Modeling Mechanical Properties of FSW Thick Pure Copper Plates and Optimizing It Utilizing Artificial Intelligence Techniques

Abstract

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.

Sensing the material by minimally invasive surgery grasper

“…In addition, the smart graspers can potentially be used as educational tools, surgical simulators as well as in Tele-Robotic surgery. Utilizing Artificial Intelligence (AI) techniques such as Neural Networks [12][13][14][15][16][17][18][19] , Fuzzy logic 20 , Ring Probabilistic Logic Neuron 21,22 can be utilized to model the generated data by sensing the material in minimally invasive surgery, and to get the reliable data the AI models can be optimized using Genetic Algorithm 23,24 , Particle Swarm 21 and other AI optimization techniques.…”

A Case Study on Computer-Based Analysis of the Stochastic Stability of Mechanical Structures Driven by White and Colored Noise: Utilizing Artificial Intelligence Techniques to Design an Effective Active Suspension System

“…In 2016, Azizi et al [15,22] utilized RPLNN structure as an optimizer tool to optimize weightless neural networks and RFID networks. Later, the RPLNN structure has been developed and utilized as a part of the hybrid optimizers [13,14,16,24,25].…”

“…Different from previous works, the aim of the current research is presenting an effective approach to eliminate the mentioned fluctuation and control the fuel consumption rate even at the beginning of the control process [11,12]. For this purpose, Ring Probabilistic Logic Neural Network (RPLNN) which is an effective paradigm and has been utilized by researchers as a function optimizer, prediction tool, and plant simulator [13][14][15][16] is adopted here to be trained by the PID controller proposed by Azizi et al [10]. In this paper, MATLAB software and Simulink toolbox have been utilized to develop a simulation-based model of the active suspension system of the vehicle and design an effective RPLNN controller to investigate and reduce the effect of the imposed vibration from the road profile.…”

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

“…This feedback and feed forward connection line, which connects the first and last PLNs, is known as the "ring structure," and because of this property this kind of MPLN network is known as "Ring Probabilistic Logic Neural Network" (RPLNN). In 2016, Azizi et al [45] utilized the RPLNN structure as part of a weightless Neural Network to optimize a weighted Artificial Neural Network model of the mechanical behaviour of the friction stir welding process (see Figure 13). A special structure of this algorithm, in which the inputs and outputs of the RPLNN have been defined, has been implemented by Azizi et al [44] to deal with the static RNP problem and the same model has been used to optimize the proposed dynamic RNP model in this paper.…”