SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components

Freqüência de Candida sp. em biópsias de lesões da mucosa bucal

O objetivo desse trabalho foi determinar a freqüência da infecção por Candida sp. em biópsias de lesões da mucosa bucal, assim como associar a presença de Candida sp. com lesões malignas e lesões com vários graus de displasia. Foram utilizadas 832 biópsias da mucosa bucal, previamente incluídas em parafinas, cujos blocos foram obtidos dos arquivos da Disciplina de Patologia da Faculdade de Odontologia de Araraquara da UNESP, no período entre 1990-2001. Três cortes seqüenciais foram corados pelo ácido periódico de Schiff (PAS). do total de biópsias 27,2% foram PAS positivas, dessas 83,25% eram provenientes de pacientes do sexo masculino. Houve associação positiva entre infecção com displasia epitelial leve, moderada, severa, carcinoma espinocelular e hiperqueratose (p < 0,05). Não houve associação entre hiperplasia fibrosa inflamatória, líquen plano, granuloma piogênico (p < 0,05) com infecções fúngicas. A língua foi o sítio mais acometido por infecções em relação a outros sítios (p < 0,05). A partir dos dados quantitativos, concluiu-se que houve correlação positiva de infecção por fungos, lesões displásicas e carcinoma, sendo mais freqüente no sexo masculino. Estes dados não permitem inferir se o fungo causa displasia epitelial e carcinoma, mas confirmam a maior presença de Candida nessas lesões.Candidosis is the most common fungal infection in the oral cavity, and is usually associated with local and systemic predisposing factors. The ocurrence and relevance of Candidal infection in oral lesions such as liquen planus, leukoplakias and carcinomas are still to be understood. The aim of the present study was to define the frequency of infection by Candida sp. on biopsies of oral mucosal lesions and associate its presence with malignant and dysplastic lesions. Histopathology reports issued between 1990 and 2001 inclusive were reviewed. Three sections of each mucosal biopsy were stained using the periodic acid-Schiff (PAS) technique. From the 832 biopsies 27.2% were PAS positive, of which 83.25% were obtained from male patients. There was positive association between fungic infection and mild, moderate and severe epithelial dysplasia, squamous cell carcinoma and hiperqueratosis (p < 0.05). There was no association between fungic infection and inflammatory fibrous hyperplasia, hyperkeratosis, lichen planus and pyogenic granuloma (p < 0.05). The frequency of infection in the tongue was significantly higher (p < 0.05) than in the other sites. Our results do not show a causal relation between Candida sp. and dysplastic lesions and carcinomas, but do confirm the higher presence of that microrganism in those lesions