To address the challenges of quality control
caused by the nonlinear coupling of multi-stage and multi-source variations in
the machining of long sleeves for chemical fiber winders, this paper proposes a
method for identifying key quality characteristics (KQCs) based on a
heterogeneous weighted variation propagation network (HWVPN). First, based on
the analysis of the multi-source variation propagation mechanism within the
actual manufacturing process, a topological model of HWVPN is constructed to
integrate both quality characteristics and operating conditions. Second, to
handle the constraint of small sample sizes typical in industrial settings, a
two-stage algorithm combining Elastic Net (EN) and Bayesian Regression (BayR),
termed EN-BayR, is proposed. This algorithm accurately quantifies the variation
propagation weights between nodes via sparse feature selection and
probabilistic posterior inference. Furthermore, a Net-Weighted-NodeRank indicator
is designed to account for both variation amplification and cancellation
effects, facilitating the precise localization of KQCs. An industrial case
study validates that the proposed method achieves a fitting accuracy (R2)
of 98.1%, significantly outperforming traditional regression and ensemble learning
models. The results effectively reveal the evolution laws of machining
variations and identify critical control points for the long sleeve.
DING Siyi1, 2, 3, LIN Jia4, CHEN Wenhua4, ZHANG Jie1, 2, MAO Xinhua3
. Modeling and Analysis of Heterogeneous Weighted Variation Propagation Network
for Multistage Machining of Long Sleeve[J]. Journal of Shanghai Jiaotong University, 0
: 1
.
DOI: 10.16183/j.cnki.jsjtu.2025.381
