Neural network modeling and analysis of the material removal process during laser machining

To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.     

Predicting surface quality of γ-TiAl produced by additive manufacturing process using response surface method

Electron beam melting (EBM) has been found to be a promising technology for producing complex shaped parts from gamma titanium aluminide alloys (γ-TiAl). The parts produced by this process are projected to have dimensions very close to the desired final shapes. However, the surface roughness of the parts produced by EBM is excessively rough. In many applications, it is necessary to improve the quality of manufactured parts using a convenient post process. This paper determines process parameters of end milling when it is used as a post process for the parts produced by EBM. Design of experiments has been used to study the effect of the selected input parameters of end milling (spindle speed, feed rate, depth of cut and coolant type) on the surface roughness of γ-TiAl parts. Response surface methodology is used to develop a predictive model for surface roughness. Effects of the selected milling process are investigated. This paper also optimizes the selected process parameters to minimize the value of the obtained surface roughness.     

Three-Dimensional Network Polymeric Structure of (2-Amino-5 Ammoniopyridinium)[CuCl4]: Crystal Supramolecularity and Magnetic Properties

A single crystal structure determination of the complex (C₅H₉N₃)CuCl₄ [henceforth I] has been carried out. It crystallizes in the orthorhombic space group Cmca, with the cell parameters a=6.7053(5) Å, b=22.4171(19) Å, c=13.4173(9) Å, V=2016.8(3) Å³, and Z=8. In the complex, the anion is comprised of infinite zigzag chains of monochloro-bridged $\mathrm{CuCl}_{4}^{2-}$ units ?–CuCl₃–μ–Cl–CuCl₃–μ–Cl–CuCl₃–? surrounded by the pyridinium-ammonium cations. The Cu ions are five-coordinate to chloride ions in a distorted square-pyramidal geometry with the zigzag chains formed by corner-sharing polyhedra. The Cu ion is located 0.151 Å above the basal plane. In the crystal, there are four intermolecular hydrogen bonding interactions, linking the CuCl chains to the cations to form a 2D-network. The resulting 2D-networks are further linked by aryl?aryl (π?π) interactions within the cationic chains leading to a 3D-network. Variable temperature magnetic susceptibility data were fit to a one-dimensional S=1/2 antiferromagnetic chain model yielding C=0.44(1) emu-K/mol-Oe and J=?37(1) K.     

Contention aware mobility prediction routing for intermittently connected mobile networks

This paper introduces a novel multi-copy routing protocol, called predict and forward (PF), for delay tolerant networks, which aims to explore the possibility of using mobile nodes as message carriers for end-to-end delivery of the messages. With PF, the message forwarding decision is made by manipulating the probability distribution of future inter-contact and contact durations based on the network status, including wireless link condition and nodal buffer availability. In particular, PF is based on the observations that the node mobility behavior is semi-deterministic and could be predicted once there is sufficient mobility history information. We implemented the proposed protocol and compared it with a number of existing encounter-based routing approaches in terms of delivery delay, delivery ratio, and the number of transmissions required for message delivery. The simulation results show that PF outperforms all the counterpart multi-copy encounter-based routing protocols considered in the study.     

Assessment of quality of fruits using impedance spectroscopy

This study deals with the development of a nondestructive impedance spectroscopic technique that may assess the conditions of the fruits to pluck them with the help of robotic arms. Preliminary investigations are made with the help of two‐terminal probe and an accurate LCR meter. The bulk impedance of mango has been measured to characterise raw and ripe fruits. Effective resistance and effective capacitance vs. frequency characteristics have been determined. The bulk effective resistances, of the ripe fruits, are found to be more than those of the raw fruits, in the frequency range of 1–6 kHz. In the same frequency range, effective capacitances of the raw fruits are found more than those of the ripe fruits. In the light of the data obtained, it can be said that the effective resistance may be used to differentiate between raw and ripe fruits in the frequency range of 1–6 kHz.     

Temperature and Spatiotemporal Variability of Salicylihalamide A in the Sponge <Emphasis Type="Italic">Haliclona</Emphasis> sp.

The production of salicylihalamide A by the marine sponge Haliclona sp. was investigated. Samples of the two morphologies (green and brown) were collected from four locations covering approximately 1,200 km of coastline. Temporal variation between winter and summer was also examined at Bremer Bay. Chemical profiling by using liquid chromatography coupled with ultra violet detection and mass spectrometry showed that salicylihalamide A was produced only by the green morphology. Salicylihalamide A concentration was significantly correlated to water temperature but not to the size or depth of the sponge. Salicylihalamide A concentration was found to differ significantly among locations (Bremer Bay 13.5 μg g⁻¹, Hamelin Bay 11 μg g⁻¹, Rottnest Island 9.9 μg g⁻¹, and Jurien Bay 8.5 μg g⁻¹) partially accounted for by the influence of water temperature. A difference between seasons was also observed in Bremer Bay (summer concentration of 13.5 μg g⁻¹ vs. winter concentration of 8.2 μg g⁻¹). Environmental and physiological factors appear to be important in the production of salicylihalamide A by the green morphology. Additionally, the brown morphology does not produce salicylihalamide A, thus adding to the evidence that this morphology may be a different species.     

El Niño-Southern Oscillation and Rainfall Variability in Central and Southern Sudan

Abstract

In this study, the effects of the El Niño-Southern Oscillation (ENSO) on the rainfall variability in the central (Savannah) and southern (Equatorial) regions of Sudan are examined. The annual rainfall data from 12 rainfall stations for 49 years are used in this examination. The results of the study show that the areal annual regionally averaged rainfall values in the two regions have decreased markedly since the early 1960s, with co-existence between the driest years and the warm ENSO events. The correlation between the annual regional rainfall values and the ENSO events is found to be relatively higher for the Savannah region than for the Equatorial region. Two regional ENSO-rainfall prediction models are developed, one for each region. These models use the ENSO sea surface temperature. The results of the models test show that both models can significantly improve the predictability of the annual rainfall values, which is essential for the planning and the management of water resources in Sudan