  The traditional design and development of metallic alloys has taken a
hill-climbing approach to date, with incremental advances. Throughout the last
century, aluminium (Al) alloy design has been essentially empirical and
iterative, based on lessons learned from in service use and human experience.
Incremental alloy development is costly, slow, and doesn't fully harness the
data that exists in the field of Al-alloy metallurgy. In the present work, an
attempt has been made to utilise a data science approach to develop a machine
learning (ML) model for Al-alloy design. An objective-optimisation process has
also been developed, to exploit the ML model, for user experience and practical
application. A successful model was developed and presented herein, along with
the open-access software.
