Investigating Grey-Box Modeling for Predictive Analytics in Smart Manufacturing

Yang, Zhuo; Eddy, Douglas; Krishnamurty, Sundar; Grosse, Ian R.; Denno, Peter; Lu, Yan; Witherell, Paul

doi:10.1115/detc2017-67794

Cited by 23 publications

(7 citation statements)

References 14 publications

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“…Unfortunately, white-box models often lack subtle details related to the working environment and do not reach the desired level of accuracy as a result. Where grey-box models have been described for industrial applications, such as powder bed fusion additive manufacturing [14] or for HVAC (heating, ventilation and air conditioning) control systems [15], they have not yet been investigated for mash seam welding quality prediction. Grey-box models combine theoretical knowledge with data-driven methods, resulting in an insightful and accurate model.…”

Section: Related Literaturementioning

confidence: 99%

An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills

et al. 2022

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Annealing and galvanization production lines in steel mills run continuously to maximize production throughput. As a part of this process, individual steel coils are joined end-to-end using mash seam welding. Weld breaks result in a production loss of multiple days, so non-destructive, data-driven techniques are used to detect and replace poor quality welds in real-time. Statistical models are commonly used to address this problem as they use data readily available from the welding machine and require no specialized equipment. While successful in finding anomalies, these statistical models do not provide insight into the underlying process and are slow to adapt to changes in the machine’s or material’s behavior. We combine knowledge-based and data-driven techniques to create an incremental grey-box welding current prediction model for detecting anomalous welds, resulting in a powerful and interpretable model. In this work, we detail our approach and show evaluation results on industrial welding data collected over a period of 15 months containing behavioral shifts attributed to machine maintenance. Due to its incremental nature, our model resulted in two-thirds fewer rejected welds compared to statistical models, thus greatly reducing production overhead. Grey-box modeling can be applied to other welding features or domains and results in models that are more desirable for the industry.

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Section: Related Literaturementioning

confidence: 99%

An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills

et al. 2022

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“…In these cases, the dependability and effectiveness of cybermanufacturing may be questioned, especially in case a delicate and complex situation requires decision-making in a real-time manner (Broy et al 2012). These issues are more common to the newly introduced additive manufacturing (Babu and Goodridge 2015; Yang et al 2017;Dilberoglu et al 2017;Jared et al 2017;Li et al 2018;Mahmoudi et al 2018;Sabbaghi et al 2018;Kenett et al 2019).…”

Section: Incomplete Engineering Knowledgementioning

confidence: 99%

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

et al. 2021

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In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

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“…In other disciplines, e.g. in smart manufacturing or chemical industries, these types of models are referred to as graybox models (Yang et al, 2017;Zendehboudi et al, 2018), combining a white-box (physics-based) with a black-box (data-driven). These models can be configured in three ways: parallel, physics-to-data and data-to-physics, as visualized in Figure 1.…”

Section: Hybrid Prognosticsmentioning

confidence: 99%

Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions

Keizers

Loendersloot

Tinga

2021

IJPHM

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Prognostics gained a lot of research attention over the last decade, not the least due to the rise of data-driven prediction models. Also hybrid approaches are being developed that combine physics-based and data-driven models for better performance. However, limited attention is given to prognostics for varying operational and environmental conditions. In fact, varying operational and environmental conditions can significantly influence the remaining useful life of assets. A powerful hybrid tool for prognostics is Bayesian filtering, where a physical degradation model is updated based on realtime data. Although these types of filters are widely studied for prognostics, application for assets in varying conditions is rarely considered in literature. In this paper, it is proposed to apply an unscented Kalman filter for prognostics under varying operational conditions. Four scenarios are described in which a distinction is made between the level in which real-time and future loads are known and between short-term and long-term prognostics. The method is demonstrated on an artificial crack growth case study with frequently changing stress ranges in two different stress profiles. After this specific case, the generic application of the method is discussed. A positioning diagram is presented, indicating in which situations the proposed filter is useful and feasible. It is demonstrated that incorporation of physical knowledge can lead to highly accurate prognostics due to a degradation model in which uncertainty in model parameters is reduced. It is also demonstrated that in case of limited physical knowledge, data can compensate for missing physics to yield reasonable predictions.

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Investigating Grey-Box Modeling for Predictive Analytics in Smart Manufacturing

Cited by 23 publications

References 14 publications

An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills

An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions

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