Abstract
Robotic grinding is considered as an alternative machining towards an efficient and intelligent machining of components due to its flexibility, intelligence and cost efficiency, particularly in comparison with the current mainstream manufacturing modes such as CNC machines. The advances in robotic grinding during the past years aims to solve problems of precision machining in small scale surfaces and other emphasizes on the efficient machining of large-scale surfaces. In this work, a method was investigated to improve surface repair accuracy by eliminating the workpiece datum error by directly engaging the grinding wheel. In fact, the proposed method uses acoustic emission sensing technique to detect grinding contact so as to estimate correct reference datum. Process variables based on machining parameters such as depth of cut, wheel speed, feed speed, dressing condition and system time constant is used to as a key parameter for controlling the robot to conduct the grinding process. The geometrical relationship and machining precision level developed has reached an accuracy level of 30 µm and error is been controlled by considering the process variables such as depth of cut, wheel speed, feed speed, dressing condition and system time constant which is the key for controlling the robot to conduct grinding process. The recorded data provide a significant evidence to support the viability of implementing a 6-axis robotic system for various grinding applications, combining more quality and critical surface finishing practices, and an increased focus on the size and form of generated components.
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Sufian, M., Chen, X. (2022). Robotic Grinding for Surface Repair. In: Batako, A., Burduk, A., Karyono, K., Chen, X., Wyczółkowski, R. (eds) Advances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering. GCMM 2021. Lecture Notes in Networks and Systems, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-90532-3_20
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DOI: https://doi.org/10.1007/978-3-030-90532-3_20
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