scholarly journals A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 667
Author(s):  
Mariangela Quarto ◽  
Gianluca D’Urso ◽  
Claudio Giardini ◽  
Giancarlo Maccarini ◽  
Mattia Carminati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Element Model ◽  
Micro Edm ◽  
Swarm Optimization ◽  
Artificial Neural Network Ann ◽  
Edm Drilling

Artificial Neural Network (ANN), together with a Particle Swarm Optimization (PSO) and Finite Element Model (FEM), was used to forecast the process performances for the Micro Electrical Discharge Machining (micro-EDM) drilling process. The integrated ANN-PSO methodology has a double direction functionality, responding to different industrial needs. It allows to optimize the process parameters as a function of the required performances and, at the same time, it allows to forecast the process performances fixing the process parameters. The functionality is strictly related to the input and/or output fixed in the model. The FEM model was based on the capacity of modeling the removal process through the mesh element deletion, simulating electrical discharges through a proper heat-flux. This paper compares these prevision models, relating the expected results with the experimental data. In general, the results show that the integrated ANN-PSO methodology is more accurate in the performance previsions. Furthermore, the ANN-PSO model is faster and easier to apply, but it requires a large amount of historical data for the ANN training. On the contrary, the FEM is more complex to set up, since many physical and thermal characteristics of the materials are necessary, and a great deal of time is required for a single simulation.

Statistical Vibration Based Damage Identification Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Element Model ◽  
Point Estimation ◽  
Structural Vibration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) telah digunakan dengan meluas bagi tujuan mengesan kerosakan dalam struktur menggunakan data–data mod dari gegaran. Walau bagaimanapun, ketidakpastian yang wujud dalam model unsur terhingga dan data dari lapangan yang tidak dapat dielakkan boleh menyebabkan kesilapan dalam meramalkan magnitud dan lokasi kerosakan. Dalam kajian ini kaedah statistik digunakan untuk mengambil kira ketidakpastian ini. ANN digunakan untuk meramalkan parameter–parameter kekukuhan dari frekuensi dan mod bentuk bagi sesebuah struktur. Untuk mengambil kira ketidakpastian dalam ramalan, kaedah statistik digunakan di mana kaedah Rossenblueth point estimation diperbandingkan dengan kaedah Monte Carlo diaplikasikan bagi mengambil kira ketidakpastian ini. Keputusan menunjukkan bahawa dengan mengambil kira ketidakpastian dalam membuat ramalan menggunakan ANN, kerosakan boleh diramalkan pada tahap keyakinan yang tinggi. Kata kunci: Artificial neural network; ketidakpastian; kesilapan rawak Artificial Neural Network (ANN) has been widely applied to detect damages in structures based on structural vibration modal parameters. However, uncertainties that inevitably exist in finite element model and measured vibration data might lead to false or unreliable prediction of structural damage. In this study, a statistical approach is proposed to include the effect of uncertainties in the ANN algorithm for damage prediction. ANN is used to predict the stiffness parameters of structures from measured structural vibration frequencies and mode shapes. Uncertainties in the measured data and finite element model of the structure are considered in the prediction. The statistics of the identified parameters are determined using Rossenblueth’s point estimation method and verified by Monte Carlo simulation. The results show that by considering these uncertainties in the ANN model, the damages can be detected with a higher confidence level. Key words: Artificial neural network; uncertainties; random error

Application of ANN and PSO Swarm Optimization for Optimization in Advanced Manufacturing

2018 ◽  
pp. 386-406 ◽  
Author(s):  
Nehal Dash ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Advanced Manufacturing ◽  
Swarm Optimization ◽  
Cnc Lathe ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

CNC lathe is one of the best machining techniques which provides us with better accuracy and precision. Considering speed, feed and depth of cut as inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as the factors those affect the quality, machining time and cost of machining. Design of experiments (DOE) would be carried out in order to minimize the number of experiments. In the later stages application of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) would be used for the Optimization in the advanced manufacturing considering CNC lathe. The obtained output would be minimized (for surface roughness) and maximized (for MRR) using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO). The combination of various input parameters for the same would be identified and a comparison would be drawn with the various above methods.

Application of ANN and PSO Swarm Optimization for Optimization in Advanced Manufacturing

2022 ◽  
pp. 804-823
Author(s):  
Nehal Dash ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Advanced Manufacturing ◽  
Swarm Optimization ◽  
Cnc Lathe ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

CNC lathe is one of the best machining techniques which provides us with better accuracy and precision. Considering speed, feed and depth of cut as inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as the factors those affect the quality, machining time and cost of machining. Design of experiments (DOE) would be carried out in order to minimize the number of experiments. In the later stages application of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) would be used for the Optimization in the advanced manufacturing considering CNC lathe. The obtained output would be minimized (for surface roughness) and maximized (for MRR) using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO). The combination of various input parameters for the same would be identified and a comparison would be drawn with the various above methods.

Literature Review

2018 ◽  
pp. 67-80
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Literature Review ◽  
Noise Removal ◽  
Swarm Optimization ◽  
Artificial Neural Network Ann ◽  
Intelligent Methods ◽  
Short Explanation

This chapter introduces different resources about noise in heart signals. It also provides a short explanation about artificial neural network (ANN), particle swarm optimization (PSO), and presents some of the previous studies related to heart signal noise removal, intelligent methods for detection of disorders, and feature extraction.

scholarly journals Modified Form of PSO and Its Variation in Channel Equalization

2020 ◽  
Vol 8 (5) ◽  
pp. 3534-3538
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Classification Problem ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Fitness Functions ◽  
Convergence Performance ◽  
Artificial Neural Network Ann ◽  
Equalization Problem

Novelty of PSO is the techniques of parameter improvement. Using many “strategy principles” for the PSO is important for its convergence performance and the optimisation job. In the proposed work, we applied PSO and its advanced form trained with our proposed algorithm for channel equalization. Since particle swarm optimization is matured in the literature ,we apply PSO in its optimized structure trained with radial basis function Artificial Neural Network (ANN). Therefore, this work introduces most favourable design of RBFNN equalizers using OPSO. We treat equalization problem as a classification problem. We assessed a set of fitness functions of modified form of PSO and analysed with it to the presentation of existing PSO .

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