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.     

Experimental Investigation and Computational Fluid Dynamics Simulation of the Magnetite Concentrate Reduction Using Methane-Oxygen Flame in a Laboratory Flash Reactor

Metallurgical and Materials Transactions B - An experimental investigation of the reduction of magnetite concentrate particles was conducted in a laboratory-scale flash reactor representing a novel...     

Modeling of thermodynamic properties of carrot product using ALO,GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer

Engineering with Computers - In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2 m/s)...     

Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching–learning-based optimization methods

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.

     

Lower-bound time-complexity greening mechanism for duplication-based scheduling on large-scale computing platforms

The Journal of Supercomputing - Large-scale computing platforms become essential in nowadays business and scientific activities. The electrical energy consumed by such platforms increases...     

QoS-enabled TCP for software-defined networks: a combined scheduler-per-node approach

The Journal of Supercomputing - Software-defined network (SDN) can ease the implementation of QoS concept by introducing a flexible and manageable mechanism to overcome the TCP pacing in the...     

Nanoethosomal formulation of gammaoryzanol for skin-aging protection and wrinkle improvement: a histopathological study

Background: Free radical scavengers and antioxidants, with the main focus on enhanced targeting to the skin layers, can provide protection against skin ageing.

Objective: The aim of the present study was to prepare nanoethosomal formulation of gammaoryzanol (GO), a water insoluble antioxidant, for its dermal delivery to prevent skin aging.

Methods: Nanoethosomal formulation was prepared by a modified ethanol injection method and characterized by using laser light scattering, scanning electronic microscope (SEM) and X-ray diffraction (XRD) techniques. The effects of formulation parameters on nanoparticle size, encapsulation efficiency percent (EE%) and loading capacity percent (LC%) were investigated. Antioxidant activity of GO-loaded formulation was investigated in vitro using normal African green monkey kidney fibroblast cells (Vero). The effect of control and GO-loaded nanoethosomal formulation on superoxide dismutase (SOD) and malondialdehyde (MDA) content of rat skin was also probed. Furthermore, the effect of GO-loaded nanoethosomes on skin wrinkle improvement was studied by dermoscopic and histological examination on healthy humans and UV-irradiated rats, respectively.

Results: The optimized nanoethosomal formulation showed promising characteristics including narrow size distribution 0.17?±?0.02, mean diameter of 98.9?±?0.05?nm, EE% of 97.12?±?3.62%, LC% of 13.87?±?1.36% and zeta potential value of –15.1?±?0.9?mV. The XRD results confirmed uniform drug dispersion in the nanoethosomes structure. In vitro and in vivo antioxidant studies confirmed the superior antioxidant effect of GO-loaded nanoethosomal formulation compared with control groups (blank nanoethosomes and GO suspension).

Conclusions: Nanoethosomes was a promising carrier for dermal delivery of GO and consequently had superior anti-aging effect.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach

Water Resources Management - Water resource systems are under enormous pressures globally. To diagnose and quantify potential vulnerabilities, effective modeling tools are required to represent the...