Two-stage approach to causality analysis-based quality problem solving for discrete manufacturing systems

Wang, Haonan; Xu, Yuming; Peng, Tao; Agbozo, Reuben Seyram Komla; Xu, Kaizhou; Liu, Weipeng; Tang, Renzhong

doi:10.1080/09544828.2023.2247858

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…It provides insights into the fundamental interaction mechanisms between product quality and production factors, aiding decision-making in root cause analysis (RCA). In discrete manufacturing quality problem solving, [22] proposes a two-stage approach to tackle the complexities of causal relationships. In the first stage, an improved Bayesian network is used to pinpoint likely root causes directly influencing quality indicators.…”

Section: Causal Discovery In Manufacturing Industrymentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

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In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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Section: Causal Discovery In Manufacturing Industrymentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In discrete manufacturing quality problem solving, Ref. [ 40 ] proposes a two-stage approach to tackle the complexities of causal relationships. In the first stage, an improved Bayesian network is used to pinpoint likely root causes directly influencing quality indicators.…”

Section: Background and Related Workmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Sensors

View full text Add to dashboard Cite

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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3D printed cellulose nanofiber/silica nanoparticle scaffolds for daytime radiative cooling

Shi,

Liu,

Lin

et al. 2024

Additive Manufacturing

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Two-stage approach to causality analysis-based quality problem solving for discrete manufacturing systems

Cited by 6 publications

References 53 publications

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

3D printed cellulose nanofiber/silica nanoparticle scaffolds for daytime radiative cooling

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