Compensation of friction and force ripples in the estimation of cutting forces by neural networks

Heydarzadeh, M.S.; Rezaei, Seyed Mehdi; Azizi, Nurul Zeety

doi:10.1016/j.measurement.2017.09.032

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…where αxj, βxj, αyij, and βyij denote the coefficients of the trigonometric series; ωxj and ωyij denote jth frequency; j = 1, 2, …, 6. Moreover, χx and χyi denote friction forces that can be expressed as follows [12]:…”

Section: Lms-driven X-y-y Stagementioning

confidence: 99%

“…C. J. Lin et al investigate the parameters of the nonlinear friction model using particle swarm optimization and genetic algorithms [11]. M. S. Heydarzadeh et al use neural networks to estimate the friction and force ripple of LMs [12]. However, implementing the above-mentioned control approach requires proper tuning for the controller gains to achieve the good performance.…”

Section: Introductionmentioning

confidence: 99%

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Contour Tracking Control of a Linear Motors-Driven X-Y-Y Stage Using Auto-Tuning Cross-Coupled 2DOF PID Control Approach

et al. 2020

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Linear motors (LMs) are widely used in numerous industry automation where precise and fast motions are required to convert electric energy into linear actuation without the need of any switching mechanism. This study aims to develop a control strategy of auto-tuning cross-coupled two-degree-of-freedom proportional-integral-derivative (ACC2PID) to achieve extremely high-precision contour control of a LMs-driven X-Y-Y stage. Three 2PID controllers are developed to control the mover positions in individual axes while two compensators are designed to eliminate the contour errors in biaxial motions. Furthermore, an improved artificial bee colony algorithm is employed as a powerful optimization technique so that all the control parameters can be concurrently evaluated and optimized online while ensuring the non-fragility of the proposed controller. In this way, the tracking error in each axis and contour errors of the biaxial motions can be concurrently minimized, and further, satisfactory positioning accuracy and synchronization performance can be achieved. Finally, the experimental comparison results confirm the validity of the proposed ACC2PID control system regarding the multi-axis contour tracking control of the highly uncertain and nonlinear LMs-driven X-Y-Y stage.

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Section: Lms-driven X-y-y Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contour Tracking Control of a Linear Motors-Driven X-Y-Y Stage Using Auto-Tuning Cross-Coupled 2DOF PID Control Approach

et al. 2020

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“…Set M 0 h = 0, substituting equation 12into equation 5and applying the Laplace transform to equations (2) and 5yields…”

Section: Friction Modelmentioning

confidence: 99%

“…1 To deal with the friction problem, designing an appropriate compensation scheme is a common solution. Some non-model-based compensation methods have been developed such as neural networks, 2 non-smooth HN-tracking synthesis, 3 Kalman filter, 4 and reduced observer, 5 which have the advantage that information of complex dynamics is not required in advance and we do not have to identify the parameters of friction model with experiments. However, since the behavior of the nonlinear factor is ignored and considered as disturbances in these methods, there always remains a non-null estimation error.…”

Section: Introductionmentioning

confidence: 99%

Friction compensation and observer-based adaptive sliding mode control of electromechanical actuator

Zhang

Zhou

Liu

et al. 2018

Advances in Mechanical Engineering

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The performance of the electromechanical actuator system is usually affected by the nonlinear friction torque disturbance, model uncertainty, and unknown disturbances. In order to solve this problem, a model-based friction compensation method combined with an observer-based adaptive sliding mode controller for the speed loop of electromechanical actuator system is presented in this article. All the disturbances and model uncertainty of electromechanical actuator system are divided into two parts. One is model-based friction torque disturbance which can be identified by experiments, and the other is the residual disturbance which cannot be identified by experiments. A modified LuGre model is adopted to describe the friction torque disturbance of electromechanical actuator system. An extended state observer is designed to estimate the residual disturbance. An adaptive sliding mode controller is designed to control the system and compensate the friction torque disturbance and the residual disturbance. The stability of the electromechanical actuator system is discussed with Lyapunov stability theory and Barbalat's lemma. Experiments are designed to validate the proposed method. The results demonstrate that the proposed control strategy not only provides better disturbance rejecting ability but also provides better steady state and dynamic performance.

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“…They showed that the developed ANN model can be a useful tool to fix the cutting parameters in order to achieve a desired surface finish. Heydarzadeh et al [17] estimated the cutting forces using ANNs. They used the designed method and proved that it was successfully applied to the precise estimation of micro-milling forces, in order to estimate tool deflections.…”

Section: Introductionmentioning

confidence: 99%

First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors

Mirifar

Kadivar

Azarhoushang

2020

JMMP

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The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness.

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Compensation of friction and force ripples in the estimation of cutting forces by neural networks

Cited by 16 publications

References 23 publications

Contour Tracking Control of a Linear Motors-Driven X-Y-Y Stage Using Auto-Tuning Cross-Coupled 2DOF PID Control Approach

Contour Tracking Control of a Linear Motors-Driven X-Y-Y Stage Using Auto-Tuning Cross-Coupled 2DOF PID Control Approach

Friction compensation and observer-based adaptive sliding mode control of electromechanical actuator

First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors

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