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

In manufacturing, rework refers to an optional step of a production processwhich aims to eliminate errors or remedy products that do not meet the desiredquality standards. Reworking a production lot involves repeating a previousproduction stage with adjustments to ensure that the final product meets therequired specifications. While offering the chance to improve the yield andthus increase the revenue of a production lot, a rework step also incursadditional costs. Additionally, the rework of parts that already meet thetarget specifications may damage them and decrease the yield. In this paper, weapply double/debiased machine learning (DML) to estimate the conditionaltreatment effect of a rework step during the color conversion process inopto-electronic semiconductor manufacturing on the final product yield. Weutilize the implementation DoubleML to develop policies for the rework ofcomponents and estimate their value empirically. From our causal machinelearning analysis we derive implications for the coating of monochromatic LEDswith conversion layers.

Causally Learning an Optimal Rework Policy