According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through the conveyor belt motion. An optimal speed controlling mechanism of the conveyor belt is presented by detecting smartly the parts' number and weights using the vision sensor, where the latter will give sufficient visualization about the system. Then image processing will deliver the important data to ANN, which will optimally decide the best conveyor belt speed. This decided speed will achieve the aim of power saving in belt motion. The proposed controlling system will optimally switch the speed of the conveyor belt system to ON, OFF and idle status in order to minimize the consumption of energy in the conveyor belt. As the conveyor belt is fully loaded it moves at its maximum speed. But if the conveyor is partially loaded, the speed will be adjusted accordingly by the ANN. If no loading existed, the conveyor will be stopped. By this way, a very significant energy amount in addition to cost will be saved. The developed conveyor belt system will modernize industrial manufacturing lines, besides reducing energy consumption and cost and increasing the conveyor belts lifetime

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Точкове контактне зварювання (ТКЗ) являє собою один з найбiльш важливих зварювальних процесiв. Якiсть точкового контактного зварювання залежить вiд таких параметрiв процесу, як зварювальний струм, сила електрода i час зварювання, а також вiд їх рiвнiв. У данiй роботi експериментальна частина пiдтверджується моделюванням, де останнє буде використовуватися для прогнозування результатiв для нових даних з досить прийнятним вiдсотком точностi. У цьому дослiдженнi представлена експериментальна робота з точкового контактного зварювання двох однакових листiв аустенiтних нержавiючих сталей (AISI 304), якi припускають утримувати разом в однiй точцi тиском електродiв iз застосуванням високої величини електричного струму, при цьому параметри точкового контактного зварювання (зварювальний струм i час зварювання) можуть змiнюватися, щоб показати вплив кожного параметра на властивостi зварюваного матерiалу (максимальне зусилля зсуву, якому може пiддаватися метал, крiм дiаметра зварної точки площi контакту мiж зварюваними деталями). Експериментальна робота в даному дослiдженнi надає справжнi i важливi данi, якi стануть основою для створення контролера нечiткої логiки (КНЛ). Роль штучного iнтелекту (який представлений контролером нечiткої логiки) полягає в прогнозуваннi оптимальних параметрiв зварюваного матерiалу при будь-яких заданих параметрах точкового контактного зварювання, а також у визначеннi ймовiрностi витiснення, руйнування або розриву в процесi зварювання до його здiйснення, в той час як в цьому дослiдженнi КНЛ прогнозує оптимальне значення максимального зусилля зсуву для ТКЗ, яке виникає при часi зварювання 20 циклiв i зварювальному струмi 8 КA, а оптимальне значення дiаметра зварної точки розраховане КНЛ для ТКЗ забезпечується при часi зварювання 20 циклiв i зварювальному струмi 8 КA. Таке прогнозування дозволить зберегти металевi деталi та зварювальнi електроди, а також заощадить витрати i зусилля Ключовi слова: точкове контактне зварювання (ТКЗ), аустенiтнi нержавiючi сталi (AISI 304), контроль на базi нечiткої логiки (КНЛ)

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, but few of them provided the distribution of RS in a direct and singular way. This work focuses on studying and optimizing the effect of cutting speed, feed rate, and depth of cut for 6061-T3 aluminum alloy on the RS of the surface. The optimum values of geometry parameters have been found by using the L27 orthogonal array. Analysis and simulation of RS by using an artificial neural network (ANN) were carried out to predict the RS behavior due to changing machining process parameters. Using ANN to predict the behavior of RS due to changing machining process parameters is presented as a promising method. The milling process produces more RS at high cutting speed, roughly intermediate feed rate, and deeper cut, according to the results. The best residual stress obtained from ANN is ‒135.204 N/mm2 at a cutting depth of 5 mm, feed rate of 0.25 mm/rev and cutting speed of 1,000 rpm. ANN can be considered a powerful tool for estimating residual stress

The surface roughness (Ra) of machine parts effects significantly the fatigue strength, corrosion resistance and aesthetic appeal of them. Therefore, Ra is an important parameter in manufacturing process. In this research, Ra of Aluminum Al-7075 in milling process is predicted and minimized. Ra minimization has to be in standard mathematical model formula. In order to predict minimum Ra value, developing a model is taken to deal with real Ra experimental data of the milling process. Two model approaches which are Regression and Artificial Neural Network (ANN) are proposed for minimum Ra value prediction. The studied process parameters were: speed of cut, feed rate and depth of cut. Regression and ANN were used to investigate the effect of these parameters on Ra through 27 cases of study, where full Analysis of Ra besides to determining regression equation and optimum process parameters are achieved. This study results show that each of Regression & ANN models had reduced minimum Ra in very similar value by 0.987. This similarity reflects the promise approach of this study in predicting Ra in AL-7075 milling, unlike previous studies that either the regression or the Artificial intelligence method was the dominant in results.