Prediction of Powder Injection Molding Process Parameters Using Artificial Neural Networks

Abstract

The parameters of Powder Injection Molding (PIM) process were modeled by artificial neural networks (ANNs). The feed-forward multilayer perceptron was utilized and trained by back-propagation algorithm. Particle size, particle morphology, debinding time, and sintering temperature were taken into account and regarded as inputs of the ANN model. The outputs included relative density, wax loss, shrinkage, and hardness. The results obtained using the ANN model were in good agreement with the experimental data. In fact, they displayed an average R-value of 0.95 versus the experimental values. The optimum architecture of ANN was 7-4-1, in which the network was trained with Levenberg–Marquardt training algorithm. Thus, the ANN model can be used to evaluate, calculate, and forecast PIM process parameters.

Comparative study of topical diltiazem with lateral internal sphincterotomy in management of chronic anal fissure

“…Rajabi MA reported complete pain relief in the 75% patientsof sphincterotomy group and 45% in the diltiazem group two weeks after the onset of treatment which is similar to our results [14]. Knight Js, Birks M, Farouk R reported in a study after 2-3months of treatment with 2 percent topical diltiazem ointment 75 % healing of the fissure [15].…”

Avances en las investigaciones sobre la encapsulación mediante gelación iónica: una revisión sistemática

“…Su estructura química se conforma en una cadena lineal de unidades N-acetil-D-glucosamina unidas por enlaces glicosídicos β-1,4 [48]. Es un biopolímero derivado de la quitina, es altamente aplicable en la industria farmacéutica y alimentaria debido a sus propiedades únicas, como no toxicidad, biocompatibilidad, biodegradabilidad y es extraíble de fuentes económicas [49].…”

“…Se formaron cápsulas esféricas y lisas, un tamaño de cápsulas de 295 nm, un potencial zeta de +50.5 mV y una eficiencia de encapsulación de 52.34 %. El estudio reveló que el aumento en CS y GA mejora la eficiencia de encapsulación, en cambio el aumento en el contenido de azafrán lo redujo, esto se debe a que a mayor contenido de materiales de pared la capa de pared es más gruesa y fuerte [49].…”