Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, bu

Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating conditions. In order to achieve adaptive controller, the Neural Network (NN) self-tuning PID control is proposed in this paper which combines conventional PID controller and Neural Network learning capabilities. The proportional, integral and derivative (K_P, K_I, K_D) gains are self tuned on-line by the NN output which is obtained due to the error value on the desired output of the system under control. The conventio

In this work, the finite element analysis of moving coordinates has been used to study the thermal behavior of the tissue subjected to both continuous wave and pulsed CO2 laser. The results are compared with previously published data, and a good agreement has been found, which verifies the implemented theory. Some conclusions are obtained; As pulse width decreases, or repetition rate increases, or fluence increases then the char depth is decreased which can be explained by an increase in induced energy or its rate, which increases the ablation rate, leading to a decrease in char depth. Thus: An increase in the fluence or decreasing pulse width or increasing repetition rate will increase ablation rate, which will increase the depth of cut

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

The aim of this paper, is to design multilayer Feed Forward Neural Network(FFNN)to find the approximate solution of the second order linear Volterraintegro-differential equations with boundary conditions. The designer utilized to reduce the computation of solution, computationally attractive, and the applications are demonstrated through illustrative examples.

Background: Pervasive Developmental Disorder (PDD) is a term refers to the overarching group of conditions to which autism spectrum disorder (ASD) belongs .

Objective: This study was designed to determine the existing behavior of children with autism in dental sitting, the behavior improvements in recall dental visits and evaluate the improvement in oral hygiene with using specific visual pedagogy chart.

Type of the study: Cross-sectional study.

Methods: Forty children of both genders, ages ranged from 4 – 6 years having primary teeth only were selected whose medical history included a diagnosis