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CAD/CAM-based force controller using a neural network-based effective stiffness estimator

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Abstract

In industries manufacturing metallic molds, various NC machine tools are used. We have already proposed a desktop NC machine tool with compliance control capability to automatically cope with the finishing process of LED lens molds. The NC machine tool has the ability to control the polishing force acting between an abrasive tool and a work piece. The force control method is called impedance model force control. The most effective gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition after considering the effective stiffness in the force control system. However, there is a problem in that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity has a bad influence on the force control stability. In this article, a fine tuning method of the desired damping is considered using neural networks. The neural networks acquire the nonlinearity of effective stiffness. The promise is evaluated through an experiment.

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References

  1. Nagata F, Hase T, Haga Z, et al (2007) CAD/CAM-based position/force controller for a mold polishing robot. Mechatronics 17(4–5):207–216

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  2. Nagata F, Hase T, Haga Z, et al (2009) A desktop NC machine tool with a position/force controller using a fine-velocity pulse converter. Mechatronics 19(5):671–679

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Correspondence to Fusaomi Nagata.

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This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010

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Nagata, F., Mizobuchi, T., Hase, T. et al. CAD/CAM-based force controller using a neural network-based effective stiffness estimator. Artif Life Robotics 15, 101–105 (2010). https://doi.org/10.1007/s10015-010-0776-9

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  • DOI: https://doi.org/10.1007/s10015-010-0776-9

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