Surface roughness is regarded as an essential indicator of the quality of machining. In machining demands, it is often necessary that the surface roughness of the workpiece lies in a specific range. Therefore, it is significant to detect the surface roughness level of the workpiece. For the traditional roughness detection methods with high manual involvement and unable to achieve automation, we propose a new artificial intelligence detection approach. The approach consists of a 1-dimensional convolutional neural network (1DCNN) and a bi-directional GRU network (BiGRU), called the 1DCNN-BiGRU model. 1DCNN-BiGRU accomplishes the detection of roughness levels by classifying surface images directly, without extracting specific roughness features. First, 1DCNN is applied to automate the extraction of roughness-related features along the texture direction of the product surface image. Subsequently, the feature sequences extracted by 1DCNN are fed into BiGRU to learn the overall dependence of the roughness on the sequences. Experiments were performed on a 45steel workpiece roughness image dataset. The 1DCNN-BiGRU model gave 90.60% and 88.06% detection performance on the training and test sets, respectively.
Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.
Background: Metal surface roughness detection is an essential step of quality control in the metal processing industry. Due to the high manual involvement and poor efficiency of traditional roughness testing, rapid automated vision detection has received increasing attention in product quality control. Many methods have focused on extracting features related to roughness from images by means of mathematical statistics. However, these methods often rely on extensive experiments and complex calculations, while being sensitive to external environmental disturbances. background: Metal surface roughness detection is an essential step of quality control in the metal processing industry. Due to the high manual involvement and poor efficiency of traditional roughness testing, the rapid automated vision detection has received increasing attention in product quality control. Many methods have focused on extracting features related to roughness from images by means of mathematical statistics. However, these methods often rely on extensive experiments and complex calculations, while being sensitive to external environmental disturbances. Method: In this paper, a convolution neural network-based approach for metal surface roughness evaluation has been proposed. The convolutional neural network was initialized using a transfer learning strategy, and the data augmentation technique was applied to the benchmark dataset for sample expansion. Results: To evaluate this approach, samples of 4 types of roughness classes were prepared. The samples were divided into a training set, validation set, and test set in the ratio of 7:2:1. The accuracy of the neural network on the test set was found to be above 86%. method: In this paper, we propose a convolution neural network-based approach for metal surface roughness evaluation. We applied migration learning to initialize the convolutional neural network and used data augmentation techniques for sample expansion on the benchmark dataset. Conclusion: The effectiveness of the proposed approach and its superiority over manual detection have been demonstrated in the experiments. other: No more
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