Experimental investigation of the effect of carbon black on coefficient of thermal expansion (CTE) of Al/carbon
black composites is presented for electronic packaging applications. Liquid metallurgy casting was used to fabricate Al
base alloy and different wt. % of carbon black reinforced Al black composites. The CTE and thermal properties of both Al
and its composites were measured between 30 and 500 °C is used NETZSCH DIL 402 E and Laser Flash Apparatus
thermal analysis. The result shows that the addition of carbon black into the Al matrix was found to significantly decrease
the thermal expansion. Thermal expansion increases as the function of temperature due to the softening martial phase.
It has been found that thermal conductivity decreases with increase of temperature, but specific heat increases with
temperature.
Rapid prototyping (RP) refers to a class of technology that can automatically construct physical models from
computer aided design (CAD) data. Reduction of product development cycle time is a major concern in industries for
achieving competitive advantage. So, the focus of industries has shifted from traditional product development methodology
to rapid fabrication techniques. The Fused deposition modeling (FDM) is a one of the rapid prototyping (RP) technology
by which physical objects are created directly from CAD model using layer by layer deposition of extruded material.
The quality of FDM produced parts is significantly affected by various parameters used in the process.
In this present work three important process parameter of the FDM process such as layer thickness,
part builds orientation and raster width are considered.. The powerful Taguchi’s method is used for design of experiments
because of it can be provide simplification of design plans and reduced the number of experimental runs. Specimens are
prepared for compressive test and impact test as per ASTM standards. The signal-to-noise (S/N) ratio is used to get the
contribution of each parameter. The validity of process parameters and response is tested by using analysis of variance
(ANOVA). Through this study the main process parameter that affects the quality of prototype can be found.
At this end, Artificial neural network is carried out. The ANN models are developed in order to predict compressive and
impact strength of test specimen. The experimental data and data obtained by ANN is closely correlated which validated
the models. After completing the experiments we have found that the mechanical properties and surface roughness of the
test specimens is increasing with the increase in layer thickness and decrease in the part build orientation.
The major reason for weak strength of FDM processed parts may be attributed to distortion within the layer or between the
layers while building the parts due to temperature gradients