Research Papers

Grinding Wheel Loading Evaluation Using Digital Image Processing

[+] Author and Article Information
Hamed Adibi

Department of Mechanical Engineering,
Amirkabir University of Technology,
Tehran, Iran 159164311
e-mail: h_adibi@aut.ac.ir

S. M. Rezaei

Department of Mechanical Engineering,
Amirkabir University of Technology, Iran
New Technologies Research Center,
Amirkabir University of Technology, Iran,
Tehran, Iran 159164311
e-mail: smrezaei@aut.ac.ir

Ahmed A. D. Sarhan

Center of Advanced Manufacturing
and Material Processing,
Department of Engineering Design
and Manufacture,
University of Malaya,
Kuala Lumpur 50603, Malaysia
e-mail: ah_sarhan@um.edu.my

1Corresponding author.

Manuscript received June 15, 2012; final manuscript received October 2, 2013; published online December 2, 2013. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 136(1), 011012 (Dec 02, 2013) (10 pages) Paper No: MANU-12-1175; doi: 10.1115/1.4025782 History: Received June 15, 2012; Revised October 02, 2013

Wheel loading entails chip accumulation in porosities between grains or welding to the top of cutting grains. It is considered one of the most prevalent problems in grinding Nickel-based super alloys. Identification of wheel loading is an important issue for optimizing the dressing intervals, but it can be a time consuming and an expensive process. A novel technique based on digital image processing to determine the loading areas over the surface of CBN vitrified grinding wheels using the toolbox of MATLAB is presented in this paper. The optical characteristics of the metal chips, the abrasive grains and wheel bond are considered. Experiments were performed to examine the repeatability of the proposed technique. The results were verified by the use of a scanning electron microscope. Based on the proposed technique, the effects of cutting parameters on the loaded area to wheel surface ratio in relation to grinding performance were studied empirically.

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Fig. 1

Light striking between interface of different substance

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Fig. 2

CBN vitrified wheel surface image captured with digital microscope

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Fig. 3

Cartesian grid—eight neighbors for any point

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Fig. 4

Unit vector specifying the derivative's direction

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Fig. 5

Pixel values of image

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Fig. 15

Grinding force against depth of cut, cutting speed = 30 m/s, table speed = 100 mm/s, wheel type:B126, MRV = 450 mm3

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Fig. 14

Wheel loading against depth of cut, cutting speed = 30 m/s, table speed = 100 mm/s, wheel type:B126, MRV = 450 mm3

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Fig. 13

Ground surface of workpiece, depth of cut = 20 μm, cutting speed = 30 m/s, table speed = 100 mm/s, wheel type:B126

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Fig. 10

Standard deviation of loading percentage against number of pictures in circumference of wheel

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Fig. 9

Processing of newly dressed wheel surface image

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Fig. 8

Loaded areas compare with wear flat areas

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Fig. 7

Steps of image processing of grinding wheel surface

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Fig. 6

The wheel loading measurement device

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Fig. 16

Wheel loading against table speed, depth of cut = 20 μm, cutting speed = 30 m/s, wheel type:B126, MRV = 450 mm3

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Fig. 17

Wheel loading against cutting speed, depth of cut = 20 μm, table speed = 100 m/s, wheel type:B126, MRV = 450 mm3

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Fig. 12

Grinding force against grinding passes from results of Tso [30]

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Fig. 11

Loading against grinding passes, depth of cut = 20 μm, cutting speed = 30 m/s, table speed = 100 mm/s, wheel type:B126



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