Model-based chatter stability prediction and detection for the turning of a flexible workpiece

Lu, Kaibo; Zi-sheng, Lian; Gu, Fengshou; Hun-ju, Liu

doi:10.1016/j.ymssp.2017.08.022

Cited by 64 publications

(25 citation statements)

References 31 publications

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“…These authors verified the capacity of the method for different dynamic conditions since the piece continuously loses mass. Later, Lu et al [58] investigated the initiation mechanisms of chatter according to the position of the tool during the turning of flexible parts. Urbikain et al [59] presented a stability prediction model for the interrupted turning of cylindrical pieces.…”

Section: Of 18mentioning

confidence: 99%

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

et al. 2019

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The general trend towards lightweight components and stronger but difficult to machine materials leads to a higher probability of vibrations in machining systems. Amongst them, chatter vibrations are an old enemy for machinists with the most dramatic cases resulting in machine-tool failure, accelerated tool wear and tool breakage or part rejection due to unacceptable surface finish. To avoid vibrations, process designers tend to command conservative parameters limiting productivity. Among the different machining processes, turning is responsible of a great amount of the chip volume removed worldwide. This paper reports some of the main efforts from the scientific literature to predict stability and to avoid chatter with special emphasis on turning systems. There are different techniques and approaches to reduce and to avoid chatter effects. The objective of the paper is to summarize the current state of research in this hot topic, particularly (1) the mechanistic, analytical, and numerical methods for stability prediction in turning; (2) the available techniques for chatter detection and control; (3) the main active and passive techniques.

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Section: Of 18mentioning

confidence: 99%

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

et al. 2019

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“…The receptance coupling method was used by Jasiewicz and Powalka [14] for determining the lathe workpiece dynamics and inverse receptance coupling was applied for the spindle dynamics. Lu et al [15] focused on turning process and proposed a model for chatter prediction for turning a tailstock-supported flexible rod. They considered the tool position effect along the workpiece axis.…”

Section: Introductionmentioning

confidence: 99%

Machining Chatter Prediction Using a Data Learning Model

Cherukuri

Perez‐Bernabeu

Sellès

et al. 2019

JMMP

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Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit a self-excited vibration. In this paper, an artificial neural network (ANN)—a data learning model—is applied to model turning stability. The novel approach is to use a physics-based process model—the analytical stability limit—to generate a (synthetic) data set that trains the ANN. This enables the process physics to be combined with data learning in a hybrid approach. As anticipated, it is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.

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“…Whereas, the forced-chatter usually can be vanished by removing or adjusting the external force. All types of chatter have many destructive effects such as increasing cutting forces, poor surface finish, decreasing of the tool and lathe lifetime, imprecise dimensions of the product, and load noises [2].…”

Section: Introductionmentioning

confidence: 99%

System Identification and Model Predictive Control of the Chatter Phenomenon in Turning Process

Karimipour¹,

Emami²

2019

Adv. Sci. Technol. Res. J.

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Chatter is a series of unwanted and extreme vibrations which frequently happens during different machining processes and impose variety of adverse effects on the machine-tool and surface finish. Chatter has two main types namely forced-chatter and self-existed chatter. The forced-chatter has an external cause; however, self-exited chatter has no external stimuli, rather it is created due to the phase difference between the previous and current waves on the surface of the workpiece. Due to the self-generative nature of this type of chatter, its recognition and prevention is much more difficult. For preventing self-exited chatter its model should be available first. The chatter is usually simulated as a one degree of freedom mass-spring-damper model with unknown parameters that they should be determined somehow. In this paper, the parameters of the tool equation of motion i.e. mass, damping, and stiffness coefficients of the system are predicted through a wavelet-based method online, and then based on the achieved parameters, the system is controlled via Model Predictive Control (MPC) approach. For the validation, the algorithm is applied to 25 different experimental tests in which the acceleration of the tool and cutting force are measured via an accelerometer and a dynamometer. By investigation of the SLDs generated by the predicted parameters, the presented system identification method is validated. Also, it is shown that the chatter vibration is completely restrained by means of MPC. For investigation of the MPC performance, MPC algorithm is compared with PID controller and simulations has indicated a much stronger performance of MPC rather than PID controller in terms of vibration attenuation and control effort.

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Model-based chatter stability prediction and detection for the turning of a flexible workpiece

Cited by 64 publications

References 31 publications

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Machining Chatter Prediction Using a Data Learning Model

System Identification and Model Predictive Control of the Chatter Phenomenon in Turning Process

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