Wear Particles Surface Identification Using Neural Network
نویسنده
چکیده
This paper investigates the analysis of microscopic particles generated by wear mechanisms using image processing techniques. Particles are classified using their visual and morphological attributes to predict wear failure in engines and other machinery. The paper describes the stages of identification processing involved including a neural network system to classify wear articles in terms of their surface texture.
منابع مشابه
Predictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models
The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on De...
متن کاملIntelligent tool wear identification based on optical scattering image and hybrid artificial intelligence techniques
Tool wear monitoring is crucial for an automated machining system to maintain consistent quality of machined parts and prevent damage to the parts during the machining operation. A vision-based approach is presented for tool wear identification in finish turning using an adaptive resonance theory (ART2) neural network embedded with fuzzy classifiers. The proposed approach is established upon th...
متن کاملModeling of weld penetration in SAW process in the presence of boehmite nano-particles surface adsorbed by boric acid using MLP-ANN
This paper investigates the effect of boehmite nano-particles surface adsorbed byboric acid (BNBA) along with other input welding parameters such as welding current, arc voltage, welding speed, nozzle-to-plate distance on weld penetration. Weld penetration modeling was carried out using multi-layer perceptron artificial neural network (MPANN) technique. For the sake of training the network, 70%...
متن کاملModeling of weld penetration in SAW process in the presence of boehmite nano-particles surface adsorbed by boric acid using MLP-ANN
This paper investigates the effect of boehmite nano-particles surface adsorbed byboric acid (BNBA) along with other input welding parameters such as welding current, arc voltage, welding speed, nozzle-to-plate distance on weld penetration. Weld penetration modeling was carried out using multi-layer perceptron artificial neural network (MPANN) technique. For the sake of training the network, 70%...
متن کاملDesenvolvimento de um sistema de análise de imagem para quantificação do tamanho e distribuição de partículas de desgaste
This paper describes the development of an image analysis system for wear particles found in industrial equipment lubricating oil. Hence, it was utilized an image acquisition system to capture image samples of the oil held in filter membranes. An analytical methodology was also developed to classify the particles quantitatively and qualitatively, relating them to the wear mode where they had be...
متن کامل