Cutting edge microgeometries in metal cutting: a review

Zhuang, Kejia; Fu, Changni; Weng, Jian; Hu, Cheng

doi:10.1007/s00170-021-07558-6

Cited by 54 publications

(26 citation statements)

References 162 publications

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“…On the other hand, tool micro geometry focuses on the cutting edge. Recent studies show that tool micro geometry has a significant influence on the cutting process [ 2 , 3 ]. In addition, extending the life of the cutting edge is possible if it has a specific shape and quality.…”

Section: Introductionmentioning

confidence: 99%

“…There are traditional cutting edge preparation processes that are used by cutting tool manufacturers and other new technologies that are in the development or implementation process [ 3 , 7 , 8 , 9 ]. Examples of a traditional processes are brushing and brushing–polishing , which are widely used by cutting tool manufacturers [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool

2022

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In recent years, cutting edge preparation became a topic of high interest in the manufacturing industry because of the important role it plays in the performance of the cutting tool. This paper describes the use of the drag finishing DF cutting edge preparation process on the cutting tool for the broaching process. The main process parameters were manipulated and analyzed, as well as their influence on the cutting edge rounding, material remove rate MRR, and surface quality/roughness (Ra, Rz). In parallel, a repeatability and reproducibility R&R analysis and cutting edge radius re prediction were performed using machine learning by an artificial neural network ANN. The results achieved indicate that the influencing factors on re, MRR, and roughness, in order of importance, are drag depth, drag time, mixing percentage, and grain size, respectively. The reproducibility accuracy of re is reliable compared to traditional processes, such as brushing and blasting. The prediction accuracy of the re of preparation with ANN is observed in the low training and prediction errors 1.22% and 0.77%, respectively, evidencing the effectiveness of the algorithm. Finally, it is demonstrated that the DF has reliable feasibility in the application of edge preparation on broaching tools under controlled conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool

2022

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“…Because of a closed‐form solution and computationally efficient procedures, analytical models were more common in the early years 1,2,5,6 . Different analytical models are still being developed and used for different cutting conditions, including tools with different designs (grooved, 7,8 rounded edge, 9–11 and chamfered 12–15 ), different microcutting processes, 16 and for simulating serrated chip formation 17 . Nevertheless, these models suffer from oversimplifications, and are not suitable for accurately capturing the complexity of the cutting process.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of chip formation during the cutting process using imaging techniques: A review

Nie

Yang

Zhang

et al. 2022

Int Journal of Mech Sys Dyn

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Imaging techniques have been widely implemented to study the dynamics of chip formation. They can offer a direct method and a full field measurement of the cutting process, providing kinematic information of the chip formation process. In this article, the state of the art of the imaging techniques reported in the literature has been summarized and analyzed. The imaging techniques have been applied to study the chip formation mechanism, friction behavior, strain/strain rate, and stress fields. Furthermore, the study of surface integrity has been advanced by deriving the thermo‐mechanical loading, subsurface deformation, and material constitutive model from the imaging technique. Finally, achievements in the area of imaging techniques have been summarized, followed by future directions for their application in the study of surface integrity.

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“…The oldest process to shape components is metal cutting or machining in the manufacturing industry [1]. It is evaluated that 15% of the all-mechanical part produced worldwide is derived from machining operation.…”

Section: Introductionmentioning

confidence: 99%

Optimal Selection of Cutting Parameters for Surface Roughness in Milling Machining of AA6061-T6

2022

IJE

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Due to its ability to remove material quickly while maintaining optimum surface quality, end milling is considered one of the most frequent metal cutting procedures in industry. The present study aimed to investigate the impacts of cutting parameters and tool geometry on milling of Aluminum Alloy 6061-T6 to examine the impact surface roughness by utilizing response surface methodology (RSM). RSM was used to create a second-order mathematical model of surface roughness for this purpose. A multiple regression analysis used the analysis of variance to demonstrate the effect of machining settings on surface roughness and determine experiment performance. The trials for optimizing surface roughness were set up utilizing the central composite design (CCD) method and various cutting parameters such as spindle speed, feed rate and depth of cut. Also the parameters used in tool geometry are the radial rake angle (10, 13, 16, 19 and 22 degrees), and nose radius (0, 0.2, 0.4, 0.6 and 0.8 mm). The result shows that the nose radius has more significant effect on the surface roughness followed by the radial rake angle. Moreover, the effect of the depth of cut on surface roughness is more dominant than cutting speed. The optimum combinations of cutting and tool geometry parameters were cutting speed (60.53 m/min), feed rate (0.025 mm/tooth), depth of cut (0.84 mm), radial rake angle (12.72 degree) and nose radius (0.34 mm).

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Cutting edge microgeometries in metal cutting: a review

Cited by 54 publications

References 162 publications

Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool

Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool

Dynamics of chip formation during the cutting process using imaging techniques: A review

Optimal Selection of Cutting Parameters for Surface Roughness in Milling Machining of AA6061-T6

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