Process variable

About: Process variable is a research topic. Over the lifetime, 3983 publications have been published within this topic receiving 43130 citations. The topic is also known as: process parameter.

A Study on the Influence of Process Parameters on the Viscoelastic Properties of ABS Components Manufactured by FDM Process

Abstract: Fused Deposition Modelling (FDM) is a fast growing Rapid Prototyping (RP) technology due to its ability to build parts having complex geometrical shape in reasonable time period. The quality of built parts depends on many process variables. In this study, the influence of three FDM process parameters namely, slice height, raster angle and raster width on viscoelastic properties of Acrylonitrile Butadiene Styrene (ABS) RP-specimen is studied. Statistically designed experiments have been conducted for finding the optimum process parameter setting for enhancing the storage modulus. Dynamic Mechanical Analysis has been used to understand the viscoelastic properties at various parameter settings. At the optimal parameter setting the storage modulus and loss modulus of the ABS-RP specimen was 1008 and 259.9 MPa respectively. The relative percentage contribution of slice height and raster width on the viscoelastic properties of the FDM-RP components was found to be 55 and 31 % respectively.

Application of artificial intelligence to optimize the process parameters effects on tensile properties of Ti-6Al-4V fabricated by laser powder-bed fusion

Abstract: Laser powder-bed fusion (LPBF) process, as one of the most widely used technologies of additive manufacturing, enables fabrication of parts with intricate geometries. The choice of process parameters in this technology plays a major role in defining the microstructural, mechanical and surface properties of the fabricated parts. In this study, the effects of LPBF process parameters on static tensile properties (including yield strength and ultimate tensile strength and elongation) of Ti-6Al-4V samples were investigated using artificial intelligence methods. Deep learning approach was employed by using neural networks for prediction, optimization and parametric and sensitivity analyses. Relevant experimental data available in the literature were collected to feed the network. Stacked auto-encoder was assigned to the networks for high accuracy pre-training. LPBF process parameters including laser power, scanning speed, hatch spacing, layer thickness and sample direction were regarded as inputs while yield strength, ultimate strength and elongation were considered as outputs of the neural networks. The obtained results indicate the high potential of neural networks to be used as a powerful tool for process parameter optimization for enhanced mechanical performance of additive manufactured parts.

Membrane control system and process

Abstract: A membrane system controller and control method for maximizing retentate product output and incorporating compressor load detection apparatus and process parameter instrumentation for feeding essential monitoring information to a control unit. Product output maximization is accomplished by increasing the membrane operating temperature during periods when compressor capacity is underutilized.