"Our research challenges the long-held belief in the industry that low power diode models cannot achieve sufficient melting" - Dr Kristian Groom, University of Sheffield.

Dr Kamran Mumtaz, one of the inventors of the DAM process, discusses metal additive manufacturing.

Researchers at the University of Sheffield have developed a new additive manufacturing process using energy efficient diode lasers, which could lead to faster, smaller and cheaper 3D printing technologies.

Laser melting system are being adopted more and more by high value sectors, such as the aerospace and automotive industries, to manufacture metallic and plastics parts in layers from powder. Yet, this process is limited as it relies on a mirror to deflect a single laser, curbing the speed of the system.

Sheffield University’s Advanced Additive Manufacturing research unit believes its new Diode Area Melting (DAM) process can overcome the challenges of previous methods by melting large areas while using an array of individual laser diodes. These laser beams can be switched on or off as they move across the powder bed making it faster but also more energy efficient.

“Our research challenges the long-held belief in the industry that low power diode models cannot achieve sufficient melting due to their low power and poor beam quality,” said Dr Kristian Groom, from the Department of Electronic and Electrical Engineering. “Key to the success of the DAM process was a move to shorter wavelength laser array (808nm) where increased absorption of the individually collimated and focused beams allowed melting points in excess of 1400℃ to be reached within a few milliseconds, enabling production of fully dense stainless steel 17-4 parts.”

Inventors of the DAM process, Dr Groom and Dr Kamran Mumtaz, from the Department of Mechanical Engineering, plan to continue the research investigating laser interaction, while broader plans are for scaling-up the system and extending to polymer processing. The team hold hope that it will be possible to combine wavelength-targeted processing of a wide range of materials on one machine.

The research was supported by proof of concept funding from an Engineering and Physical Sciences Research Council (EPSRC) allocated impact acceleration grant (IIKE).

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