The Experts below are selected from a list of 6264 Experts worldwide ranked by ideXlab platform
Paolo Bosetti - One of the best experts on this subject based on the ideXlab platform.
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a model based adaptive controller for chatter mitigation and productivity enhancement in cnc milling machines
Robotics and Computer-integrated Manufacturing, 2016Co-Authors: Carlos Maximiliano Giorgio Bort, Marco Leonesio, Paolo BosettiAbstract:Seeking a higher level of automation, according to Intelligent Manufacturing paradigm, an optimal process control for milling process has been developed, aiming at optimizing a multi-objective target function defined in order to mitigate vibration level and surface quality, while preserving production times and decreasing Tool wear rate. The control architecture relies on a real-time process model able to capture the most significant phenomena ongoing during the machining, such as cutting forces and Tool vibration (both forced and self-excited). For a given Tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior. For the first time in the literature, following a Model-Predictive-Control (MPC) approach, the controller is able to adapt its actions taking into account process and axes dynamics on the basis of Optimal Control theory. The developed controller has been implemented in a commercial CNC of a 3-axes milling machine manufactured by Alesamonti; the effectiveness of the approach is demonstrated on a real industrial application and the performance enhancement is evaluated and discussed. HighlightsModel-based controller for milling process based on Optimal Control Theory.Multi-objective optimization: vibrations, Tool Deflection, Tool wear and productivity.Real-time model of cutting process based on computation of Tool-workpiece engagement.Implementation and testing on a commercial milling machine by Alesamonti.
Carlos Maximiliano Giorgio Bort - One of the best experts on this subject based on the ideXlab platform.
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a model based adaptive controller for chatter mitigation and productivity enhancement in cnc milling machines
Robotics and Computer-integrated Manufacturing, 2016Co-Authors: Carlos Maximiliano Giorgio Bort, Marco Leonesio, Paolo BosettiAbstract:Seeking a higher level of automation, according to Intelligent Manufacturing paradigm, an optimal process control for milling process has been developed, aiming at optimizing a multi-objective target function defined in order to mitigate vibration level and surface quality, while preserving production times and decreasing Tool wear rate. The control architecture relies on a real-time process model able to capture the most significant phenomena ongoing during the machining, such as cutting forces and Tool vibration (both forced and self-excited). For a given Tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior. For the first time in the literature, following a Model-Predictive-Control (MPC) approach, the controller is able to adapt its actions taking into account process and axes dynamics on the basis of Optimal Control theory. The developed controller has been implemented in a commercial CNC of a 3-axes milling machine manufactured by Alesamonti; the effectiveness of the approach is demonstrated on a real industrial application and the performance enhancement is evaluated and discussed. HighlightsModel-based controller for milling process based on Optimal Control Theory.Multi-objective optimization: vibrations, Tool Deflection, Tool wear and productivity.Real-time model of cutting process based on computation of Tool-workpiece engagement.Implementation and testing on a commercial milling machine by Alesamonti.
Marco Leonesio - One of the best experts on this subject based on the ideXlab platform.
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a model based adaptive controller for chatter mitigation and productivity enhancement in cnc milling machines
Robotics and Computer-integrated Manufacturing, 2016Co-Authors: Carlos Maximiliano Giorgio Bort, Marco Leonesio, Paolo BosettiAbstract:Seeking a higher level of automation, according to Intelligent Manufacturing paradigm, an optimal process control for milling process has been developed, aiming at optimizing a multi-objective target function defined in order to mitigate vibration level and surface quality, while preserving production times and decreasing Tool wear rate. The control architecture relies on a real-time process model able to capture the most significant phenomena ongoing during the machining, such as cutting forces and Tool vibration (both forced and self-excited). For a given Tool path and workpiece material, an optimal sequence of feedrate and spindle speed is calculated both for the initial setup of the machining process and for the continuous, in-process adaptation of process parameters to changes the current machining behavior. For the first time in the literature, following a Model-Predictive-Control (MPC) approach, the controller is able to adapt its actions taking into account process and axes dynamics on the basis of Optimal Control theory. The developed controller has been implemented in a commercial CNC of a 3-axes milling machine manufactured by Alesamonti; the effectiveness of the approach is demonstrated on a real industrial application and the performance enhancement is evaluated and discussed. HighlightsModel-based controller for milling process based on Optimal Control Theory.Multi-objective optimization: vibrations, Tool Deflection, Tool wear and productivity.Real-time model of cutting process based on computation of Tool-workpiece engagement.Implementation and testing on a commercial milling machine by Alesamonti.
Kazuhisa Yanagi - One of the best experts on this subject based on the ideXlab platform.
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estimation of machined surface texture being based on cutter run out trajectory and cutting edge profile of small diameter end mill 1st report analytical model and geometrical transfer of run out components
Journal of The Japan Society for Precision Engineering, 2009Co-Authors: Teppei Nebuka, Hidetake Tanaka, Kazuhisa YanagiAbstract:This study deals with a development of analytical estimation system for machined surface texture based on Tool run-out and cutting edge profile measurement for end-mills by optical method. In the article, geometrical cutting model is proposed with a consideration of Tool Deflection, Tool run-out and cutting edge profile. In order to obtain those analytical parameter values, we constructed certain optical equipments for measuring spindle rotation trajectory and cutting edge profile in three dimensions. Through an analytical consideration of Tool run-out trajectory, we revealed that the Tool run-out components (both RRO and NRRO) are geometrically transferred to the machined surface as the corresponding wavelength components. From the experimental result, wavelength spectrum between surface roughness profile and Tool run-out trajectory is closely conformed to the analytical simulation.
Teppei Nebuka - One of the best experts on this subject based on the ideXlab platform.
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estimation of machined surface texture being based on cutter run out trajectory and cutting edge profile of small diameter end mill 1st report analytical model and geometrical transfer of run out components
Journal of The Japan Society for Precision Engineering, 2009Co-Authors: Teppei Nebuka, Hidetake Tanaka, Kazuhisa YanagiAbstract:This study deals with a development of analytical estimation system for machined surface texture based on Tool run-out and cutting edge profile measurement for end-mills by optical method. In the article, geometrical cutting model is proposed with a consideration of Tool Deflection, Tool run-out and cutting edge profile. In order to obtain those analytical parameter values, we constructed certain optical equipments for measuring spindle rotation trajectory and cutting edge profile in three dimensions. Through an analytical consideration of Tool run-out trajectory, we revealed that the Tool run-out components (both RRO and NRRO) are geometrically transferred to the machined surface as the corresponding wavelength components. From the experimental result, wavelength spectrum between surface roughness profile and Tool run-out trajectory is closely conformed to the analytical simulation.