PAF-Net: Phase-Aligned Frequency Decoupling Network for Multi-Process Manufacturing Quality Prediction

TL;DR

PAF-Net introduces a novel frequency domain approach for multi-process manufacturing quality prediction, effectively addressing temporal misalignment, heterogeneous features, and shared frequency dependencies, leading to superior predictive accuracy.

Contribution

It proposes a frequency decoupled framework with phase alignment, frequency independent attention, and frequency decoupled cross attention for improved quality prediction.

Findings

Outperforms 10 baselines with 7.06% lower MSE

Achieves 3.88% lower MAE

Demonstrates effectiveness on 4 real-world datasets

Abstract

Accurate quality prediction in multi-process manufacturing is critical for industrial efficiency but hindered by three core challenges: time-lagged process interactions, overlapping operations with mixed periodicity, and inter-process dependencies in shared frequency bands. To address these, we propose PAF-Net, a frequency decoupled time series prediction framework with three key innovations: (1) A phase-correlation alignment method guided by frequency domain energy to synchronize time-lagged quality series, resolving temporal misalignment. (2) A frequency independent patch attention mechanism paired with Discrete Cosine Transform (DCT) decomposition to capture heterogeneous operational features within individual series. (3) A frequency decoupled cross attention module that suppresses noise from irrelevant frequencies, focusing exclusively on meaningful dependencies within shared bands. Experiments on 4 real-world datasets demonstrate PAF-Net's superiority. It outperforms 10 well-acknowledged baselines by 7.06% lower MSE and 3.88% lower MAE. Our code is available at https://github.com/StevenLuan904/PAF-Net-Official.