The Effect of Time, Percent of Copper and Nickel on Naturally Aged Al-Cu-Ni Cast Alloys

In this paper, the hardness property during natural age hardening phenomenon for aluminum based alloy has been studied. Different factors play role in aging hardening of aluminum. In this study, the chosen factors were percentages of copper and nickel in aluminum alloys. The specimens were manufactured using casting process, and then heat treatment was carried out for all produced samples together at 550 °C for 3 h before quenching in water. Finally, the specimens were left at room temperature for 936 hours (39days) to allow solute atoms to defuse and form coherent phases to allow the age hardening to take place. The results show that the hardness increased with time in the first 300 hour after the quenching time, and then it remained constant for the rest of the 936 hours. Furthermore, the hardness did not drop until the end of 936 hours which means the over-aging status was not achieved. To get full analysis of the natural aging, design of experiment technique was used to study the effect of %Cu, %Ni and aging time.

Modeling the Drilling Process of Aluminum Composites Using Multiple Regression Analysis and Artificial Neural Networks

In recent years, aluminum-matrix composites (AMCs) have been widely used to replace cast iron in aerospace and automotive industries. Machining of these composite materials requires better understanding of cutting processes re- garding accuracy and efficiency. This study addresses the modeling of the machinability of self-lubricated aluminum /alumina/graphite hybrid composites synthesized by the powder metallurgy method. In this study, multiple regression analysis (MRA) and artificial neural networks (ANN) were used to investigate the influence of some parameters on the thrust force and torque in the drilling processes of self-lubricated hybrid composite materials. The models were identi- fied by using cutting speed, feed, and volume fraction of the reinforcement particles as input data and the thrust force and torque as the output data. A comparison between two prediction methods was developed to compare the prediction accuracy. ANNs showed better predictability results compared to MRA due to the nonlinearity nature of ANNs. The statistical analysis accompanied with artificial neural network results showed that Al2O3, Gr and cutting feed (f) were the most significant parameters on the drilling process, while spindle speed seemed insignificant. Since the spindle speed was insignificant, it directed us to set it either at the highest spindle speed to obtain high material removal rate or at the lowest spindle speed to prolong the tool life depending on the need for the application.

Extracting Depth Information Using a Correlation Matching Algorithm

This manuscript presents a modified algorithm to extract depth information from stereo-vision acquisitions using a correlation based approaches. The main implementation of the proposed method is in the area of autonomous Pick & Place, using a robotic manipulator. Current vision-guided robotics is still based on a priori training and teaching steps, and still suffers from long response time. This study uses a stereo triangulation setup where two Charged Coupled Devices CCDs are arranged to acquire the scene from two different perspectives. The study discusses the details to calculate the depth using a correlation matching routine which programmed using a Square Sum Difference SSD algorithm to search for the corresponding points from the left and the right images. The SSD is further modified using an adjustable Region Of Interest ROI along with a center of gravity based calculations. Furthermore, the two perspective images are rectified to reduce the required processing time. The reported error in depth using the modified SSD method is found to be around 1.2 mm.