A recent model-based approach for predicting the compensation required on the next part to be turned on a CNC machine solely on the basis of three independent measurements conducted at selected locations on a limited set of previously machined parts under a similar cutting set-up is reviewed. A new method of achieving the same objective through the use of the learning capability of an adaptive neuro-fuzzy network is developed and tested against experimental data for cylindrical turning. This method requires only one on-machine measurement per sample. It is conducted by a novel contact sensor that probes with the tool and facilitates automation by providing proximity information as the tool approaches the workpiece.
Article PDF
Similar content being viewed by others
Use our pre-submission checklist
Avoid common mistakes on your manuscript.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Li, X., Venuvinod, P., Djorjevich, A. et al. Predicting Machining Errors in Turning Using Hybrid Learning. Int J Adv Manuf Technol 18, 863–872 (2001). https://doi.org/10.1007/PL00003954
Issue Date:
DOI: https://doi.org/10.1007/PL00003954