Wear and breakage of coated carbide tool in milling of H13 steel and SKD11 hardened steel

Gong, Feng; Zhao, Jun; Ni, Xiaowu; Liu, Changxia; Sun, Jianlin; Zhang, Quanzhong

doi:10.1007/s42452-019-1152-6

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Yan L [21] proved that the tool breakage failure also accounted for a large proportion in the actual milling process, and the tensile stress exceeding the tool tensile strength was the fundamental factor that leads to the tool damage. Gong F [22] studied the fracture mechanism of cemented carbide tool by milling hardened steel, which further proved that the hardness of the workpiece had an effect on the tool failure, and the geometric model of tool wear and the fracture was established. Yuan Q [23] studied that when cemented carbide tools milling workpiece using large cutting parameters, the main form of tool failure was impact damage, and tool wear also lead to tool damage after a period of stable cutting.…”

Section: Introductionmentioning

confidence: 97%

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: Introductionmentioning

confidence: 97%

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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“…Furthermore, experiments revealed that using cutting tools with a smaller rake angle, a smaller right clearance angle, and a large helix angle effectively reduced cutting forces, extended tool life, and facilitated smoother cutting processes. Gong et al [13] conducted milling experiments on H13 steel and SKD11 hardened steel by using coated carbide tools to investigate the tool wear and breakage mechanisms. The results indicated that the hardness of the workpiece had a dominant effect on tool failure patterns.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

Lin,

Hsieh

2024

Applied Sciences

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This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

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“…For instance, Inkhamnoi and Jirapattarasilp 26 examined the effects of milling parameters and cooling conditions on the surface hardness of SKD11. Gong et al 27 focused on the breakage and wear mechanisms of coated-carbide tools in the milling process of SKD11. Recently, Dong et al 28 and Duc et al 29 have explored the performance of MQL with MoS 2 /alumina nanoparticles in the milling of SKD11.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sustainable milling of SKD11 steel under minimum quantity lubrication

Nguyen

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this study, the sustainably face milling of SKD11 steel under a minimum quantity lubrication condition was investigated. The goal of this work is twofold: (i) observing the impact of main milling parameters, including cutting speed ( Vc), depth of cut ( ap), and feed per tooth ( fz) on surface roughness ( Ra) and specific cutting energy (SCE), and (ii) finding out an optimal combination of cutting parameters for ameliorating the surface quality, productivity, and diminishing energy consumption. For this purpose, the experiment was designed by using the Box–Behnken matrix. A series of 15 experimental runs have been conducted to acquire experimental data. The regression models of Ra and SCE were subsequently developed and evaluated by using the analysis of variance. Finally, the multi-objective optimization issue was resolved by using the technique for order of preference by similarity to ideal solution (TOPSIS) algorithm and the desirability approach (DA). The results show that the parameter fz reveals the highest impact on surface roughness and SCE, followed by ap and Vc. The TOPSIS method allows finding the optimal solution with a reduction of 46% in terms of SCE when compared with that achieved by DA. This is significant in the context of sustainable manufacturing. This research is expected to enrich the knowledge of milling SKD11, and the achieved results can be used for research community and manufacturing industries.

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Wear and breakage of coated carbide tool in milling of H13 steel and SKD11 hardened steel

Cited by 10 publications

References 24 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

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

Optimization of sustainable milling of SKD11 steel under minimum quantity lubrication

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