Adaptive Control Constraint of Machining Processes

Liu, Yan; Cheng, Tao; Zuo, Lei

doi:10.1007/s001700170117

Cited by 24 publications

(14 citation statements)

References 42 publications

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“…In a study by Liu et al (2001), the major adaptive control constraint systems were discussed based on the feedback control, parameter adaptive control/self-tuning control, model reference adaptive control, variable structure control/sliding mode control, neural network control, and fuzzy control. Their typical applications to constant cutting force control system are also described, and some recent experiments results were presented.…”

Section: Literature Surveymentioning

confidence: 99%

“…Because of this reason TWR is not a measurable value as real time. Constraint Adaptive Control was classified by Liu et al (2001) as below, -A feedback controller-based ACC system, -Self-tuning control-based ACC system, -A model reference adaptive control-based ACC system, -A variable structure system-based ACC system, -A neural network-based ACC system, -A fuzzy control-based ACC system.…”

Section: Adaptive Control Constraint (Acc)mentioning

confidence: 99%

See 1 more Smart Citation

Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system

Asiltürk

Ünüvar

2009

Journal of Materials Processing Technology

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Section: Literature Surveymentioning

confidence: 99%

Section: Adaptive Control Constraint (Acc)mentioning

confidence: 99%

Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system

Asiltürk

Ünüvar

2009

Journal of Materials Processing Technology

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“…However, the use of smart actuators is not entirely satisfying. It is especially the case for the reaction to disturbances mainly because their behaviors are strongly predetermined like in the particular case of adaptation techniques that aim at keeping constant the cutting forces [9,10]. Those adaptations are specialized and do not entirely match flexibility requirements.…”

Section: Smart Actuator Conceptmentioning

confidence: 99%

“…Adaptive controls are solutions to counter the changes of the machining process parameters like tool wear, depth of cut, vibrations, etc., but they are often very dedicated. Indeed they can keep the cutting force constant or aim at reducing vibrations [9,10].…”

Section: Introductionmentioning

confidence: 99%

Design methodology for smart actuator services for machine tool and machining control and monitoring

Desforges

Habbadi

Archimède

2011

Robotics and Computer-Integrated Manufacturing

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International audienceThis paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented

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“…Conclusions from Pan and Wang determine that "The pattern of the process structure deformation is related to all of the following parameters: robot configuration, the location in the workspace, and the direction as well as the magnitude of the process force" [20], depicting the relevance to optimize robot configurations in relation to the robot stiffness. Liu proposes an adaptive control constraint of machining processes with a constant cutting force control system [22]. Olof ensures time-efficiency and improves the accuracy of robotic machining processes by removing the maximum amount of material per time unit by adaptively controlling the applied force, as well as continuously compensating for the path deviations caused by process forces [23].…”

Section: Introductionmentioning

confidence: 99%

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

et al. 2017

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Optimal and intuitive robotic machining is still a challenge. One of the main reasons for this is the lack of robot stiffness, which is also dependent on the robot positioning in the Cartesian space. To make up for this deficiency and with the aim of increasing robot machining accuracy, this contribution describes a solution approach for optimizing the stiffness over a desired milling path using the free degree of freedom of the machining process. The optimal motion is computed based on the semantic and mathematical interpretation of the manufacturing process modeled on its components: product, process and resource; and by configuring automatically a sample-based motion problem and the transition-based rapid-random tree algorithm for computing an optimal motion. The approach is simulated on a CAM software for a machining path revealing its functionality and outlining future potentials for the optimal motion generation for robotic machining processes.

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Adaptive Control Constraint of Machining Processes

Cited by 24 publications

References 42 publications

Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system

Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system

Design methodology for smart actuator services for machine tool and machining control and monitoring

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

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