Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Hanchate, Abhishek; Bukkapatnam, Satish T. S.; Lee, Kye Hwan; Srivastava, A. K.; Kumara, Soundar

doi:10.1016/j.jmapro.2023.06.003

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…General techniques exist for interpretation of black-box models, e.g., Shapley Additive Explanations (SHAP) and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI). Specifically for TF images, there exist a number of popular methods to identify the globally important features and their connection to a specific TF area, e.g., GradCAM and Local Interpretable and Model-agnostic Explanation (LIME) [54,55,56].…”

Section: Signal Processing For Machining Optimizationmentioning

confidence: 99%

A Concept from Sensor to Sustainability in Machining – An Interdisciplinary Approach over a Wide TRL Range

Ståhl,

Mikkelsen,

Lenrick

et al. 2024

Advances in Transdisciplinary Engineering

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This publication describes a concept that intends to enable an optimization of machining with regard to the balance between criteria related to technology, economy and sustainability. The work is of a discussion nature and intends to provide a framework for further research and development in the area. Previous research and development during the 80’s and 90’s is presented in general terms and in particular the reasons for its limited success in providing real-time feedback on machining operations are highlighted, despite very large financial investments even by today’s standards. Ongoing research worldwide in current process optimization and its associated building blocks will be highlighted, and identified important work is referenced. Below are new conditions that can be linked to both process knowledge and its modeling, as well as new conditions for developing integrated sensors that can handle the extreme environment in and around a processing operation. A previous limiting factor has been signal processing and signal transmission, which with new knowledge and developed technology in the last 10 years provides new conditions for process optimization in real time.The need for new and up-to-date principles for process optimization, which also integrate sustainability issues and environmental impact, has increased in importance in several respects. Important issues such as tool utilization, efficient use of materials and high time utilization have become relevant as these process results control both energy consumption and environmental impact. The geopolitical development linked to the availability of critical tool materials such as cobalt and tungsten also drives research issues that can generally optimize and streamline production processes.Finally, the publication describes the possibility of realizing a real-time feedback and optimized machining that takes into account technology, economy and sustainability, through interdisciplinary research across several levels of technology readiness (TRL). The results are expected to have positive effects on several production factors before, during and after machining. Developed technologies in machining can also make valuable contributions to the development of other products and production processes.

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Section: Signal Processing For Machining Optimizationmentioning

confidence: 99%

A Concept from Sensor to Sustainability in Machining – An Interdisciplinary Approach over a Wide TRL Range

Ståhl,

Mikkelsen,

Lenrick

et al. 2024

Advances in Transdisciplinary Engineering

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Recent Applications of Explainable AI (XAI): A Systematic Literature Review

Saarela,

Podgorelec

2024

Applied Sciences

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This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria—namely, being recent, high-quality XAI application articles published in English—and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and entertainment. Additionally, emerging applications in law, education, and social care highlight XAI’s expanding impact. The review reveals a predominant use of local explanation methods, particularly SHAP and LIME, with SHAP being favored for its stability and mathematical guarantees. However, a critical gap in the evaluation of XAI results is identified, as most studies rely on anecdotal evidence or expert opinion rather than robust quantitative metrics. This underscores the urgent need for standardized evaluation frameworks to ensure the reliability and effectiveness of XAI applications. Future research should focus on developing comprehensive evaluation standards and improving the interpretability and stability of explanations. These advancements are essential for addressing the diverse demands of various application domains while ensuring trust and transparency in AI systems.

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Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process

Cited by 2 publications

References 18 publications

A Concept from Sensor to Sustainability in Machining – An Interdisciplinary Approach over a Wide TRL Range

A Concept from Sensor to Sustainability in Machining – An Interdisciplinary Approach over a Wide TRL Range

Recent Applications of Explainable AI (XAI): A Systematic Literature Review

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