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Numerical simulation and tool parameters optimization of aluminum alloy transmission intermediate shell

SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Zhao, Haiyue Cao, Yan Bai, Yu Yao, Hui Tian, Chunlei Xian Technol Univ Sch Mechatron Engn 2 Xuefu Middle St Xian 710021 Peoples R China Xian Technol Univ Sch Comp Sci & Engn 2 Xuefu Middle St Xian 710021 Peoples R China

Due to its challenging manufacturing and intricate morphology, the aluminum alloy transmission intermediate shell used in vehicle transmission has been the focus of many academic studies. In this study, the three-dimensional cutting model is condensed to a two-dimensional cutting model and utilized to simulate the finishing process of an aluminum alloy workpiece using the finite element modeling program DEFORM-3D. Through orthogonal testing and range analysis, the impact of integral end mill side edge parameters on cutting performance was investigated. It is determined that tool chamfering has a greater impact on cutting performance than tool rake and relief angles, that chamfering width has the most impact on cutting force, and that chamfering angle has the greatest impact on cutting temperature. The workpiece's surface roughness is tested during a cutting experiment, and an analysis of the data reveals that the finite element simulation model is accurate and the orthogonal test method is reasonable. The tool chamfer has a greater impact on roughness than the tool rake angle and relief angle. The tool settings are further optimized using the firefly method. By examining the data, it is determined that the prediction model is correct and the optimization model is reasonable. The cutting efficiency is higher and the surface quality is better when the chamfer width is 0.17 mm and the chamfer angle is 7.3 degrees or 18.3 degrees. Therefore, optimizing the side edge parameters of the integral end mill during the finishing process of a thin-walled aluminum alloy shell has practical technical value.

关键词： Thin-walled parts shell Numerical simulation Tool parameters optimization Cutting experiment firefly algorithm

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Experimental investigation on fiber laser micro drilling of Titanium grade 5: fabrication, nature-inspired optimization and analysis through image processing

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第2期6卷

作者： Kar, Tuhin Goswami, Arjyajyoti Natl Inst Technol Dept Mech Engn Durgapur India

This investigation showed that micro holes were created on Titanium grade 5 substrate surface using a 30 W fiber laser. The impact of the control factors such as scan speed, frequency, number of passes and power were studied on the responses namely heat affected zone (HAZ), hole circularity (HC) and deviation in diameter (DIV). The control factors were optimized using firefly algorithm. Mathematical models were developed for each response having significant R-square value. 3D surface plots were used to examine how the control parameters affected the response. The firefly algorithm identifies the optimal conditions for micro drilling as scan speed of 210 mm s-1, frequency of 40 kHz, power of 8 W and total of 40 passes which improved experimental findings i.e. HC-0.974, DIV-37.02 mu m and HAZ-19.53. After comparing the predicted values with the experimental findings, it was observed that the prediction error is lowest for HC (1.23%) followed by DIV (13.9%) and HAZ (16.9%). Image processing technique was used to convert regular images into a digital format to extract useful information.

关键词： laser drilling titanium grade 5 multi objective optimization firefly algorithm image processing

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Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Singh, Balraj Sepahvand, Alireza Sihag, Parveen Singh, Karan Prabha, Chander Nag, Anindya Hassan, Md. Mehedi Vimal, S. Kang, Dongwann Panipat Inst Engn & Technol Panipat 132102 India Lorestan Univ Khorramabad *** Iran Chandigarh Univ Mohali 140413 India Graphic Era Dept Civil Engn Dehra Dun 248002 India Chitkara Univ Chitkara Univ Inst Engn & Technol Rajpura Punjab India Khulna Univ Comp Sci & Engn Discipline Khulna Bangladesh Sri Eshwar Coll Engn Ctr Excellence Data Sci Dept Artificial Intelligence & Data Sci Coimbatore 641202 India Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul 01811 South Korea

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with firefly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989;coefficient of determination 0.9612 & 0.9980;root mean square error 0.3036 & 0.3340;Nash-Sutcliffe error 0.9980 & 0.9979;and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predi

关键词： Water quality index Artificial neural network firefly algorithm Gene expression programming Reduced error pruning tree Lorestan Province

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A Study on Load Frequency Control for a Hybrid Power Plant 2

A Study on Load Frequency Control for a Hybrid Power Plant

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2nd IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT)

作者： Datta, Asim Konar, Sourav Singha, L. Joydeep Singh, Ksh Millaner Lalfakzuala, Aldrin Mizoram Univ Dept Elect Engn Aizawl India

ISBN: (纸本)9781509032396

Intermittent nature of renewable energy sources (RESs) greatly influences power generation in a RESs interfaced generating plant. So, in addition to load variations, RESs have an adverse effect on system frequency in such plant. This paper presents a study on load frequency control (LFC) performance in a hybrid power system, where the RESs such as solar photovoltaic (SPV), wind turbine generator (WTG), etc., are integrated to conventional energy sources. Proportional-integral-differential (PID) controllers have been used for LFC and firefly algorithm (FA) is applied for optimization of the controller parameters. The study expose that FA tuned PID controller provides acceptable dynamics. However, accomplishment of the proposed LFC is mostly dependent on tuning of the controller gains.

关键词： Hybrid power system Load frequency control PID controller Optimization technique firefly algorithm

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Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin

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HELIYON 2023年第4期9卷 e15355页

作者： Zerouali, Bilel Santos, Celso Augusto Guimaraes de Farias, Camilo Allyson Simoes Muniz, Raul Souza Difi, Salah Abda, Zaki Chettih, Mohamed Heddam, Salim Anwar, Samy A. Elbeltagi, Ahmed Univ Chlef Fac Civil Engn & Architecture Dept Hydraul Vegetal Chem Water Energy Lab BP 78C Ouled Fares 02180 Chlef Algeria Univ Fed Paraiba Dept Civil & Environm Engn BR-58051900 Joao Pessoa Brazil Univ Fed Campina Grande Acad Unit Environm Sci & Technol BR-58840000 Pombal Brazil Amar Telidji Univ Fac Civil Engn & Architecture Dept Civil Engn Res Lab Water Resources Soil & Environm POB 37-G Laghouat 03000 Algeria Univ 20 Aout 1955 Agron Dept Hydraul Div Route El HadaikBP 26 Skikda 21024 Algeria Egyptian Meteorol Author POB 11784 Cairo Egypt Mansoura Univ Agr Engn Dept Mansoura 35516 Egypt

Characterized by their high spatiotemporal variability, rainfalls are difficult to predict, especially under climate change. This study proposes a multilayer perceptron (MLP) network optimized by Genetic algorithm (GA), Particle Swarm Optimization (PSO), firefly algorithm (FFA), and Tel-econnection Pattern Indices -such as North Atlantic Oscillation (NAO), Southern Oscillations (SOI), Western Mediterranean Oscillation (WeMO), and Mediterranean Oscillation (MO) -to model monthly rainfalls at the Sebaou River basin (Northern Algeria). Afterward, we compared the best-optimized MLP to the application of the Extreme Learning Machine optimized by the Bat algorithm (Bat-ELM). Assessment of the various input combinations revealed that the NAO index was the most influential parameter in improving the modeling accuracy. The results indicated that the MLP-FFA model was superior to MLP-GA and MLP-PSO for the testing phase, presenting RMSE values equal to 33.36, 30.50, and 29.92 mm, respectively. The comparison between the best MLP model and Bat-ELM revealed the high performance of Bat-ELM for rainfall modeling at the Sebaou River basin, with RMSE reducing from 29.92 to 11.89 mm and NSE value from 0.902 to 0.985 during the testing phase. This study shows that incorporating the North Atlantic Oscillation (NAO) as a predictor improved the accuracy of artificial intelligence systems opti-mized by metaheuristic algorithms, specifically Bat-ELM, for rainfall modeling tasks such as filling in missing data of rainfall time series.

关键词： Precipitation Machine learning Multilayer perceptron firefly algorithm Bat algorithm

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Hybrid bio-inspired model for fraud detection with correlation based feature selection

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2021年第5期24卷 1365-1374页

作者： Singh, Ajeet Jain, Anurag Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol New Delhi 110078 India

The credit card is one of the most used forms of online transactions accepted by almost all the service payment systems. The increasing popularity of credit card use is increasing the challenge and fraud cases. This paper proposes a hybrid model based on the feature selection method, firefly bio-inspired algorithm, and random forest ensemble classifier (namely HBRF) for fraud detection and overcomes the imbalanced dataset issue in the banking domain. The firefly bio-inspired algorithm and the correlation-based feature selection (CFS) method have been applied to optimize the predictive features. The proposed HBRF model has been implemented on the Brazilian credit card dataset, and its performance has been compared with machine learning techniques such as Naive Bayes, KNN, Logistic Regression, Support Vector Machine, and existing methods. The proposed HBRF model has achieved an accuracy of 96.23% with a low error rate of 3.77%. It has been identified that the HBRF model outperforms state-of-the-art classifiers and existing techniques.

关键词： firefly algorithm Fraud detection Bio-inspired technique Machine learning techniques Credit card

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Health status identification of catenary based on VMD and FA-ELM

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2021年第15期15卷 -页

作者： Yi, Lingzhi Guo, You Liu, Nian Zhao, Jian Li, Wang Sun, Junyong Xiangtan Univ Hunan Prov Engn Res Ctr Multienergy Collaborat Co Xiangtan Peoples R China Xiangtan Univ Sch Automat & Elect Informat Xiangtan Peoples R China North China Elect Power Univ Beijing Peoples R China State Key Lab Heavy Duty AC Dr Elect Locomot Syst Zhuzhou Peoples R China

Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the catenary based on firefly algorithm optimized extreme learning machine combined with variational mode decomposition is proposed in this paper. Variational mode decomposition is used to decompose the original detection curve of catenary into a series of intrinsic mode function components, and the intrinsic mode function components filtered by the correlation coefficient method after decomposing each detection curve are input into the firefly algorithm optimized extreme learning machine model to realize health status identification. Compared with some other models, the results show that the proposed model has better health status identification effect.

关键词： Catenary firefly algorithm variational mode decomposition extreme learning machine health status identification

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Mathematical approach for the geometrical deformation of fused deposition modelling build parts

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Materials Today: Proceedings 2020年 33卷 5051-5054页

作者： Dipabrata Banerjee Swayam Bikash Mishra Mohammed Sadique Khan M. Ajay Kumar School of Mechanical Engineering KIIT DU Bhubaneswar Odisha India

In the era of current fast moving society, we need to move rapidly with all our attributes like planning, designing, digital manufacturing and marketing etc, to match the growth, Hence the rapid prototyping (RP) is introduced. Among all RP processes, the fused deposition modelling (FDM) is very much significant due to its advancement in printing mechanism. In FDM, 3D complex geometries are printed by adding or depositing material in a layer by layer manner with less human interventions. Since FDM is a parametric dependent process, even a slight modification in process parameters causes large influence over the mechanical characteristics of the built parts. In recent years, various experimental approaches are made to check the effect of process parameters on the performance of FDM built parts. In this research work, effects of following process parameters such as layer thickness, raster width, air gap and part orientation on part deformation are investigated using mathematical approach to check the dependency level. Mathematical models are generated for prediction purpose. This study mainly focuses on the performance measures of warpage deformation, and geometrical accuracy of the FDM build parts. To generate maximum data from less number of experimental run order, a design of experiment (DOE) approach i.e response surface methodology (RSM) technique is adopted. One nature inspired metaheuristic algorithms namely firefly algorithm is adopted to get some optimum parameter settings for the improvement of performance measures.

关键词： FDM RSM firefly algorithm T-test DOE

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Metaheuristic algorithms Applied to Color Image Segmentation on HSV Space

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JOURNAL OF IMAGING 2022年第1期8卷 6-6页

作者： Giuliani, Donatella Univ Bologna Sch Econ Management & Stat I-40126 Bologna Italy

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes' rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

关键词： color image segmentation color image processing metaheuristic algorithms firefly algorithm artificial bee colony algorithm

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Development and validation of software that quantifies the larval mortality of Rhipicephalus (Boophilus) microplus cattle tick

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TICKS AND TICK-BORNE DISEASES 2022年第3期13卷 101930-101930页

作者： Sousa, Isabella C. Figueredo, Weslley K. R. Tavares, Caio P. Gomes, Matheus N. Klafke, Guilherme M. Silva, Aristofanes C. Costa-Junior, Livio M. Fed Univ Maranhao UFMA Parasite Control Lab Sao Luis Maranhao Brazil Fed Univ Maranhao UFMA Appl Comp Core Sao Luis Maranhao Brazil Inst Pesquisas Vet Ctr Pesquisa Saude Anim Secretaria Agr Pecuaria & Desenvolvimento Rural Eldorado Do Sul RS Brazil

Rhipicephalus (Boophilus) microplus is controlled almost exclusively using synthetic acaricides, and reports of resistant populations have been described worldwide. Several time-consuming and laborious toxicological in vitro tests have been used to diagnose acaricidal resistance, especially those that require differential counting of live and dead larvae. Larval mortality is currently done manually and subjectively, which can limit the performance of a large number of tests and comparing results between different laboratories. The present study aimed to develop and validate a new automatic counting method to evaluate tick larval mortality. A software for differentiation of live and dead larvae was developed using different steps: obtaining videos;image segmentation using the firefly algorithm;detection of larvae with the fast radial symmetry transform technique (FRST);and tracking of the larvae at a given time. Larval immersion tests with ivermectin, cypermethrin, and fipronil were performed to validate the developed software. The larval mortality evaluation was performed by (1) recording for 60 s for each package and (2) manual counts of the same sample using three different analysts, each responsible for counting one replicate of each test. All videos obtained were copied and cut at 60, 40, and 20 s for later analysis in the counting software. The median lethal doses (LD50) of the different compounds in each test were calculated for each method (automatic and manual) for different video times. There was no statistical difference in LD50 between manual and automatic count techniques for ivermectin and fipronil. The LD50 of cypermethrin calculated with manual evaluation was up to 2.2 times lower than that of automatic evaluation. The acquisition time of the videos was 2.9-4.4 times faster than the manual evaluation. The average processing time for each video was 5.73 min, regardless of their duration. Thus, the method developed for automatic counting of tick larvae

关键词： Ticks Resistance Automatic counting firefly algorithm FRST

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