Modeling and optimization of machining parameters in milling of INCONEL-800 super alloy considering energy, productivity, and quality using nanoparticle suspended lubrication

Hsiao, Te-Ching; Vu, Ngoc-Chien; Tsai, Ming‐Chang; Dang, Xiaohong; Huang, Shyh‐Chour

doi:10.1177/0020294020925842

Cited by 23 publications

(3 citation statements)

References 63 publications

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“…It is the basis of further research on cutting mechanism. The shape and nature of the cutting geometry affect the way of cutting material allowance and the distribution of cutting load for each cutter tooth on the cutting tool, which has an important impact on the formulation of machining technology and the performance indexes such as cutting force, tool life and machining efficiency in the cutting process [18].…”

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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“…Analytical, numerical, and empirical approaches have been used to model the cutting operation. The output of the cutting model, directly or indirectly, is used to estimate machining objective functions, such as process productivity [18], machined part quality [19], production cost [20], tool life [21,22], chatter stability thresholds [23,24], energy consumption [25,26], and carbon emissions [27,28]. Online process optimization is defined as a numerical control process in which the machining parameters are regulated based on time-varying feedback [29].…”

Section: Introductionmentioning

confidence: 99%

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Gohari,

Hassan,

Shi

et al. 2024

Sensors

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The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

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“…The outstanding features of the material can be mentioned as superior toughness, tremendous tensile strength, immeasurable corrosion resistance, and hightemperature strength always attract users to find suitable machining methods to increase the machinability. So, the machining difficult-to-cut materials also revealed a concern for scientists and engineers [2].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Metamodel Based Optimization of Cutting Parameters for Milling Inconel-800 Superalloy Under Nanofluid MQL Condition

Vu¹,

Nguyen²,

Nguyen³

et al. 2023

MMEP

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Machining of Inconel-800, a superalloy material that is difficult-to-cut materials, has received the special attention of many scientists worldwide. This paper adopts the advanced and industry-accepted lubrication method that is minimum quantity lubrication technique (MQL) which enhances nanoparticle particles to improve the machinability of Inconel-800 superalloy material and reduce the quantity of conventional cutting fluids. The metamodel namely Radial basis function (RBF) was used for expressing the relationship between cutting velocity, feed per tooth, depth of cut, and corner radius to two quality factors, including cutting force and specific cutting force. A combination of the RBF approximate model and Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm was applied to find out optimal global solutions for the multi-objective optimization problem. The results show that this study plays a part in supporting scientists and engineers to understand machining difficult-tocut materials better and minimize waste to the environment towards sustainable and environmentally friendly machining.

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Modeling and optimization of machining parameters in milling of INCONEL-800 super alloy considering energy, productivity, and quality using nanoparticle suspended lubrication

Cited by 23 publications

References 63 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

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

Experimental and Metamodel Based Optimization of Cutting Parameters for Milling Inconel-800 Superalloy Under Nanofluid MQL Condition

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