  Most industrial processes in real-world manufacturing applications are
characterized by the scalability property, which requires an automated strategy
to self-adapt machine learning (ML) software systems to the new conditions. In
this paper, we investigate an Electroslag Remelting (ESR) use case process from
the Uddeholms AB steel company. The use case involves predicting the minimum
pressure value for a vacuum pumping event. Taking into account the long time
required to collect new records and efficiently integrate the new machines with
the built ML software system. Additionally, to accommodate the changes and
satisfy the non-functional requirement of the software system, namely
adaptability, we propose an automated and adaptive approach based on a drift
handling technique called importance weighting. The aim is to address the
problem of adding a new furnace to production and enable the adaptability
attribute of the ML software. The overall results demonstrate the improvements
in ML software performance achieved by implementing the proposed approach over
the classical non-adaptive approach.
