2011
DOI: 10.1142/s0219686711002181
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Optimization of Process Parameters in Fused Deposition Modeling Using Weighted Principal Component Analysis

Abstract: Fused deposition modeling (FDM) is a process by which functional parts can be produced rapidly through deposition of fused layers of material according to a numerically defined cross-sectional geometry. Literature suggests that process parameters largely influence on quality characteristics of rapid prototyping (RP) parts. A functional part is subjected to different loading conditions in actual practice. Therefore, process parameters need to be determined in such a way that they collectively optimize more than… Show more

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Cited by 79 publications
(44 citation statements)
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“…Fang et al (1998) proposed FDM prototype internal defect detection method using image processing technology. Many scholars have also studied the optimization of process parameters, because layer thickness, temperature, speed and other process parameters have a great impact on the part quality (Garg et al 2014;Thorsten et al 2014;Mohamed et al 2016;Sbriglia et al 2016;Sood et al 2011;Luo et al 2016). Rao et al (2015) used multi-sensor to monitor the manufacturing process and studied the influence on the surface roughness of the parameters, such as material extrusion speed, layer thickness, and extruder temperature.…”
Section: Introductionmentioning
confidence: 99%
“…Fang et al (1998) proposed FDM prototype internal defect detection method using image processing technology. Many scholars have also studied the optimization of process parameters, because layer thickness, temperature, speed and other process parameters have a great impact on the part quality (Garg et al 2014;Thorsten et al 2014;Mohamed et al 2016;Sbriglia et al 2016;Sood et al 2011;Luo et al 2016). Rao et al (2015) used multi-sensor to monitor the manufacturing process and studied the influence on the surface roughness of the parameters, such as material extrusion speed, layer thickness, and extruder temperature.…”
Section: Introductionmentioning
confidence: 99%
“…Functional relationship between process parameters and strength (tensile, flexural, and impact) was determined using response surface methodology with experimental factors of layer thickness, build orientation, raster angle, raster width, and air gap [8]. Sood et al [9] further examined the effect of these same five process parameters on the compressive strength of test specimens and developed a predictive equation. Their results displayed the importance of interfiber bond strength and the control of distortion during the build process.…”
Section: Introductionmentioning
confidence: 99%
“…Due to the nature of FDM printing, the material is extruded through the nozzle at 175 ° C in a build chamber maintained at 50 ° C. As the nozzle deposits material in the X-Y plane by layer to build the sample, temperature differentials exist within the material deposited. Once the material is laid, it cools from 175 °C to the chamber temperature of 50 ° C. An increased temperature gradient exists within the layers as the material being laid is at a higher temperature than already deposited layers, introducing residual stresses consequence of shrinkage (Sood, 2011). Additionally, building the model in layers, allows for poor layeradhesion which adversely affect the mechanical properties (Sood, 2011).…”
Section: Fdm Nylon-12 Tensile Test Experimental Resultsmentioning
confidence: 99%
“…However, currently AM techniques especially FDM are inferior in some cases (Ligon, 2017). The mechanical properties of parts produced via FDM have strong dependence on the process parameters such as road width, raster angle, orientation of build, layer thickness and air gaps have significant influence over strength properties of the 3D printed part (Sood, 2011) (Centre, 2016. As FDM methods deposit semi-molten material directionally onto a build area, it results in anisotropic materials and introduces residual stresses from shrinkage, weak interlayer bonds and interlayer porosity (Sood, 2011).…”
Section: Fused Deposit Modellingmentioning
confidence: 99%
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