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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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References

Malkin, S. , 1989, Grinding Technology: Theory and Application of Machining With Abrasives, Wiley, New York.
Malkin, S. , and Cook, N. H. , 1971, “ The Wear of Grinding Wheels—Part 1: Attritious Wear,” ASME J. Eng. Ind., pp. 1121–1128.
Linke, B. S. , 2014, “ Sustainability Concerns in the Life Cycle of Bonded Grinding Tools,” CIRP J. Manuf. Sci. Technol., 7(3), pp. 258–263. [CrossRef]
Field, M. , Kegg, R. , and Bueschar, S. , 1980, “ Computerized Cost Analysis of Grinding Operations,” Ann. CIRP, 29(1), pp. 233–237. [CrossRef]
Pavic, A. , Josipovic, B. , and Brozovic, M. , 2007, “ Machining Costs in Grinding,” 11th International Scientific Conference on Production Engineering, Biograd, Croatia, June 13–17, pp. 13–17.
QYResearch, 2018, “ Global Grinding Wheels Sale Market Report,” Global Information, Kawasaki, Japan, accessed Mar. 13, 2018, https://www.giiresearch.com/report/qyr458117-global-grinding-wheels-sales-market-report.html
Lachance, S. , Bauer, R. , and Warkentin, A. , 2004, “ Application of Region Growing Method to Evaluate the Surface Condition of Grinding Wheels,” Int. J. Mach. Tool Manuf., 44(7–8), pp. 823–829. [CrossRef]
Arunachalam, N. , and Ramamoorthy, B. , 2007, “ Texture Analysis for Grinding Wheel Wear Assessment Using Machine Vision|,” Proc. Inst. Mech. Eng., Part B, 221(3), pp. 419–430. [CrossRef]
Arunachalam, N. , and Vijayaraghavan, L. , 2014, “ Assessment of Grinding Wheel Conditioning Process Using Machine Vision,” IEEE Conference on Prognostics and Health Management (PHM), Cheney, WA, June 22–25, pp. 1–5.
Adibi, H. , Rezael, S. M. , and Sarha, A. D. , 2014, “ Grinding Wheel Loading Evaluation Using Digital Image Processing,” ASME J. Manuf. Sci. Eng., 136(1), p. 011012.
Arunachalam, N. , and Vijayaraghavan, L. , 2015, “ Evaluation of the Working Surface of the Grinding Wheel Using Speckle Image Analysis,” ASME Paper No. MSEC2015-9416.
Lezanski, P. , 2001, “ Intelligent System for Grinding Wheel Condition Monitoring,” J. Mater. Process. Technol., 109(3), pp. 258–263. [CrossRef]
Karpuschewski, B. , Wehmeier, M. , and Inasaki, I. , 2000, “ Grinding Monitoring System Based on Power and Acoustic Emission Sensors,” Ann. CIRP, 49(1), pp. 235–240. [CrossRef]
Kwak, J. S. , and Ha, M. K. , 2004, “ Detection of Dressing Time Using the Grinding Force Signal Based on the Discrete Wavelet Decomposition,” Int. J. Adv. Manuf. Technol., 23(1–2), pp. 87–92. [CrossRef]
Chen, X. , and Limchimchol, T. , 2006, “ Monitoring Grinding Wheel Redress Life Using Support Vector Machines,” Int. J. Autom. Comput., 1, pp. 56–62. [CrossRef]
Alexandre, F. P. , Lopes, W. N. , Dotto, L. , Ferreira, F. I. , Agular, P. R. , Binachi, E. C. , and Lopes, J. C. , 2018, “ Tool Condition Monitoring of Aluminium Oxide Grinding Wheel Using AE and Fuzzy Model,” Int. J. Adv. Manuf. Technol., 96(1–4), pp. 67–79.
Cai, R. , Rowe, W. B. , Mourzzi, J. L. , and Morgan, M. N. , 2007, “ Intelligent Grinding Assistant (IGA(©))—System Development—Part I: Intelligent Grinding Database,” Int. J. Adv. Manuf. Technol., 35(1–2), pp. 75–85. [CrossRef]
Ding, H. , and Cheng, K. , 2014, “ Development of an Innovative ERWC Approach to Sustainable Manufacturing With Application to Design of an Energy-Resource Efficient CNC Centerless Grinding,” KEC Transaction on Sustainable Design and Manufacturing I, pp. 653–667 http://nimbusvault.net/publications/koala/inimpact/papers/sdm14-076.pdf.
Linke, B. , and Overcash, M. , 2012, “ Life Cycle Analysis of Grinding,” 19th CIRP Conference on Life Cycle Engineering, Berkeley, CA, May 23–25, pp. 293–298.
Hong, H. , Yin, Y. , and Chen, X. , 2016, “ Ontological Modeling of Knowledge Management for the Human-Machine Integrated Design of Ultra-Precision Grinding Machine,” Enterprise Inf. Syst., 10(9), pp. 970–981. [CrossRef]
Jiang, P. , Li, G. , Liu, P. , Jiang, L. , and Li, X. , 2017, “ Energy Consumption Model and Energy Efficiency Evaluation for CNC Continuous Generating Grinding Machine Tools,” Int. J. Sustainable Eng., 10(4–5), pp. 226–232. [CrossRef]
Negri, E. , Fumagalli, L. , and Macchi, M. , 2017, “ A Review of the Roles of Digital Twin in CPS-Based Production Systems,” Procedia Manuf., 11, pp. 939–948. [CrossRef]
Cai, Y. , Starly, B. , Cohen, P. , and Lee, Y. S. , 2017, “ Sensor Data and Information Fusion to Construct Digital-Twin Virtual Machine Tools for Cyber-Physical Manufacturing,” Procedia Manuf., 10, pp. 1031–1042. [CrossRef]
Botkina, D. , Hedlind, M. , Olsson, B. , Henser, J. , and Lundholm, T. , 2018, “ Digital Twin of a Cutting Tool,” Procedia CIRP, 72, pp. 215–218. [CrossRef]
Qi, Q. , Tao, F. , Zuo, Y. , and Zhao, D. , 2018, “ Digital Twin Service Towards Smart Manufacturing,” Procedia CIRP, 72, pp. 237–242. [CrossRef]
Zhang, W. , Huang, X. , Chen, N. , Wang, W. , and Zhong, H. , 2012, “ PaaS-Oriented Performance Modeling for Cloud Computing,” IEEE 36th International Conference on Computer Software and Applications, Izmir, Turkey, July 16–20, pp. 395–404.
Clobe, J. , and Hines, J. W. , 2009, “ Identifying Optimal Prognostic Parameters From Data: A Genetic Algorithms Approach,” Annual Conference of the Prognostics and Health Management Society, San Diego, CA, Sept. 27–30, pp. 1–11 http://ftp.phmsociety.org/sites/phmsociety.org/files/phm_submission/2009/phmc_09_69.pdf.
Williams, R. E. , and Rajurkar, K. P. , 1992, “ Stochastic Modeling and Analysis of Abrasive Flow Machining,” J. Eng. Ind., 114, pp. 74–81.
Kalpana, K. , Arunachalam, N. , Chawala, A. , and Natarajan, S. , 2018, “ Multi-Sensor Data Analytics for Grinding Wheel Redress Life Estimation—An Approach Towards Industry 4.0,” 46th Procedia Manufacturing, TX, June 18–22 , pp. 1230–1241.
Pi, V. N. , Lu, A. T. , Hung, L. X. , and Long, B. T. , 2016, “ Cost Optimization of Surface Grinding Process,” J. Environ. Sci. Eng., 5, pp. 606–611.

Figures

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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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