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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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The surface quality of the components produced in a grinding process mainly depends on the grinding wheel topography. To maintain the quality of the component, the condition of the grinding wheel should be monitored and the periodical dressing has to be done to retain the shape and the sharpness of the cutting edges. In the present paper, the machine-vision-based texture analysis methods are introduced to discriminate the surface condition of the grinding wheel. The grinding wheel images are acquired using a charge coupled device (CCD) camera at different intervals of time during the process and the texture of the grinding wheel image is analysed using appropriate statistical and fractal methods. The variation of the parameters with the condition of the grinding wheel surface is analysed and presented. The evaluated parameters are clearly discriminating the texture of the grinding wheel at every stage. The methods presented in this paper can be used to assess the grinding wheel condition effectively.

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