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.2023.05.016

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…A technique for integrating XAI-derived findings into the Data Science process for building a highly accurate classifier. By employing Synthetic Minority Oversampling Technique (SMOTE) and medoid concepts, XAI tools such as Ceteris Paribus profiles, Partial Dependence, and Breakdown profiles have been utilized to obtain valuable insights [50,51]. Inclusion of XAI in manufacturing sector leads to better performance of machines, quick fault detection and precise methods required for increased production.…”

Section: Xai In Manufacturing Industrymentioning

confidence: 99%

An extensive review on significance of Explainable Artificial Intelligence models in discrete domains for informed decisions making

Agrawal,

Sharma

2024

RIA

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Section: Xai In Manufacturing Industrymentioning

confidence: 99%

An extensive review on significance of Explainable Artificial Intelligence models in discrete domains for informed decisions making

Agrawal,

Sharma

2024

RIA

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“…Kuo et al [ 30 ] applied 2D CNNs on fractional order chaos maps of vibration data for chatter detection. Hanchate et al [ 31 ] proposed a framework for predicting average surface roughness in CNC grinding, employing 2D CNNs on time–frequency spectrogram frames of vibration signals. Furthermore, they implemented an explainable AI methodology to identify the most pertinent time–frequency bands influencing the predictions.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Stathatos,

Tzimas,

Benardos

et al. 2024

Sensors

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This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators—average roughness, peak-to-valley roughness, and diameter deviation—are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model’s ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

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Pre-control of grinding surface quality by data-driven: a review

Fu,

Lv,

Chen

et al. 2024

Int J Adv Manuf Technol

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

Cited by 11 publications

References 18 publications

An extensive review on significance of Explainable Artificial Intelligence models in discrete domains for informed decisions making

An extensive review on significance of Explainable Artificial Intelligence models in discrete domains for informed decisions making

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Pre-control of grinding surface quality by data-driven: a review

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