Towards Ontology-based Lifecycle Management in Blisk Manufacturing

Schiller, Sven; Landwehr, Markus; Vinogradov, Georg; Dimitriadis, Iraklis; Akyürek, Haydar; Lipp, Johannes; Ganser, Philipp; Bergs, Thomas

doi:10.1016/j.procir.2022.09.085

“…The average F1-score of M3, M4, and M5 presented values of 0.67, 0.44, and 0.42, respectively. For this configuration, M3 presented the best results, however, it is important to remark that the anomalies (14)(15)(16)(17)(18)(19) are not detected. In contrast, M4 and M5 detected the faults (14,17,18), though the average scores are lower than M3 scores.…”

Section: E Discussionmentioning

confidence: 91%

“…There are different ways to build a knowledge base, namely using ontologies [12] [14] [15], knowledge graphs [11] [16], failure mode and effects analysis (FMEA) and engineering knowledge [13] [17], and knowledge-based frameworks for multi-modal and multi-structured data [18] [19] [20] [21]. Notable examples of knowledge-based frameworks can be found in industrial applications for (prescriptive) maintenance [22] [23], machine condition assessment [13], providing work instructions [24], life cycle engineering [18], product lifecycle management [19] [25], and cyber-physical production systems [26] [27]. The fault detection system (FDS) is vital to identify the current state of the machinery or process.…”

Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

“…The testing set consists of 960 rows of data, in which the first 160 rows belong to the normal condition and the rest 800 rows belong to the fault case. Given the prediction difficulty, the fault cases are usually grouped into three categories: easy cases (1, 2, 4, 5, 6, 7, 12, 14, 18), medium cases(8,10,11,13,16,17,19,20) and hard cases (3, 9, 15 and 21)[54]. A detailed dataset description can be found in[53] [28].…”

mentioning

confidence: 99%

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Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Arévalo,

Alison,

Ibrahim

et al. 2024

IEEE Access

0

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Fault detection systems support the operator, providing insight during the decision-making while having an (unknown) fault. Data-based models are a common option for a detection system. However, systems that rely purely on data-based models are normally trained with a specific set of data, which cannot necessarily prevent data drift. Thus, an anomaly or unknown condition detection mechanism is required to handle data with new fault cases. Besides, the model's capability to adapt to the unknown condition is equally important to anomaly detection-in other words, its capability to update itself automatically. Alternatively, expert-centered models are powered by the knowledge of operators, which provides the models with production context and expert domain knowledge. The challenge lies in combining both systems and which framework can be used to achieve this fusion. We propose an adaptive information fusion methodology to define fault detection systems using evidence theory and uncertainty quantification. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.

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Model-Based Controlling Approaches for Manufacturing Processes

Rüppel

¹

,

Ay

²

,

Biernat

³

et al. 2023

Interdisciplinary Excellence Accelerator Series

1

0

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The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP).

show abstract

Model-Based Controlling Approaches for Manufacturing Processes

Rüppel,

Ay,

Biernat

et al. 2023

Interdisciplinary Excellence Accelerator Series

0

View full text Add to dashboard Cite

The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP).

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Towards Ontology-based Lifecycle Management in Blisk Manufacturing

Cited by 5 publications

References 6 publications

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Model-Based Controlling Approaches for Manufacturing Processes

Model-Based Controlling Approaches for Manufacturing Processes

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