Fatigue failure analysis of ceramic tools in intermittent cutting harden steel based on experiments and finite element method

Ni, Xiaowu; Zhao, Jun; Gong, Feng; Li, Gang

doi:10.1177/0954405419883042

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…The contact force model in the collision process can be obtained from Equation ( 13), and solving the contact collision problem is the primary condition for using the equivalent spring damping method. For solving the damping coefficient, Equation ( 17) adopts the hysteretic damping model, which can be specifically expressed as: e Du   (17) Where u is the hysteretic damping factor.…”

Section: Impact Force Modelling 221 Normal Contact Force Modellingmentioning

confidence: 99%

Research on Breakage Characteristics in Side Milling of Titanium Alloy with C emented Carbide End Mill

Yue

et al. 2021

Preprint

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Aiming at the breakage of tool and low precision of the machined surface in the high-speed milling process of titanium alloy, damage mechanics is used to reveal the formation mechanism of tool fatigue breakage during the milling and determine the critical condition of tool breakage. Cutting edge chipping caused by random impact fracture during the evolution of tool damage is the main failure form of tool fatigue breakage. Based on continuous damage mechanics, fatigue crack growth theory and sliding crack energy balance equation, the crack growth law of tool material is studied under different cutting impact, and the initial damage value and critical damage value of tool material fracture based on the interval method are obtained. And the impact fracture limit conditions of the end mill edge are established including cutting parameters, material hardness, tool damage, tool wear, and cutting impact, which provide a theoretical basis for determining the cutting parameters. A titanium alloy milling experiment is carried out to define the impact damage morphology of the tool in different states after the tool is damaged. The obtained tool safety area range is verified, and the research results provide parameter optimization for the high-speed and high-efficiency milling titanium alloy process.

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Section: Impact Force Modelling 221 Normal Contact Force Modellingmentioning

confidence: 99%

Research on Breakage Characteristics in Side Milling of Titanium Alloy with C emented Carbide End Mill

Yue

et al. 2021

Preprint

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“…22 Meanwhile, the FEA method can also be adopted to investigate the evolution of cutting stress distribution, influence of residual stress, and formation cutting chip. 23–25 Currently, several commercial FEA softwares like ADVANTEDGE, ABAQUS, MSC.MARC, ANSYS, and DEFORM can very cost-effectively investigate the cutting forces, cutting temperature, tool wears, stress distributions, and chip formations, 26,27 but most of the existing FEA simulations have largely concentrated on the simple predictions of time-independent average cutting forces (namely static cutting forces) and the 2D FEA models in which the milling tools have been simplified to the micro-turbine tool, only a few of 3D FEA milling models that have been constructed with whole-milling cutters can be found and reported at present.…”

Section: Fea-based Investigation For Milling Processesmentioning

confidence: 99%

The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050

Wei

Wang

Zhou

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The cutting forces will generally suffer massive complex factors, such as material deformation, tool eccentricity and system vibration, which will inevitably induce many great difficulties in accurately modeling the cutting force predictions that are very significant to investigate cutting processes. Therefore, the genetic algorithm optimized back-propagation and particle swarm optimization neural networks will be adopted to effectively construct cutting force prediction models. In these two back-propagation prediction models, the main milling parameters will be defined into their input vectors, and the transient milling forces along three different directions will be selected as their output vectors, then the implicit relationships between input and output vectors can be directly generated through practically training and learning these two built back-propagation models with a set of experimental milling force data. Meanwhile, the finite element analysis method will be also used to predict milling forces through programming two easy-to-operate plug-ins that can efficiently construct finite element analysis models, conveniently define processing parameters, and automatically perform mesh generation. Subsequently, the milling forces predicted by the established genetic algorithm optimized back-propagation and particle swarm optimization back-propagation models will be analytically compared with finite element analysis simulations and experiments; also the stress distribution and chip formations of finite element analysis and experiments will be comparatively investigated. Finally, the obtained results clearly indicate that these two back-propagation models built by artificial neural networks can well agree with finite element analysis simulations and experiments, but the particle swarm optimization back-propagation model is superior to the genetic algorithm optimized back-propagation model, which clearly demonstrate the particle swarm optimization back-propagation model has higher efficiencies and accuracies in predicting the average and transient cutting forces for different milling processes on aluminum alloy 7050.

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“…Nowadays, as people sustain to show concern for the environmental protection and energy consumption, the dry machining process has gradually become a new trend in manufacturing technique. 15,[17][18][19][20] For dry condition, higher requirements of wear and hightemperature resistance of cutting tools have been presented. Currently, cemented carbide tools are widely used and their coatings have been transformed from monolayer into multilayer due to extensive development in coating technology.…”

Section: Introductionmentioning

confidence: 99%

Performance of carbide tools in high-speed dry turning iron-based superalloys

Tian

Yan

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Machining quality and productivity of superalloys are limited due to their poor machinability, and fewer studies have focused on the cutting of iron-based superalloys. In this study, the cutting performance of coated and uncoated carbide tools in high-speed dry turning iron-based superalloy GH2132 was investigated by performing a series of cutting experiments. The experimental results indicated that cutting temperature and cutting forces increased, while tool life decreased with the increase in the cutting speed from 30 to 100 m/min. Under relatively low cutting speeds, flank face wear was dominated by abrasion and adhesion, while rake face wear mainly involved built-up edge (BUE), built-up layer (BUL), adhesion, and breakage near the depth of cut. Under higher cutting speed, adhesion wear was more serious on the flank face, and peeling off of the coatings and substrate occurred on the rake face. Owing to the protective effect of (Ti, Al)N + TiN coating, the coated tools exhibited better wear resistance and thus longer tool life, in particular, under higher cutting speeds. Analysis of the tool wear gap in the horizontal direction indicates that better dimensional accuracy could be obtained when coated tools are used. In dry turning of GH2132 with carbide tools, a favorable surface finish could be obtained. The surface roughness roughly showed a tendency to first decrease and then increase with the increase in average flank wear. The coated tools should be avoided to machine GH2132 at higher cutting speed due to the poor surface finish.

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Fatigue failure analysis of ceramic tools in intermittent cutting harden steel based on experiments and finite element method

Cited by 9 publications

References 32 publications

Research on Breakage Characteristics in Side Milling of Titanium Alloy with C emented Carbide End Mill

Research on Breakage Characteristics in Side Milling of Titanium Alloy with C emented Carbide End Mill

The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050

Performance of carbide tools in high-speed dry turning iron-based superalloys

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