English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Machine Learning (ML) is of increasing interest for modeling parametriceffects in manufacturing processes. But this approach is limited to establishedprocesses for which a deep physics-based understanding has been developed overtime, since state-of-the-art approaches focus on reducing the experimentaland/or computational costs of generating the training data but ignore theinherent and significant cost of developing qualitatively accuratephysics-based models for new processes . This paper proposes a transferlearning based approach to address this issue, in which a ML model is trainedon a large amount of computationally inexpensive data from a physics-basedprocess model (source) and then fine-tuned on a smaller amount of costlyexperimental data (target). The novelty lies in pushing the boundaries of thequalitative accuracy demanded of the source model, which is assumed to be highin the literature, and is the root of the high model development cost. Ourapproach is evaluated for modeling the printed line width in Fused FilamentFabrication. Despite extreme functional and quantitative inaccuracies in thesource our approach reduces the model development cost by years, experimentalcost by 56-76%, computational cost by orders of magnitude, and prediction errorby 16-24%.

Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge