In-machine data acquisition for evaluating the conditioning efficiency of resin-bonded super-abrasive grinding wheels

ABSTRACT

Smart manufacturing factories rely on the Internet of Things to drive changes to the machine-tool sector. Grinding processes play a leading role in the manufacturing of high added-value components for high-tech sectors. CBN and diamond grinding wheels are leading the next generation of advanced grinding. One of the critical points is the control of mechanical dressing and truing techniques. In this context, rotating SiC tools and Ta metallic sticks have been successfully applied. However, there is a lack of industrial and easy-to-use wheel topography evaluation techniques. Indirect techniques and complex topography evaluation devices had proposed. However, it is of maximum interest to obtain direct information about grit protrusion and distribution that, could be shared with digitalization platforms. In this paper, an industrial, feasible and easy-to-use optical tool is proposed to gain direct information about the surface topography left by the truing super-abrasive grinding wheels. This information is shared with digitalization platforms. The results confirm the influence of speed ratio, with positive values being related to higher grain pull-out and protrusion of new grains. SiC rotary tool shows better performance when compared to Tantalum sticks. Future work will focus on protocols to share the generated data with existing digitalization platforms.

KEYWORDS:

• Digitalization
• smart manufacturing
• grinding
• superabrasive
• truing
• resin-bonded

Abbreviation and nomenclature

Table

Acknowledgments

The authors gratefully acknowledge the funding support they received from the Spanish Ministry of Economy and Competitiveness for their support for the Research Project: Digital Solutions for Advanced Grinding Processes-GrinDTWin (PID2020-114686RB-I00). This work was also partially funded by the Basque Government through the EKOHEGAZ project of the Elkartek program (KK-2021/00092). This work was also partly carried out within the framework of the joint cross-border laboratory LTC AENIGME.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the EKOHEGAZ project of the Elkartek program (KK-2021/00092) [Basque Government through the EKOHEGAZ project of]; Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low-Pressure Turbines [DPI2017-82239-P];laboratory LTC AENIGME.