Influence of cutting tool geometrical parameters on tool wear in high-speed milling of Inconel 718 curved surface

Ma, Jian-wei; Zhang, Jia; He, Guangzhi; Liu, Zhen; Zhao, Xiaoxuan; Qin, Fuwen

doi:10.1177/0954405417716495

Cited by 22 publications

(11 citation statements)

References 25 publications

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“…18 On the other hand, tool wear increases the contact length between the tool and the workpiece. 19,20 Therefore, it can be seen from Figure 2(a) that the cutting force (especially the radial thrust force) gradually becomes larger. It can also be seen from Figure 2(b) that as the tool wear becomes more and more severe, the vibration gradually becomes larger.…”

Section: Methodsmentioning

confidence: 99%

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

Cheng

Shi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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In the process of high strength steel turning, tool wear will reduce the surface quality of the workpiece and increase cutting force and cutting temperature. To obtain the fine surface quality and avoid unnecessary loss, it is necessary to monitor the state of tool wear in the dry turning. In this article, the cutting force, vibration signal and surface texture of the machined surface were collected by tool condition monitoring system and signal processing techniques are being used for extracting the time-domain, frequency-domain and time-frequency features of cutting force and vibration. The gray level processing technique is used to extract the features of the gray co-occurrence matrix of the surface texture and found that these features changed simultaneously when the cutting tool broke. After this, an intelligent prediction model of tool wear was built using the support vector regression (SVR) whose kernel function parameters were optimized by the grid search algorithm (GS), the genetic algorithm (GA) and the particle swarm optimization algorithm respectively. The features extracted from the signals and surface texture are used to train the prediction model in MATLAB. It was found that after the surface texture features were fused using the intelligent prediction model on the basis of the features of cutting force and vibration, prediction accuracy of the proposed method is found as 97.32% and 96.72% respectively under the two prediction models of GA-SVR and GS-SVR. Moreover, the intelligent prediction model can not only predict the tool wear under different cutting conditions, but also the different wear stages in a single wear cycle and the absolute error between the predicted value and the actual value is less than 10 μm, the confidence coefficient of prediction curve is around 0.99.

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Section: Methodsmentioning

confidence: 99%

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

Cheng

Shi

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…However, it suffers from poor micro-machinability because of having low thermal conductivity and high chemical affinity for many cutting tool materials. [1][2][3] Lithographic methods such as photolithography, focus ion beam, and X-ray lithography are the most common fabrication technologies applied at the micro-and nano-scales. 4 While, these methods have extensive applications in the industry and are applicable only to a few materials such as silicon or glass.…”

Section: Introductionmentioning

confidence: 99%

Investigation on laser-induced oxidation assisted micro-milling of Inconel 718

Xia

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Poor surface quality and rapid tool wear are the main problems in micro-cutting of Inconel 718. In this study, a novel hybrid machining method named laser-induced oxidation assisted micro-milling is proposed to solve the aforementioned problems. A loose oxide layer and a relatively flat sublayer are formed on the material after laser irradiation. Under optimized laser parameters with a scanning speed of 1 mm/s and an average laser power of 4.5 W, the thicknesses of the oxide layer and the sublayer are 24 and 18 μm, respectively. The influence of cutting parameters on milling force, surface roughness, surface quality, and top burr size is studied in detail. Cutting force and thrust force in the proposed hybrid machining process are lower than those in the conventional micro-milling. Results show that for the investigated range of parameters, the optimal feed per tooth and depth of cut in the hybrid process are 3 μm/z and 3 μm, respectively. When using the optimal parameters, the surface roughness of the machined slot bottom is 108.5 nm. The top burr size on the up-milling side and the down-milling side is 26.8 and 36.2 μm, respectively. In addition, the tool wear mechanism is coating delamination in hybrid process, whereas chipping, coating delamination, tool nose breakage, and adhesion are the main tool wear mechanism in the conventional micro-milling. For the same amount of material removal, the proposed hybrid process can decrease the tool wear and enhance the service life of the micro-end mill as compared to conventional micro-milling.

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“…[23][24][25][26] Due to these properties, it is widely used in chemical, nuclear, aerospace, and power industries exhibiting non-uniform strains. 20,23,24,[26][27][28] For such operating conditions, fatigue strength and creep strength become more important, which predominantly depends on the surface quality of the machined components. Out of the conventional machining processes, grinding is the process, which is used widely in making high surface quality components.…”

Section: Introductionmentioning

confidence: 99%

Some investigations in grindability improvement of Inconel 718 under ecological grinding

Sinha

Madarkar

Ghosh

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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This work explores the improvement in grinding characteristics of Inconel 718 (IN718) using soluble oil under minimum quantity lubrication and liquid nitrogen (LN2) environments. The coolant flow rate in minimum quantity lubrication and LN2 grinding has been regulated through indigenously developed setups. Grinding performance has been studied in terms of on-machine measured grinding forces and centre line average surface roughness (Ra). The obtained grinding characteristics have been compared with the outcomes under dry and wet grinding. Surface integrity of ground surface, wheel morphology, and chip formation characteristics has been studied using scanning electron microscope, energy dispersive X-ray spectroscopy, and atomic force microscopy. Analysis of variance has been carried out to capture the variability in the experimental data for tangential forces and Ra. The main effect of the factors and their first-order interactions have been considered, and second-order regression equations have been developed using response surface methodology. LN2 grinding has been proved to be more efficient as it yielded lowest grinding forces, least oxidation, minimal ground surface damage and better surface integrity. The occurrence of almost circular chips in dry grinding indicates severe oxidation, whereas small C-type chips formed under minimum quantity lubrication and LN2 conditions indicate effective cooling under these environments. The energy dispersive X-ray spectroscopy analysis of the ground surfaces also supports these results through the occurrence of the highest oxidation in dry grinding. From this work, it has been concluded that LN2 and minimum quantity lubrication grinding offer a clean and effective means to improve grinding performance of IN718 compared to dry and wet grinding.

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Influence of cutting tool geometrical parameters on tool wear in high-speed milling of Inconel 718 curved surface

Cited by 22 publications

References 25 publications

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

Investigation on laser-induced oxidation assisted micro-milling of Inconel 718

Some investigations in grindability improvement of Inconel 718 under ecological grinding

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