Research Papers

A Digital Twin for Grinding Wheel: An Information Sharing Platform for Sustainable Grinding Process

[+] Author and Article Information
Kalpana Kannan

Department of Mechanical Engineering,
IIT Madras,
Chennai 600036, India
e-mail: kalpanakannan91@gmail.com

N. Arunachalam

Assistant Professor
Department of Mechanical Engineering,
IIT Madras,
Chennai 600036, India,
e-mail: chalam@iitm.ac.in

Manuscript received May 1, 2018; final manuscript received November 16, 2018; published online December 24, 2018. Assoc. Editor: William Bernstein.

J. Manuf. Sci. Eng 141(2), 021015 (Dec 24, 2018) (14 pages) Paper No: MANU-18-1292; doi: 10.1115/1.4042076 History: Received May 01, 2018; Revised November 16, 2018

Emerging re-industrialization demands the fusion of the physical and the digital world for the development of sustainable manufacturing processes. Sustainability in manufacturing aims at improving the resource productivity by identifying the environmental challenges as opportunities. In the present era of the fourth industrial revolution or digital manufacturing, manufacturers strive to gain value through every bit of data collection throughout the product lifecycle. Integration of the collected information as knowledge to improve the productivity and efficiency of the system is required to realize its benefits. In the present work, a digital twin for grinding wheel as a product integrated and web-based knowledge sharing platform is developed. It integrates the data collected in each phase of the grinding wheel from the manufacturing to the conditioning phase. The developed digital twin is implemented on the surface grinding machine. The methods for the abstraction of the production information from the manufacturer and the process information while grinding are presented. The development of a predictive model for redress life identification and computation of dressing interim period using spindle motor current data is developed and integrated. The quantifiable benefits from the digital twin for productivity and efficiency are discussed through a case study. The case study scenario evident that the implementation of the digital twin for grinding wheels increases energy and resource efficiency by 14.4%. This clearly depicts the usefulness of the digital twin for energy and resource efficiency toward the sustainable grinding process.

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Fig. 1

Overall conceptual framework—a digital twin for grinding wheel

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Fig. 2

Product preknowledge object

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Fig. 3

Operational methodology of RFID digital emulator

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Fig. 4

IoT enabled wheel end life prediction and service

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Fig. 6

Experimental setup: (a) RFID attached grinding wheel, (b) CNC chevaliar surface grinding machine, and (c) mounting of Hall effect sensor

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Fig. 7

Images of grinding wheel periphery over the grinding life period

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Fig. 8

Measured grinding sensors signal (a) spindle motor current (RMS) and (b) force signals

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Fig. 9

Data analyses and feature extraction over the grinding period for rough, medium and fine grinding

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Fig. 10

Grinding wheel end of life feature selection: (a) trendability, (b) monotonicity, and (c) prognosability

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Fig. 11

Variation of predicted spindle motor current RMS

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Fig. 12

Repeatability evaluation of grinding wheel end life

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Fig. 13

Evaluation of dressing interim period

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Fig. 14

Web page/mobile access to grinding wheel information

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Fig. 15

Grinding material removal and end life criteria: (a) grinding direction and (b) workpiece surface burn mark



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