Ahmad Turki Mayyas

Drilling of Al—Mg—Cu alloys and Al—Mg—Cu/SiC composites

Metal matrix composites are widely used in engineering applications including automotive, aircraft, and military industries. In this study, different Al—4 wt%Mg—Cu alloys, and Al—4 wt%Mg—Cu/SiC composites were drilled on a vertical drilling machine using moderate speed and general purpose high-speed steel tools. The machinability parameters studied in this research were drilling forces (torque and thrust force) and surface roughness of the drilled holes. The results showed that the effect of the addition of copper as alloying element (up to 5 wt%) to Al—4 wt%Mg tends to decrease torque and thrust force. Also, it was found that the addition of SiCP (up to 10 vol.%) to the Al—4 wt%Mg—Cu alloys had little effect on the drilling torque and thrust force, but tends to improve the surface roughness of the drilled surfaces. Moreover, the analysis of the produced chips indicates that most of the produced chips by dry drilling of aluminum-based materials were of continuous type.

Sustainable lightweight vehicle design: a case study of eco-material selection for body-in-white

Sustainable product development when applied to an automotive structure requires a balanced approach towards technological, economic and ecological aspects. Current paper investigates the main input parameters and the different measures for the vehicular structures design for sustainability (DFS) in general and its material selection for sustainable lightweight design in particular. This study proposes a set of metrics for material selection that takes all sustainability aspects into consideration. These metrics include: products’ environmental impact, functionality and manufacturability, in addition to the economic and societal factors. The manuscript then proceeds to show the material selection methodology and its limitations.

Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks

The design of blanking processes requires the availability of a procedure able to deal with both tooling and mechanical properties of the workpiece material (blank thickness, hardness, ductility, etc.). This research presents the development and comparison of two models to predict the quality of the blanked edge represented by burrs height, the first model is an artificial neural network (ANN) based, while the second model is a multiple regression analysis (MRA) based. Finite Element modeling of the blanking process was used to generate the data for both models. Both ANN and MRA are able to give good prediction results, however, ANN still more accurate because it deals efficiently with hidden nonlinear relations when compared to MRA. The comparison between experimental and model results shows that average absolute relative error in the case of ANN was <2.20% for carbon steel and 4.85% for corrosion-resistant steel (CRES) compared to 15.18% for carbon steel and 14.22% for CRES obtained from the second order MRA. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.