A Framework for Structural Modelling of an Rfid-Enabled Intelligent Distributed Manufacturing Control System

Barenji, Ali Vatankhah; Barenji, Reza Vatankhah; Hashemipour, Majid

doi:10.7166/25-2-681

Cited by 13 publications

(19 citation statements)

References 35 publications

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“…RFID system consists of three parts namely: RFID reader, RFID antenna and RFID tag. RFID tags offer several broad advantages, including long-distance contact, programmability, generous local storage, and non-line-of-sight scan-ability [9]. In addition, RFID tags' resistance to magnetic interference, water damage, and high temperature make them very suitable for industrial applications [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Robot Control System based on Web Application and RFID Technology

Barenji

Değirmenci

2015

MATEC Web of Conferences

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Abstract. This paper discusses an integration driven framework for enabling the RFID based identification of parts to perform robotic distributor operations in the random mix based parts control based on web application. The RFID technology senses newly arriving parts to be distribution robot, the robot is able to recognize them and perform cooperative distributing via web-based application. The developed web application control system is implemented in the educational robotic arm. RFID system sends real time information from parts to the web application and web based application makes a decision for control of the robot arm, controller of robot controls the robot as based on the decision from web application. The proposed control system has increases the reconfiguration and scalability of robot system.

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Section: Literature Reviewmentioning

confidence: 99%

Robot Control System based on Web Application and RFID Technology

Barenji

Değirmenci

2015

MATEC Web of Conferences

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“…pbest i is the previous best position vector, and gbest i is the global best position vector. Population size is presented by i, and just like the GA, it is assumed to be 100. x is called the constriction coefficient (here, x = 0.8), c 1 and c 2 are the learning rates (here, c 1 = c 2 = 2), and r 1 and r 2 are the random numbers that are uniformly distributed in [0,1].…”

Section: Particle Swarmmentioning

confidence: 99%

“…1 Radio frequency identification (RFID) technology can be addressed as one of the most reliable and efficient solutions for tackling this problem. RFID technology is known as an automatic identification technology because it uses wireless radio frequency waves that are produced by an electromagnetic field to transfer data for tracking and identifying 1 objects.…”

Section: Introductionmentioning

confidence: 99%

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Azizi

Barenji

Hashmipour

2016

Advances in Mechanical Engineering

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Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

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“…Steady state industry has been changed to dynamic industry by recent industrial revolutions, which has resulted in manufacturers being pushed by the global market to reconsider their conventional manufacturing methods [1]. Modern manufacturing needs new manufacturing operations, and effective factory management has great value in this area [2].…”

Section: Introductionmentioning

confidence: 99%

“…Modern manufacturing needs new manufacturing operations, and effective factory management has great value in this area [2]. Recent advances in technology and modern industrial engineering systems from production to transportation have created a great need to track and identify the materials, products, and even live subjects [1]. Radio Frequency Identification (RFID) technology is a reliable and efficient solution to this tracking and identifying problem.…”

Section: Introductionmentioning

confidence: 99%

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Azizi

2017

Complexity

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Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named "hybrid AI optimization technique," to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

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A Framework for Structural Modelling of an Rfid-Enabled Intelligent Distributed Manufacturing Control System

Cited by 13 publications

References 35 publications

Robot Control System based on Web Application and RFID Technology

Robot Control System based on Web Application and RFID Technology

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

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