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<p>This study presents a novel hybrid Bayesian-optimized CNN–SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis. The hybrid deep learning model, achieves unprecedented accuracy in real-time surface roughness classification and prediction from image data of Inconel 716 superalloy machined surface, eliminating human intervention for feature extraction and selection procedures. The Bayesian optimization algorithm (BOA) was employed to fine-tune the CNN model’s hyperparameters. The derived optimal hyperparameters were used to train the CNN model, which achieved a 92.37% classification accuracy on a test dataset. The classification performance was further enhanced by integrating an SVM classifier achieving an accuracy of 98.21%. Additionally, the model's performance was compared with five state-of-the-art deep learning architectures (AlexNet, ResNet50, VGGNet, Faster R-CNN ResNet50, and CNN), with the proposed model showing superior performance in terms of precision (98.02%), sensitivity (98.40%), and F1-score (98.20%). For surface roughness estimation, the hybrid model demonstrated a 96.89% accuracy in predicting roughness values, highlighting its potential for real-time in-process quality monitoring in industrial applications. These results indicate the proposed hybrid approach demonstrated substantially better performance than other popular object detection models. Hence, the presented work demonstrates an efficient end-to-end approach based on the in-process acquired machined surface image analysis by eliminating human intervention in the process of image processing, feature extraction, and feature selection for the real-time classification and prediction of the surface roughness process.</p>

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A novel hybrid Bayesian-optimized CNN–SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Abdul Arif,
  • Ponugoti Gangadhara Rao,
  • Kalapala Prasad

摘要

This study presents a novel hybrid Bayesian-optimized CNN–SVM deep learning model for real-time surface roughness classification and prediction based on in-process machined surface image analysis. The hybrid deep learning model, achieves unprecedented accuracy in real-time surface roughness classification and prediction from image data of Inconel 716 superalloy machined surface, eliminating human intervention for feature extraction and selection procedures. The Bayesian optimization algorithm (BOA) was employed to fine-tune the CNN model’s hyperparameters. The derived optimal hyperparameters were used to train the CNN model, which achieved a 92.37% classification accuracy on a test dataset. The classification performance was further enhanced by integrating an SVM classifier achieving an accuracy of 98.21%. Additionally, the model's performance was compared with five state-of-the-art deep learning architectures (AlexNet, ResNet50, VGGNet, Faster R-CNN ResNet50, and CNN), with the proposed model showing superior performance in terms of precision (98.02%), sensitivity (98.40%), and F1-score (98.20%). For surface roughness estimation, the hybrid model demonstrated a 96.89% accuracy in predicting roughness values, highlighting its potential for real-time in-process quality monitoring in industrial applications. These results indicate the proposed hybrid approach demonstrated substantially better performance than other popular object detection models. Hence, the presented work demonstrates an efficient end-to-end approach based on the in-process acquired machined surface image analysis by eliminating human intervention in the process of image processing, feature extraction, and feature selection for the real-time classification and prediction of the surface roughness process.