Cutting Parameters Optimization Based on ITLBO Algorithm with Big Data Driven

Wang, Chen; Wang, Yi; Wang, Kesheng; Dong, Yao; Zhou, Xueliang; Wang, Shenghuai

doi:10.1109/bigdatacongress.2017.88

Cited by 4 publications

(7 citation statements)

References 7 publications

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“…In [37], a similar algorithm was described but the process and theory of the algorithm were not described in detail. This section expounds on the main part of FBS, expounds on the implementation content of FBS, and standardizes the implementation process of FBS.…”

Section: The Fibonacci Branch Structure and The Fbs Algorithmmentioning

confidence: 99%

Jammer Location‐Aware Method in Wireless Sensor Networks Based on Fibonacci Branch Search

Yang

Shu

et al. 2023

Journal of Sensors

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Due to the sharing and open-access characteristics of the wireless medium, wireless sensor networks (WSNs) can be easily attacked by jammers. To mitigate the effects of a jamming attack, one reliable solution is to locate and remove the jammer from the deployed area within the WSN. To realize the jammer’s localization in the WSN, many range-free methods have been proposed. However, most of these methods are sensitive to the distribution of nodes and the parameters of the jammer. For this reason, a jammer location-aware method based on Fibonacci branch search (FBS) is proposed in this article. First, the interference region is estimated by using the interference region mapping service of sensors in wireless sensor networks. Then, the search point is selected in the jamming area and the fitness function is designed according to the average distance from the search point to the boundary sensor. According to the basic branch structure and interactive search rules, the global optimal solution is obtained in the jamming area. Finally, the position of the search point with the best fitness value is used as the estimation of the jammer position. Compared with the existing typical range-free methods, rich simulation experiments demonstrate that the FBS algorithm is superior in the location-aware method for jammers with a higher precision and a lower sensitivity to the distribution of nodes and the parameters of the jammer, respectively.

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Section: The Fibonacci Branch Structure and The Fbs Algorithmmentioning

confidence: 99%

Jammer Location‐Aware Method in Wireless Sensor Networks Based on Fibonacci Branch Search

Yang

Shu

et al. 2023

Journal of Sensors

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“…For example, the multi-objective parameter optimization model for multi-channel CNC and production cost was established based on the energy efficiency mechanism of the milling process [8]. The multi-objective optimization algorithm was studied based on teaching and learning (ITLBO) to optimize cutting and feed speed process parameters [9]. The multiobjective process parameter optimization model was established based on the transientsteady-state energy consumption mechanism with high-quality, low-energy consumption processing of CNC machine tools as the optimization goal [10].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Optimizing the Process Parameters of the Spindle Process of Universal CNC Machine Tools Based on the Most Probable Explanation of Bayesian Networks

Zhang,

Liu,

Wang

et al. 2023

Processes

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As an essential component of a universal CNC machine tool, the spindle plays a critical role in determining the accuracy of machining parts. The three cutting process parameters (cutting speed, feed speed, and cutting depth) are the most important optimization input parameters for studying process optimization. Better processing quality is often achieved through their optimization. Therefore, it is necessary to study the three cutting process parameters of the CNC machine tool spindle. In this paper, we proposed an improved algorithm incorporated with the beetle antennae search algorithm for the most probable explanation in Bayesian networks to achieve optimization calculation of process parameters. This work focuses on building adaptive dynamic step parameters to improve detection behavior. The chaotic strategy is discretized and used to establish the dominant initial population during the population initialization. This article uses four standard network data sets to compare the time and fitness values based on the improved algorithm. The experimental results show that the proposed algorithm is superior in time and accuracy compared to similar algorithms. At the same time, an optimization example for the actual machining of a universal CNC machine tool spindle was provided. Through the optimization of this algorithm, the true machining quality was improved.

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“…With the development of modern processing technology and computer science, artificial intelligent has been extensively introduced into manufacture fields. Especially in machining, data information are collected and analysed to promote the performance of different kinds of processing, like cutting [1], milling [2], drilling [3] and Wire-EDM [4].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Analysis In Magnetic Field-Assisted Electrical Discharge Machining of High Volume SiCp/Al

Xue

Chen

Zhang

et al. 2022

Preprint

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This paper presents a framework of data-driven intelligence system which can be applied on magnetic field-assisted electrical discharge machining (MF-EDM) machining process for SiC particulate reinforced Al-based metal matrix composites (SiCp/Al) with different high-volume fractions. The implemented system consists of data modelling, predicating, optimization and monitoring modules. A multi-objective moths search (MOMS) optimization algorithm with back-propagation neural network (BPNN) model and multi-hierarchy non-dominated strategy is proposed for tuning optimal processing performance. Data are collected from machining different fraction volumes of SiCp/Al composites by MF-EDM, with peak current, magnetic, pulse width and pulse interval time as input, and material removal rate, electrode wear rate, surface roughness as output. The BPNN model shows the best accuracy compared to K-nearest neighbours, least square support vector machine and Kriging model. To demonstrate the effectiveness of the MOMS optimization algorithm, a set of results is selected as paradigm, which dominates 95.83% original experiments. A verification experiment is also done for an optimized parameter with 65% fraction and 0.2T magnetic. Both result data and three-dimensional surface topography comparison show that the verification experiment result dominates the original experiment of similar input designs.

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Cutting Parameters Optimization Based on ITLBO Algorithm with Big Data Driven

Cited by 4 publications

References 7 publications

Jammer Location‐Aware Method in Wireless Sensor Networks Based on Fibonacci Branch Search

Jammer Location‐Aware Method in Wireless Sensor Networks Based on Fibonacci Branch Search

An Algorithm for Optimizing the Process Parameters of the Spindle Process of Universal CNC Machine Tools Based on the Most Probable Explanation of Bayesian Networks

Data-Driven Analysis In Magnetic Field-Assisted Electrical Discharge Machining of High Volume SiCp/Al

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