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Research Papers

Stochastic Analysis of Microgrinding Tool Topography and Its Role in Surface Generation

[+] Author and Article Information
S. Anandita

Department of Mechanical Engineering,
Indian Institute of Technology Bombay,
Mumbai 400076, India
e-mail: s_anandita@iitb.ac.in

Rakesh G. Mote

Department of Mechanical Engineering,
Indian Institute of Technology Bombay,
Mumbai 400076, India
e-mail: rakesh.mote@iitb.ac.in

Ramesh Singh

Mem. ASME
Department of Mechanical Engineering,
Indian Institute of Technology Bombay,
Mumbai 400076, India
e-mail: rameshksingh@gmail.com

1Corresponding author.

Manuscript received March 31, 2017; final manuscript received September 26, 2017; published online November 2, 2017. Assoc. Editor: Kai Cheng.

J. Manuf. Sci. Eng 139(12), 121013 (Nov 02, 2017) (14 pages) Paper No: MANU-17-1205; doi: 10.1115/1.4038056 History: Received March 31, 2017; Revised September 26, 2017

With the rising trend of miniaturization in modern industries, micro manufacturing processes have made a significant position in the manufacturing domain. Demands of high precision along with super finish of the final machined product have started rising. Grinding, being largely considered as a finishing operation, has large potential to cater to such requirements of micro manufacturing. However, stochastic nature of the grinding wheel topography results in a high degree of variation in the output responses especially in the case of microgrinding. With an aim to obtain a good and predictable surface finish in brittle materials, the current study aims at developing a surface generation model for wall grinding of hard and brittle materials using a microgrinding tool. Tool topographical features such as grit protrusion height, intergrit spacing, and grit distribution on the tool tip of a microgrinding pin have been calculated from the known mesh size of the grits used during tool manufacturing. Kinematic analysis of surface grinding has been extended to the case of wall grinding and each grit trajectory has been predicted. The kinematic analysis has been done by taking into consideration the effect of tool topographical features and the process parameters on the ground surface topography. Detailed analysis of the interaction of the grit trajectories is done to predict the final surface profile. The predicted surface roughness has been validated with the experimental results to provide an insight to the surface quality that can be produced for a given tool topography.

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Figures

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Fig. 1

(a) Optical and (b) scanning electron microscope (SEM) image of a microgrinding tool (900 μm diameter) and (c) grit size histogram along with plots showing normal and (Right and Left) skewed normal probability distributions

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Fig. 2

(a) SEM image of a microgrinding tool showing a random distribution of abrasives on its surface, (b) axial and angular sample space of the grits, and (c) radial sample space for a grit in a single-layered electroplated microgrinding tool

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Fig. 3

(a) Optical image of a microgrinding tool surface (b) depiction of “active” and “static” grits (c) depiction of grit protrusion height for different grit sizes, and (d) random distribution of grits on microgrinding tool tip showing inter-grit spacing

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Fig. 4

SEM image of a microgrinding tool showing clustering of grits

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Fig. 5

Different conditions of grit interaction depending on their location

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Fig. 6

Flowchart showing steps for grit location allocation

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Fig. 7

Grit workpiece interaction during wall grinding

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Fig. 8

(a) Progressive development of surface profile by a single grit during multiple tool rotations and (b) tool motion during microgrinding

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Fig. 9

Flowchart for surface generation during wall grinding

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Fig. 10

(a) Simulated topography of the microgrinding tool tip and (b) SEM image of the microgrinding tool tip showing random grit arrangement

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Fig. 11

(a) Experimentally measured protrusion height, (b) predicted values of protrusion height for normal distribution of grit sizes, and (c) predicted values of grit protrusion height for the right skewed normal distribution of grit sizes

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Fig. 12

(a) Experimentally measured inter-grit spacing, (b) predicted values of inter-grit spacing for normal distribution of grit sizes, and (c) predicted values of intergrit spacing for the right skewed normal distribution of grit sizes

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Fig. 13

(a) Schematic of micro machining center, (b) image of micro machining center, (c) front view of the workpiece tool assembly, and (d) schematic of workpiece prepared for micro surface grinding

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Fig. 14

(a) Simulated surface profile and (b) optical image of surface profile generated at N = 24000 rpm, Vw=100 μm/s, depth of cut = 20 μm, and (c) comparison of surface roughness for different nature of grit size distribution

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Fig. 15

(a) Simulated surface profile and (b) optical image of surface profile generated at N = 24000 rpm, Vw=100 μm/s, depth of cut = 20 μm, and (c) comparison of surface roughness for different nature of grit size distribution

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Fig. 16

(a) Simulated surface profile and (b) optical image of surface profile generated at N = 12000 rpm, Vw = 100 μm/s, depth of cut = 5μm, and (c) comparison of surface roughness for different nature of grit size distribution

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