Optimization of process parameters in CNC milling of P20 steel by cryo-treated tungsten carbide tools using NSGA-II

Mukkoti, Vishnu Vardhan; Mohanty, Chinmaya P.; Gandla, Sankaraiah; Sarkar, Pallab; P, Srinivasa Rao; B, Dhanraj

doi:10.1080/21693277.2020.1790436

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…In addition, intercalation and milling can quickly remove a large amount of workpiece materials with a large cutting width, feed per tooth, cutting step distance and appropriate spindle speed, which makes the intercalation and milling have low requirements on the machine tool and thus can obtain high processing efficiency on the machine tool with insufficient power [16].…”

Section: Interpolation and Milling Machining Mode And Modelingmentioning

confidence: 99%

Interpolation and Milling Optimization of Titanium Alloy Based on Machine Learning and Multi-objective Algorithm

2020

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With the continuous progress of the society, the manufacturing industry is facing greater challenges and opportunities. Complex, diverse and customized requirements demand greater flexibility and faster response times in manufacturing process systems. As a machining method that can quickly remove metal materials, insert milling has attracted much attention in recent years, and has been widely used in aerospace and die manufacturing industries. In this paper, the interpolation and milling optimization of titanium alloy based on machine learning and multi-objective algorithm is studied. In this paper, taking milling TC4 titanium alloy as an example, a machining optimization model based on multi-objective optimization is proposed to improve the quality and improve the efficiency. DDQN is used to optimize and model the parameters (satisfying the minimum surface roughness, maximum material removal rate and optimal milling force stability). Finally, the effectiveness of the multi-objective algorithm is verified by comparing with the empirical parameters.

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Section: Interpolation and Milling Machining Mode And Modelingmentioning

confidence: 99%

Interpolation and Milling Optimization of Titanium Alloy Based on Machine Learning and Multi-objective Algorithm

2020

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“…In the field of process parameter optimization, some scholars integrate predictive models with optimization models, establishing optimization models with the predictive model as the objective function [18][19][20][21][22][23]. Drawing on the work of these authors, this paper plans to use the NSGA-II algorithm with the predictive results of the BP neural network as the fitness value to explore the optimal production process parameters for the crankshaft abrasive belt polishing machine.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Production Process Parameters for Polishing Machine Tools in Crankshaft Abrasive Belt based on BP Neural Network and NSGA-II

HE,

LI,

et al. 2023

Preprint

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In order to improve the polishing quality of the connecting rod journals of crankshafts and reduce polishing time for abrasive belt polishing machines, a method for optimizing the polishing process parameters for connecting rod journals is proposed, combining BP neural network and NSGA-II algorithm. Firstly, the factors affecting the polishing quality are screened, and in consideration of practical production requirements, a five-factor four-level orthogonal experiment is designed. Then, a BP neural network is used to establish a nonlinear mapping relationship between the polishing process parameters and the polishing quality of the connecting rod journals. The predicted results from the BP neural network are used as fitness values, and the NSGA-II algorithm is employed to obtain the Pareto frontier optimal solution set and the corresponding combination of polishing process parameters. Based on the optimization results, two sets of recommended process parameter schemes are provided. Compared to the initial process parameters of the polishing machine, one group can improve the polishing quality by 0.101 $\mu$ m and reduce the polishing time by 5 seconds, while the other group can achieve an additional polishing time of 10 seconds in exchange for an improvement in polishing quality of 0.151$\mu$m. Finally, experimental validation has been conducted for the two sets of polishing process parameters, and the results align with the optimization expectations.

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Experimental Investigation During Machining of P20 Tool Steel Using EDM

Kumar

Oza²,

Prajapati

et al. 2021

Lecture Notes in Mechanical Engineering

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Optimization of process parameters in CNC milling of P20 steel by cryo-treated tungsten carbide tools using NSGA-II

Cited by 7 publications

References 39 publications

Interpolation and Milling Optimization of Titanium Alloy Based on Machine Learning and Multi-objective Algorithm

Interpolation and Milling Optimization of Titanium Alloy Based on Machine Learning and Multi-objective Algorithm

Optimization of Production Process Parameters for Polishing Machine Tools in Crankshaft Abrasive Belt based on BP Neural Network and NSGA-II

Experimental Investigation During Machining of P20 Tool Steel Using EDM

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