Philipp Schwarz, Oliver Schacht, Sven Klaassen, Daniel Grünbaum, Sebastian Imhof, Martin Spindler
arXiv 17 Jun 2024 · Machine Learning · 2 citations (OpenAlex)
arXiv:2406.11308 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based system that allows for optional rework steps. While the rework decision depends on an intermediate state of the lot and system, the final product inspection, and thus the assessment of the actual yield, is delayed until production is complete. Repair steps are applied uniformly to the lot, potentially improving some of the individual items while degrading others. The challenge is thus to balance potential yield improvement with the rework costs incurred. Given the inherently causal nature of this decision problem, we propose a causal model to estimate yield improvement. We apply methods from causal machine learning, in particular double/debiased machine learning (DML) techniques, to estimate conditional treatment effects from data and derive policies for rework decisions. We validate our decision model using real-world data from opto-electronic semiconductor manufacturing, achieving a yield improvement of 2 - 3% during the color-conversion process of white light-emitting diodes (LEDs).
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Daryl Powell, Maria Chiara Magnanini, Marcello Colledani, and Odd My… (2021) Advancing zero defect manufacturing: A state-of-the-art perspective and future research directions | 0.874 | 5 | 2 | 100% |
| 2 | Paul F. Zantek, Gordon P. Wright, and Robert D. Plante (2002) Process and product improvement in manufacturing systems with correlated stages | 0.874 | 5 | 2 | 100% |
| 3 | Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, and Ricardo Silva (2022) Causal machine learning: A survey and open problems | 0.843 | 3 | 3 | 100% |
| 4 | M. Colledani, D. Coupek, A. Verl, J. Aichele, and A. Yemane (2014) Design and evaluation of in-line product repair strategies for defect reduction in the production of electric drives | 0.811 | 4 | 2 | 100% |
| 5 | Susan Athey and Stefan Wager (2021) Policy learning with observational data | 0.811 | 4 | 2 | 100% |
| 6 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.811 | 4 | 2 | 100% |
| 7 | Daniel F. McCaffrey, Beth Ann Griffin, Daniel Almirall, Mary Ellen S… (2013) A tutorial on propensity score estimation for multiple treatments using generalized boosted models | 0.737 | 4 | 2 | 75% |
| 8 | Marcello Colledani and Alessio Angius (2020) Production quality performance of manufacturing systems with in-line product traceability and rework | 0.737 | 3 | 2 | 100% |
| 9 | Florian Eger, Daniel Coupek, Davide Caputo, Marcello Colledani, Mari… (2017) Zero defect manufacturing strategies for reduction of scrap and inspection effort in multi-stage production systems | 0.737 | 3 | 2 | 100% |
| 10 | Meir J. Rosenblatt and Hau L. Lee (1986) Economic production cycles with imperfect production processes | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 52 scored citations.