Signal Analysis and Chatter Control Studies in Internal Turning Operations

Abstract

The current manufacturing scenario with industry 4.0 and IoT standards produces a variety of highly precise and accurate products. In spite of several developments in manufacturing technology, the machining instabilities are still inevitable due to several operating factors like work material inhomogeneities, variation of chip cross-section, disturbances in tool or workpiece, environmental disturbances and so on. The vibration instability or chatter conditions results-in a relatively poor surface finish and reduced overall productivity. Particularly, in operations like internal turning and thin walled milling, the chatter vibrations have detrimental effect in achieving accurate products. Present work focuses on the development of some accurate models for dynamic analysis with regenerative and friction chatter forces to understand the machining stability in internal turning and proposes some control methodologies. A dynamic modeling of regenerative chatter is initially studied with one dimensional models and an improved friction-induced regenerative chatter model is developed using a two-degree of freedom (2-DOF) model of internal turning system by considering process damping forces along with nonlinear cubic stiffness terms. The proposed model is validated with the available cutting models using the stability boundary diagram obtained from linear stability analysis performed using the natural parameter continuation method. The stability lobes obtained from the proposed model outperforms and predicts stable zones with good accuracy. Then the nonlinear behaviour of cutting tool in 2-DOF model with Stribeck and regenerative effects under internal resonance and primary resonance conditions is investigated. The nonlinear responses and the cutting stability are determined using higher order multiple time scales method (MTSM). The results obtained from MTSM are validated with a numerical time integration solution. The effect of nonlinearities on the frequency responses and stability is studied further, and the system parameters for stable cutting operation are identified. As the boundaries predicted in the stability lobe diagrams are affected by many surrounding factors such as speed, temperature, tool wear, etc., their reliability is sometimes not acceptable. Therefore, signal based identification approaches are used. The raw signal contaminated by noise is first collected, and a denoising technique is applied, followed by some signal processing method. In the present work, a novel adaptive wavelet threshold (AWT) based wavelet denoising and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) approaches are proposed in order to identify the exact stability states and range of resonance frequency. Further, classification of cutting conditions is performed using an improved probabilistic neural network (PNN) model. The results are compared with the existing approaches and observed that the proposed methods are more accurate compared to the available standard approaches. Cutting experiments are conducted on AISI 1020 workpieces and the vibration signals are extracted practically and processed for the identification of stable states. Towards the design of damped boring bar configuration, the method of constrained layer damping (CLD) is employed with composite material damping layer. The viscoelastic damping layer effectiveness is enhanced by considering different volume fractions of silicon carbide nanoparticles embedded in the carbon-epoxy micro composite. Both analytical and experimental works are conducted to understand the passive vibration control ability of the system. Next, as an extension towards active control, the piezoelectric material is applied for the part of length in constrained layer with optimized damping layer configurations. The dynamic analysis and control of this boring bar is studied as a sandwich model subjected to cutting forces at the tool tip. Proportional derivative with sliding mode control law is used to estimate the required electric field for the piezoelectric materials to minimize the tool tip vibration amplitudes in an online manner. Results of the model are presented for different speeds of operation and unstable vibrations are minimized by proper selection of control gains and location of the piezoelectric patches along the tool length. Furthermore, the effectiveness of control scheme is tested in the presence of external disturbance signal added to the tool dynamics. The disturbance estimation model is developed with radial basis function neural network. Numerical simulations are conducted with finite element modeling of the tool. Overall, few new approaches are proposed for identification and control of chatter in internal turning operations.