Double-loop robust tracking control for micro machine tools

Fan, Sisi; Nagamune, Ryozo; Ding, Fan

doi:10.1007/s11431-011-4561-3

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…To break through the limitations of conventional milling dynamic model for prediction of the geometric accuracy of machined surfaces, we should fuse the process dynamics with the multi-physics model [74][75][76][77], developing efficient numerical methods to explore the macro/micro performance of machined surfaces with respect to process parameters. (4) The last one is to fuse the dynamics model with some advanced machine tool control strategies [78][79][80][81][82][83][84][85][86][87][88] for high performance control ensuring high performance machining. …”

Section: Discussionmentioning

confidence: 99%

On time-domain methods for milling stability analysis

Ding

Zhu

2012

Chin. Sci. Bull.

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As a basic and advanced machining technique, the high-speed milling process plays an important role in realizing the goal of high performance manufacturing. From the viewpoint of machining dynamics, obtaining chatter-free machining parameters is a prerequisite to guaranteeing machining accuracy and improving machining efficiency. This paper gives an overview on recent progress in time domain semi-analytical methods for chatter stability analysis of milling processes. The state of art methods of milling stability prediction in milling processes and their applications are introduced in detail. The bottlenecks involved are analyzed, and potential solutions are discussed. Finally, a brief prospect on future works is presented. High speed milling is widely utilized in manufacturing of complex surfaces for the fields of aerospace, ship, automotive, dies and molds etc., due to its advantage of obtaining high machining accuracy and high material removal rate with keeping low-amplitude cutting forces. Regarding to modeling of machining processes, the research topics on high speed milling can be categorized as: tool path planning, machining dynamics and machining physical modeling. Tool path planning is to plan the tool path according to the workpiece model, machining strategy and required machined error [1][2][3][4][5]. From the viewpoint of machining dynamics, the relative vibration between the workpiece and cutter in the milling process is the main reason of reducing product surface quality and limiting the production efficiency. To suppress the vibration impact by optimizing the machining parameters, the following two questions need to be addressed.(1) Stability analysis based on the dynamics of milling processes. As far as chatter vibrations are concerned, there are four different mechanisms: regeneration [6], mode coupling [7], friction [8] and thermo-mechanics of chip formation [9]. In milling processes, regenerative chatter is the most common form of self-excited vibration to reduce surface quality and machining efficiency [10,11]. The works on analysis of the stability of milling processes focus on calculating the stability boundary of the machining parameters based on the dynamic models characterizing the milling processes.(2) Machining accuracy analysis on the basis of stability analysis. Due to forced vibrations in milling processes, chatter-free machining parameters cannot ensure high performance machining. Vibration-induced surface errors can be classified as surface location error (SLE) [12] and surface roughness [13]. It is essential to take the stability and dynamical errors into account in machining optimization models to achieve high performance milling.

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Section: Discussionmentioning

confidence: 99%

On time-domain methods for milling stability analysis

Ding

Zhu

2012

Chin. Sci. Bull.

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“…For the angular motion of z ‐axis, the mathematical modelling of servomotor is given as (Fan et al, 2011; Yadav & Gaur, 2020):

τ_{m} (t) = k_{t} i = J_{e} \dot{ω_{z}} (t) + B_{e} ω_{z} (t) + τ_{c},

where equivalent inertia is denoted by

J_{e}

and equivalent damping coefficient is denoted by

B_{e}

, angular speed of the motor in the z ‐axis direction is given by

ω_{z} (t),

motor torque and motor torque constant are

τ_{m}

and

k_{t}

respectively, motor current is denoted by

i

, and

τ_{c}

is the torque component that appeared in the system due to cutting force type disturbance. A detailed diagrammatic representation of considered CNC machine in form of blocks, for the control of linear position and angular speed along x ‐axis and z ‐axis is represented in Figure 2, which is obtained from Figure 1, and ().…”

Section: System Description and Mathematical Modellingmentioning

confidence: 99%

“…For the angular motion of z-axis, the mathematical modelling of servomotor is given as (Fan et al, 2011;:…”

Section: Modelling Of Z-axis Angular Motionmentioning

confidence: 99%

“…The forecasting of cutting forces in the milling process using modified internal model control (M‐IMC) (Yadav & Gaur, 2020), adaptive neuro‐fuzzy inference system (Yang et al, 2018), feed rate scheduling algorithm (Jamaludin et al, 2009), disturbance observer (Aslan & Altintas, 2018a), feed drive current measurements (Fan et al, 2011) are briefed. Various other control schemes, such as;

H_{\infty}

control

μ

synthesis (Kim & Jeon, 2011), fuzzy logic and artificial neural network (ANN) (Aslan & Altintas, 2018b; Jee & Koren, 2004; Molina et al, 2014), observer‐based control (Choi et al, 1999), model‐based control (Bort et al, 2016; Sencer & Dumanli, 2017) and optimal linear quadratic regulator (Ang et al, 2005) are reported related to milling process of CNC machine.…”

Section: Introductionmentioning

confidence: 99%

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BWOA based metaheuristic approach for uncertain nonlinear milling CNC machine system

2023

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The scientific advances in the machining process inculcate intelligence in computer numerical control (CNC), to enhance reliability and quality production. The study made in this article has been directed towards the metaheuristic-based intelligent control of a nonlinear uncertain CNC machine for milling purposes. The presence of cutting force and frictional type constraints, along with parametric uncertainties, degrade the production quality in milling operations. Moreover, enhanced performance of milling operation can be achieved via intelligent control of position and velocity of machine table and tool. In this work, the black widow optimization algorithm (BWOA) driven proportional-integral-derivative (PID) and PI controllers are employed for the position and the velocity of machine table and tool, respectively.Servo and regulatory are the two important control problems that are considered, analysed, and addressed in this work. The most challenging task of simultaneous tuning of PI and PID controllers is also addressed in this brief. Finally, a vivid comparative analysis of five state-of-the-art optimization techniques is also performed, and the obtained results demonstrate the superiority of the proposed control scheme. While dealing with the servo performance of x-axis, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time, and for regulatory performance, it gives a 98.45% improvement in undershoot. Moreover, for z-axis control, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time and for regulatory performance gives a 100% improvement in undershoot compared to published literature.

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“…12–14 The coupling inertia was considered as a load perturbation for each control channel, and a robust control method was proposed to suppress the load perturbation. 15–17 In Fan et al’s 18 articles, a double-loop robust tracking controller was proposed to solve the disturbance torque and load perturbation in two-axis pointing mechanism; a DOB and a μ-synthesis were employed to suppress the disturbance torque and load perturbation as an inner loop and outer loop, respectively. However, in the differential cable drive ISP, there are inertia, torque and angular position coupling factors.…”

Section: Introductionmentioning

confidence: 99%

Dynamics analysis and decoupling control for a differential cable drive inertially stabilized platform

Liao

Lü

Fan

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a decoupling control method is proposed to solve the coupling problem of the differential cable drive inertially stabilized platform. First, the transmission chain and motion of the inertially stabilized platform are briefly illustrated, and the dynamics is analysed based on the Lagrange equation. According to the established dynamic equation, the coupling of inertia, torque and angular position between two motors are analysed. Then, the decoupling controller is used to address the coupling problems, and the numerical simulation is realized in the MATLAB/Simulink. The experimental results show that the dynamic equation can accurately describe the dynamic characteristic of the differential cable drive inertially stabilized platform, and the control performance is improved by suppressing the coupling factors.

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Double-loop robust tracking control for micro machine tools

Cited by 4 publications

References 18 publications

On time-domain methods for milling stability analysis

On time-domain methods for milling stability analysis

BWOA based metaheuristic approach for uncertain nonlinear milling CNC machine system

Dynamics analysis and decoupling control for a differential cable drive inertially stabilized platform

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