Microstructure Effects on Cutting Forces and Flow Stress in Ultra-Precision Machining of Polycrystalline Brittle Materials

Venkatachalam, Siva; Fergani, Omar; Li, Xiaoping; Yang, Jianguo; Chiang, Kuo‐Ning; Liang, Steven Y.

doi:10.1115/1.4029648

Cited by 35 publications

(7 citation statements)

References 18 publications

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“…The cutting forces, as well as other cutting conditions, vary when the cutting tool moves along two adjacent grains that have different mechanical properties. A 'two-grain model' has been used to study the grain boundary influences on cutting forces [21] or surface generation [22]. In addition, elastic recover in micromachining is also an important factor of microstructure effects [18].…”

Section: Microstructure Effectmentioning

confidence: 99%

A Review on Nanocomposites. Part 2: Micromachining

Liu

Khaliq

Huo

et al. 2020

Journal of Manufacturing Science and Engineering

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Micromachining of nanocomposites is deemed to be a complicated process due to the anisotropic, heterogeneous structure and advanced mechanical properties of these materials associated with the size effects in micromachining. It leads to poorer machinability in terms of high cutting force, low surface quality, and high rate of tool wear. A comprehensive review on mechanical properties of nanocomposites aiming to pointout their effects on micro-machinability has been addressed in part 1. In part 2, the subsequent micro-machining processes are critically discussed based on relevant studies from both experimental and modeling approaches. The main findings and limitations of these micro-machining methods in processing nanocomposites have been highlighted together with future prospects.

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Section: Microstructure Effectmentioning

confidence: 99%

A Review on Nanocomposites. Part 2: Micromachining

Liu

Khaliq

Huo

et al. 2020

Journal of Manufacturing Science and Engineering

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“…In fact, SSGB/PSGB on a diamond-turned surface is affected by various factors, e.g. the maximum undeformed chip thickness, tool nose radius, cutting speed, grain size, etc [22]. Furthermore, due to the natural random distribution of the grain size and the crystallographic orientation, the height and shape of SSGB/PSGB vary for different material grains [23].…”

Section: Introductionmentioning

confidence: 99%

A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

He¹,

Yan²,

Wang³

et al. 2023

Int. J. Extrem. Manuf.

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Polycrystalline materials are extensively employed in industry. Its surface roughness has a great impact on the working performance. The material defect, particularly grain boundary, has great impact on the achieved surface roughness of polycrystalline materials. However, it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods. In this work, a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed. The kinematic-dynamic roughness component in relation to the tool profile duplication effect, work material plastic side flow, relative vibration between diamond tool and workpiece, etc. is theoretically calculated. The material-defect roughness component is modelled with a cascade forward neural network. In the neural network, the relative tool sharpness, work material properties (misorientation angle and grain size), and spindle rotation speed are configured as input variables. The material-defect roughness component is set as the output variable. To validate the developed model, polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools. Compared with the previously developed model, obvious improvement on the prediction accuracy is observed with this hybrid prediction model. Based on this model, the influences of different factors on the surface roughness of polycrystalline materials are discussed. The influencing mechanism of the misorientation angle and grain size is quantitatively analysed. Two fracture mode, including transcrystalline and intercrystalline fracture at different relative tool sharpness is observed. Meanwhile, optimal processing parameters are obtained with a simulated annealing algorithm. Cutting experiments are performed with the optimal parameters, and a flat surface finish with Sa 1.314 nm is finally achieved.

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“…Since the MD method is first proposed to be applied in the machining by Belak in the late 1980s [ 12 ], it has been another extensively used approach to investigate the deformation and mechanical properties during the cutting process [ 13 , 14 , 15 ]. In the MD method, the simulation model is normally established based on the interatomic and intra-atomic interactions between the atoms and bonds of the workpiece that can be described by proper potential functions at the atomic scale, which indicates the micro deformation of the model with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Assessment of Nanoscale Manufacturing Process on the Freeform Copper Surface

Liu

Zhang

et al. 2020

Materials

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The nanocutting has been paid great attention in ultra-precision machining and high sealing mechanical devices due to its nanometer level machining accuracy and surface quality. However, the conventional methods applicable to reproduce the cutting process numerically such as finite element (FE) and molecular dynamics (MD) are challenging to unveil the cutting machining mechanism of the nanocutting due to the limitation of the simulation scale and computational cost. Here a modified quasi-continuous method (QC) is employed to analyze the dynamic nanocutting behavior (below 10 nm) of the copper sample. After preliminary validation of the effectiveness via the wave propagation on the copper ribbon, we have assessed the effects of cutting tool parameters and back-engagement on the cutting force, stress distribution and surface metamorphic layer depth during the nanocutting process of the copper sample. The cutting force and depth of the surface metamorphic layer is susceptible to the back-engagement, and well tolerant to the cutting tool parameters such as the tool rank angle and tool rounded edge diameter. The results obtained by the QC method are comparable to those from the MD method, which indicate the effectiveness and applicability of the modified QC method in the nanocutting process. Overall, our work provides an applicable and efficient strategy to investigate the nanocutting machining mechanism of the large-scale workpiece and shed light on its applications in the super-precision and high surface quality devices.

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Microstructure Effects on Cutting Forces and Flow Stress in Ultra-Precision Machining of Polycrystalline Brittle Materials

Cited by 35 publications

References 18 publications

A Review on Nanocomposites. Part 2: Micromachining

A Review on Nanocomposites. Part 2: Micromachining

A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

Multiscale Assessment of Nanoscale Manufacturing Process on the Freeform Copper Surface

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