they are deposited.
“IHT happens as LAM subjects materials to rapid heating and cooling cycles, creating short temperature spikes that can effectively harden the material as it is deposited,” Tan added.
The challenge lay in finding the right composition of alloys that relies solely on LAM's intrinsic thermal cycles to produce strong end products, eliminating the need for energy-intensive PHT. The team turned to machine learning (ML) for help, using it to analyse vast amounts of data and quickly identify materials with the most promising properties.
Their research led them to develop a novel maraging steel formulation of iron, nickel, titanium and aluminium (Fe-Ni-Ti-Al) using a Random Forest ML model known for its accuracy. This new steel formulation successfully formed hard precipitates during the printing process itself.
When tested, the deposited material achieved an impressive tensile strength of 1538 MPa and a uniform elongation of 8.1 percent, outperforming LAM-printed traditional high-strength steels which usually need PHT to achieve similar parameters.
Tan noted that artificial intelligence techniques like ML are helping to facilitate 4D printing, which integrates what would normally be time-dependent processes like PHT reshaping into the 3D geometry of LAM, cutting manufacturing time significantly. “This not only makes LAM processes more efficient, but also reduces their energy use and carbon footprint,” Tan said.
Tan added that the metallurgical industry is a major emitter of greenhouse gases, with a large portion attributed to heat treatment. “Our approach to sustainable alloy design could significantly reduce the industry’s greenhouse gas emissions, helping it become more environmentally friendly,” said Tan.
The A*STAR-affiliated researchers contributing to this research are from the Singapore Institute of Manufacturing Technology (SIMTech).
