Explainable Machine Learning to Improve Assembly Line Automation

Sheuly, Sharmin Sultana; Ahmed, Mobyen Uddin; Begum, Shahina; Osbakk, Michael

doi:10.1109/ai4i51902.2021.00028

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…While much of the current research is focused on using in-process data to predict failures, there is relatively little research on Explainable Artificial Intelligence (XAI) for clarifying and resolving the causes of failures when they occur. Furthermore, existing XAI that has been studied in manufacturing simply suggests the most relevant features [3,4]. The knowledge of how to modify certain features to normalize the quality of a product still relies heavily on experienced experts [5].…”

Section: Introductionmentioning

confidence: 99%

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Kim,

Seo,

Lee

2024

Electronics

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In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

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Section: Introductionmentioning

confidence: 99%