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arXiv Machine Learning By Jiarui Zhang, Zhaoyang Guan, Zhixuan Xia, Naichen Shi

Delay-Guided Attention for Quality Prediction in Multistage Manufacturing

Read the original on arXiv Machine Learning →

The paper introduces Delay‑Guided Attention for Multi‑stage Forecasting (DAMF), a method that explicitly models inter‑stage lag distributions in multistage manufacturing to improve quality prediction. DAMF uses a lag predictor to estimate the probability and magnitude of delays between adjacent stages, incorporates these soft distributions into cross‑stage attention, and employs gated residual fusion for information flow. The approach is evaluated on the LiquidSugar dataset and two controlled‑delay benchmarks, showing better quality prediction and more accurate lag recovery compared to baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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