artificial neural network is one of the most robust and reliable methods in online prediction of nonlinear incidents in machining. tool flank wear as a tool life criterion is an important task which is needed to be predicted during machining processes to establish an online tool life estimation system. in this study, an artificial neural network model was developed to predict the tool wear and ...

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...

Artificial neural networks have emerged as a good candidate to mathematical wear models, due to their capabilities of handling nonlinear behavior, learning from experimental data and generalization. In the present work the potential of using neural networks for the prediction of abrasive wear properties of unfilled and graphite filled carbon fabric reinforced epoxy composite under various testi...

OBJECTIVES The primary aim of this research was to investigate the wear behavior and wear mechanism of five different veneering porcelains. METHODS Five kinds of veneering porcelains were selected in this research. The surface microhardness of all the samples was measured with a microhardness tester. Wear tests were performed on a ball-on-flat PLINT fretting wear machine, with lubrication of ...

The classical wear mechanisms abrasion, fatigue, and adhesion are the most frequent causes of surface changes of ultra high molecular weight polyethylene (UHMWPE) in artificial joints. The counterpart material has a strong influence on the wear and friction behavior of artificial joints due to its abrasive properties and adhesive interaction with UHMWPE. The formation of a transfer layer on the...