Abstract
Fused filament fabrication (FFF) 3D printing technology allows very complex parts to be obtained at a relatively low cost and in reduced manufacturing times. In the present work, the effect of main 3D printing parameters on roughness obtained in curved surfaces is addressed. Polylactic acid (PLA) hemispherical cups were printed with a shape similar to that of the acetabular part of the hip prostheses. Different experiments were performed according to a factorial design of experiments, with nozzle diameter, temperature, layer height, print speed and extrusion multiplier as variables. Different roughness parameters were measured—Ra, Rz, Rku, Rsk—both on the outer surface and on the inner surface of the parts. Arithmetical mean roughness value Ra and greatest height of the roughness profile Rz are usually employed to compare the surface finish among different manufacturing processes. However, they do not provide information about the shape of the roughness profile. For this purpose, in the present work kurtosis Rku and skewness Rsk were used. If the height distribution in a roughness profile follows a normal law, the Rku parameter will take a value of 3. If the profile distribution is symmetrical, the Rsk parameter will take a value of 0. Adaptive neural fuzzy inference system (ANFIS) models were obtained for each response. Such models are often employed to model different manufacturing processes, but their use has not yet been extended to 3D printing processes. All roughness parameters studied depended mainly on layer height, followed by nozzle diameter. In the present work, as a general trend, Rsk was close to but lower than 0, while Rku was slightly lower than 3. This corresponds to slightly higher valleys than peaks, with a rounded height distribution to some degree.
Highlights
Over the last two decades there has been a huge increase in the demand for orthopedic implants due to an aging population and increasing life expectancy [1]
With regard to additive manufacturing, in recent years, the application of machine learning and soft computing techniques are gaining a growing interest [23], as can be observed in studies such as that of Saleh et al [24], which analyzed the effect of water-silica slurry impacts on polylactic acid (PLA), processed by fused deposition modelling (FDM), by using an Adaptive neural fuzzy inference system (ANFIS) in which building orientation, layer thickness, and slurry impact angle were the inputs and weight gain resulting from water, net weight gain, and total weight gain were the outputs
The roughness parameters analyzed in this present study were the arithmetical mean roughness value or arithmetical mean of the absolute values of the profile deviations from the mean line of the roughness profile ( Ra), which is one of the most commonly employed parameters in industry; the mean roughness depth or average maximum peak to valley of five consecutive sampling lengths of the profile within a sampling length ( Rz); the kurtosis ( Rku), which is a measure of the sharpness of the profile; and the skewness ( Rsk), which measures the symmetry of the profile
Summary
Over the last two decades there has been a huge increase in the demand for orthopedic implants due to an aging population and increasing life expectancy [1]. With regard to additive manufacturing, in recent years, the application of machine learning and soft computing techniques are gaining a growing interest [23], as can be observed in studies such as that of Saleh et al [24], which analyzed the effect of water-silica slurry impacts on polylactic acid (PLA), processed by fused deposition modelling (FDM), by using an ANFIS in which building orientation, layer thickness, and slurry impact angle were the inputs and weight gain resulting from water, net weight gain, and total weight gain were the outputs. For inert ceramics, which can be employed to manufacture prostheses
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