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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Optimization in engineering requires appropriate models. In this article, aregression method for enhancing the predictive power of a model by exploitingexpert knowledge in the form of shape constraints, or more specifically,monotonicity constraints, is presented. Incorporating such information isparticularly useful when the available data sets are small or do not cover theentire input space, as is often the case in manufacturing applications. Theregression subject to the considered monotonicity constraints is set up as asemi-infinite optimization problem, and an adaptive solution algorithm isproposed. The method is applicable in multiple dimensions and can be extendedto more general shape constraints. It is tested and validated on two real-worldmanufacturing processes, namely laser glass bending and press hardening ofsheet metal. It is found that the resulting models both comply well with theexpert's monotonicity knowledge and predict the training data accurately. Thesuggested approach leads to lower root-mean-squared errors than comparativemethods from the literature for the sparse data sets considered in this work.

Compensating data shortages in manufacturing with monotonicity knowledge