Development of Automatic Chatter Suppression System in Parallel Milling by Real-Time Spindle Speed Control with Observer-Based Chatter Monitoring

Abstract

To maximize the potential for high material removal rates in simultaneous processes such as parallel milling, developing strategies for successful chatter suppression/avoidance is an important concern for manufacturers. In this study, the effectiveness of the spindle speed difference method (SDM) for chatter suppression is discussed in a parallel end-milling process where a flexible workpiece is machined by two tools rotating in opposite direction. The process model is developed, considering that the dynamic variation due to the regenerative effect occurs on a plane perpendicular to the tool axis direction. Through the process simulations and the experiments, this study provides informative discussion for comprehending the process behavior. Additionally, a real-time active chatter suppression system with adaptive SDM, where the spindle speed difference is sequentially optimized during the process according to the tracked chatter frequency, is developed by integrating a chatter monitoring system based on sensorless cutting force estimation with sliding discrete Fourier transform. The results show that the developed real-time adaptive system of spindle speed suppresses chatter vibrations more effectively than non-adaptive SDM system; hence, the integrated system can contribute self-optimizing machining systems oriented to Industry 4.0. Keywords Parallel milling • Chatter • Process control • Speed difference method • Disturbance observer Abbreviations a p Axial depth of cut a t Acceleration of table c f Feed per tooth dF t , dF r , dF a Minute cutting force in tool tangential, radial, and axial directions dF x , dF y , dF z Minute cutting force in Cartesian coordinate system F cut Cutting force F stat Non-process-related force including friction and gravity terms F x , F y , F z Cutting force in Cartesian coordinate system g(θ) Unit step function to judge tooth engagement G(iω) Frequency response function G LPF (s) Low-pass filter h Uncut chip thickness I a ref Motor current reference J r Total inertia of motor, coupling, and ball screw k c Chatter lobe number K t Torque coefficient K te , K re , K ae Edge force coefficient in tangential, radial, and axial directions K tc , K rc , K ac Cutting force coefficients in tangential, radial, and axial directions

Chatter Stability Prediction and Process Parameters’ Optimization of Milling Considering Uncertain Tool Information

“…The input vector was processed by linear functions and then transformed into the pattern layer. The neuron number of the pattern layer equaled the number of training samples, and its transfer function was the radial basis function as expressed in Equation (7).…”

“…Generally, the stability lobe diagram (SLD) described by different combinations of cutting depth and spindle speed is utilized to select appropriate chatter-free milling parameters to avoid chatter. And the SLD can be usually obtained by performing chatter tests or solving the analytical chatter stability model [6,7].…”

Integrated Design of Spindle Speed Modulation and Cutting Vibration Suppression Controls Using Disturbance Observer for Thread Milling

“…However, some factors, such as friction and dynamics of the feed drive and spindle motors, could contaminate current signals and thus degrade the cutting force and cutting torque control performance. Therefore, as the cutting force and disturbance are related, a disturbance observer was adopted to estimate the cutting force in milling [24,25] and several studies have focused on the applications of chatter detection [26,27], machining type detection [28], cutting force monitoring [29], and tool fracture detection [30]. In this study, in contrast to previous studies, a feedback control method integrated with a DOB was developed to estimate and feedback cutting torque, modulate spindle speed, and make the cutting torque in thread milling achieve a preset torque command.…”

“…Therefore, the DOB was used to detect the cutting vibrations in milling. Yamato et al [26] developed an adaptive method that integrated a DOB to monitor the chatter in a parallel end-milling process to adjust spindle speed for chatter suppression in real-time. In their method, the DOB was used to estimate the cutting force and the sliding discrete Fourier transform processed the estimated cutting force for monitoring the chatter and obtaining the chatter frequency.…”

Chatter Stability Improvement of a Slender Milling Tool via a Dynamics Variation Mechanism

“…A harmonic SSV system is also used to improve chatter stability [28,29]. Yamato et al developed an automatic chatter suppression system based on real-time spindle speed control for the parallel milling process [30]. In addition, Ding et al proposed an active chatter suppression system based on model-free adaptive sliding mode spindle speed variation [31].…”