Machine learning is a promising method to model production processes and predict product
quality. It is challenging to accurately model complex systems due to data scarcity, as mass
customisation leads to various high-variety low-volume products. This study conceptualised
knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge
embedded in similar entities to address data scarcity. A sequential cross-product KAET
(SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing …

The progress of machine learning (ML) in the past years has opened up new opportunities
to the field of engineering system modelling. However, successful implementation of ML
algorithms remains challenging, owing to complex system and data scarcity in real-life
design, manufacturing and operation (DMO) problems. Most ML-based DMO models are
confined to single operating condition, limited configurations, single-mode faults, etc. This is
far from the blueprint of smart manufacturing and industrial digital twin that Industry 4.0 …