Journal Article10.1177/09544089231154959
Roughness detection method based on image multi-features
2
TL;DR: In this paper , a roughness detection approach based on image multi-features was proposed, using part surface images as the research object, using GLCM, Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features.
read more
Abstract: 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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Machine learning investigation of high-k metal gate processes for dynamic random access memory peripheral transistor
Na Young Kwon,Joonho Bang,Won Ju Sung,Jung H. Han,Dongin Lee,Insoo Jung,S. Park,Hyodong Ban,Sangjoon Hwang,Won-Yong Shin,Jinhye Bae,Dongwoo Lee +11 more
TL;DR: Machine learning investigation of high-k metal gate processes for DRAM peripheral transistor explores the relationships between process parameters and electrical properties of HKMG in DRAM, utilizing machine learning techniques to predict and characterize the impact of process parameters on electrical properties.
1
Classification of Metal and Metal Oxide Nanoparticles Using Machine Learning and Deep Learning Models
Parashuram Bannigidad,Namita Potraj,Jalaja Udoshi,Prabhuodeyara M. Gurubasavaraj +3 more
- 02 Aug 2023
TL;DR: Classification of metal and metal oxide nanoparticles using machine learning and deep learning models is developed to automate the process of identifying metals and metal oxides based on texture analysis. The model uses KNN, PNN, LeNet, and ConvXGB classifiers to analyze the texture and classify the nanoparticles. LeNet has the highest accuracy of 95%.
References
An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis
TL;DR: A new approach based on texture analysis is proposed for diagnosing bearing vibration signals and it was observed that the obtained feature had promising results for three different data types and was more successful than the traditional methods.
151
Analysis of the effect of surface roughness on fatigue performance of powder bed fusion additive manufactured metals
TL;DR: In this paper, the effect of surface roughness on fatigue performance of powder bed fusion additively manufactured metals was evaluated in the presence of relatively large internal defects and various microstructures, considering synergistic effect of Ra along with average critical internal defect size in combination with the stress amplitude, resulted in improved correlation of the S-N fatigue data.
128
Influence of surface roughness of PVD coatings on tribological performance in sliding contacts
TL;DR: In this paper, the influence of surface roughness on the tribological performance of two commercial PVD coatings, TiN and WC/C, in sliding contact with the surface was investigated.
126
Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM
Shubhi Sharma,Pritee Khanna +1 more
TL;DR: This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant, and proves the applicability of Zernike moments as a fitting texture descriptor.