Stiffness performance index based posture and feed orientation optimization in robotic milling process

Chen, Chen; Peng, Fangyu; Yan, Rong; Li, Yuting; Wei, Dequan; Fan, Zheng; Tang, Xiaocheng; Zhu, Zhi‐Biao

doi:10.1016/j.rcim.2018.07.003

Cited by 127 publications

(41 citation statements)

References 22 publications

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“…However, they have not validated the method experimentally. To optimise a feed orientation, Chen et al [105] proposed a normal stiffness performance index which is derived from the comprehensive stiffness performance index, based on the relationship between external force and EE, to evaluate robot stiffness on a given posture. Cutting force and cutting vibration are sensitive factors for robotic machining.…”

Section: Machining Processmentioning

confidence: 99%

Industrial robotic machining: a review

Wang

2019

Int J Adv Manuf Technol

264

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For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

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Section: Machining Processmentioning

confidence: 99%

Industrial robotic machining: a review

Wang

2019

Int J Adv Manuf Technol

264

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“…To this end, an indirect method is used: the displacement of PZT is measured by a capacitive sensor, and then the cutting force is calculated by making use of the map between the cutting force and the displacement that is established by machining experiments. According to the methods proposed in References 29,30, a series of machining experiments is conducted and the relationship between the cutting force and the cutting depth is shown in the right figure of Figure 2, which is used to develop the force‐displacement map via the standard polynomial fitting technique, as given in the following

F_{N} = 2.07991 ℓ^{3} - 14.59912 ℓ^{2} + 36.5269 ℓ - 16.8154,

where F N is the cutting force and ℓ is the cutting depth, that is, the displacement of PZT approximately. Note that the map between the cutting force and the cutting depth is not linear and it can be divided into three regions: for the first region of cutting forces 5, 8, and 11 mN, this might be attributed to the absence of the cutting edges of spherical crown when the indentation depth was small.…”

Section: System Identification and Modelingmentioning

confidence: 99%

Robust model predictive control of a micro machine tool for tracking a periodic force signal

Wang

Geng

Yan

et al. 2020

Optim Control Appl Methods

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Summary The micro machine tool that can produce nanostructures by force modulation approach plays a significant role in nanotechnology. In this paper, to guarantee fast and high‐precision cutting subject to external disturbances and input saturation, a robust model predictive control (MPC) using a tube‐based method is exploited to develop a controller for the machining system consisting of a piezoelectric tube (PZT) actuator, a force sensor and a cutting tool, which updates the state of the art. In particular, the dynamic model of the machining system, with the voltage fed into PZT being input and the cutting force being output, is identified by incorporating the map between the cutting force and the displacement of PZT. Based on the voltage‐force dynamic model, a tube‐based MPC controller that consists of two optimizers is used to make PZT actuator track a desired periodic force signal. Finally, the effectiveness of the MPC method for force signal tracking under different frequencies is validated and advantages over the conventional proportional integral controller are also shown in the presence of the constraints of saturated input and external disturbances via numerical simulations.

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“…This condition, if theoretically may be always met, in practice cannot because the parameter H may only have values in accordance with the physical limits of the mechanical linear guide. Chen et al (2019) highlighted that position errors have a high sensitivity to angular position of rotation motors and angular errors. Their relation may be evaluated writing the infinitesimal variation of the positional parameters in function of the infinitesimal variation of the axes variables:…”

Section: Minimization Of the Joint Torquementioning

confidence: 99%

The optimization of the control logic of a redundant six axis milling machine

Caputi

Russo

2020

J Intell Manuf

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The primary task of machine tools is simultaneously positioning and orienting the cutting tool with respect to the work piece. The mechanism must avoid positioning errors, and limit forces and torques required to the motors. A novel approach for combined design and control of manufacturing means is proposed in this work. The focus is on the optimization of the control logic of a redundant 6 axis milling machine, derived from the 5 axis milling machine by adding redundant degree of freedom to the work piece table. The new mechanism is able to fulfill a secondary task due to the introduction of redundancy. The proposed methodology sets as secondary task the minimization of the rotary motors torque, or the minimization of the norm of the positioning error. The control is based on the solution of a constrained optimization problem, where the constraints equations are the kinematic closure equations, and the objective function is the table motor torque or the positioning error of the tool tip. The implementation of this framework in the virtual machine model of the mechanism shows an improvement of the performances: actually, the introduction of a redundant axis allows the minimization of the torques and position errors.

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Stiffness performance index based posture and feed orientation optimization in robotic milling process

Cited by 127 publications

References 22 publications

Industrial robotic machining: a review

Industrial robotic machining: a review

Robust model predictive control of a micro machine tool for tracking a periodic force signal

The optimization of the control logic of a redundant six axis milling machine

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