Effect of state-dependent time delay on dynamics of trimming of thin walled structures

Acknowledgments This work was supported by the National Key R&D Program of China (2020YFA0714900), National Natural Science Foundation of China (52075205, 92160207, 52090054, 52188102).Peer reviewedPostprin

The comprehensive analysis of milling stability and surface location error with considering the dynamics of workpiece

Cutting movement is still one of the main means to obtain the desired machined surface. As the most representative cutting method in subtractive manufacturing, milling is widely used in industrial production. However, the chatter induced by the dynamic interaction between machine tool and process not only reduces the accuracy of the machined workpiece, but also increases the tool wear and affects the rotary accuracy of the spindle. The stability lobe diagram can provide stable machining parameters for the technicians, and it is currently an effective way to avoid chatter. In fact, the dynamic interaction between the machine tool and process is very complicated, which involves the machine tool, milling tool, workpiece and fixture. The induced mechanism of chatter depends on different machining scenarios and is not entirely dependent on the vibration modes of milling tool. Therefore, it is important to obtain stable machining parameters and to know the dynamic surface location error distribution, which can ensure machining quality and improve machining efficiency. In this dissertation, two methods for constructing stability lobe diagram are first introduced, and then two machining scales, macro milling and micro milling, are studied. For the macro-milling scale, the dynamic response of the in-process workpiece with time-varying modal parameters during the material removal process is analyzed. The stability lobe diagrams for thin-walled workpiece and general workpiece with continuous radial immersion milling are established respectively. Besides, the cumulative surface location error distribution is also studied and verified for the general workpiece. For the micro-milling scale, the dynamics at the micro-milling tool point is obtained by means of the receptance coupling substructure analysis method. The stability lobe diagram and surface location error distribution are analyzed under different restricted/free tool overhang lengths. The relationship between measurement results and burrs is further explained by cutting experiments, and the difference between the two milling scales is compared in the end

Vibration behavior in modulated tool path (MTP) turning

This project studies the process dynamics and surface finish effects of modulated tool path (MTP) turning. In MTP turning, a small amplitude (typically less than 0.5 mm), low frequency oscillation (typically less than 10 Hz) is superimposed on the feed motion by the machine controller to intentionally segment the traditionally long, continuous chips. The basic science to be examined is the vibration behavior of this special case of interrupted cutting, which is not turning because the chip formation is intentionally discontinuous and is not milling because the time-dependent chip geometry is defined by the oscillatory feed motion, not the trochoidal motion of a rotating and translating milling cutter. The hypothesis that MTP will exhibit forced vibration and secondary Hopf bifurcation (a type of unstable machining conditions) depending on the MTP and machining parameters is tested. A physics-based model of the MTP process is derived and implemented through a second-order, time-delay differential equation math model. This model is used to establish the relationship between: 1) the vibration behavior; and 2) the MTP amplitude and frequency, chip width, spindle speed, nominal feed, and structural dynamics. Experiments are presented to validate the math model accuracy and understand the implications of machining stability and workpiece surface finish

Active chatter control in high-speed milling processes

In present day manufacturing industry, an increasing demand for highprecision products at a high productivity level is seen. High-speed milling is a manufacturing technique which is commonly exploited to produce highprecision parts at a high productivity level for the aeroplane, automotive and mould and dies industry. The performance of a manufacturing process such as high-speed milling, indicated by the material removal rate, is limited by the occurrence of a dynamic instability phenomenon called chatter. The occurrence of chatter results in an inferior workpiece quality due to heavy vibrations of the cutter. Moreover, a high level of noise is produced and the tool wears out rapidly. Although different types of chatter exist, regenerative chatter is recognised as the most prevalent type of chatter. The occurrence of (regenerative) chatter has such a devastating effect on workpiece quality and tool wear that it should be avoidedat all times. The occurrence of chatter can be visualised in so-called stability lobes diagrams (sld). In an sld the chatter stability boundary between a stable cut (i.e. without chatter) and an unstable cut (i.e. with chatter) is visualised in terms of spindle speed and depth of cut. Using the information gathered in a sld, the machinist can select a chatter free operating point. In this thesis two problems are tackled. Firstly, due to e.g. heating of the spindle, tool wear, etc., the sld may vary in time. Consequently, a stable working point that was originally chosen by the machinist may become unstable. This requires a (controlled) adaptation of process parameters such that stability of the milling process is ensured (i.e. chatter is avoided) even under such changing process conditions. Secondly, the ever increasing demand for high-precision products at a high productivity level requires dedicated shaping of the chatter stability boundary. Such shaping of the sld should render working points (in terms of spindle speed and depth of cut) of high productivity feasible, while avoiding chatter. These problems require the design of dedicated control strategies that ensure stable high-speed milling operations with increased performance. In this work, two chatter control strategies are developed that guarantee high-speed chatter-free machining operations. The goal of the two chatter control strategies is, however, different. The first chatter control strategy guarantees chatter-free high-speed milling operations by automatic adaptation of spindle speed and feed (i.e. the feed is not stopped during the spindle speed transition). In this way, the high-speed milling process will remain stable despite changes in the process, e.g. due to heating of the spindle, tool wear, etc. To do so, an accurate and fast chatter detection algorithm is presented which predicts the occurrence of chatter before chatter marks are visible on the workpiece. Once the onset of chatter is detected, the developed controller adapts the spindle speed and feed such that a new chatter-free working point is attained. Experimental results confirm that by using this control strategy chatter-free machining is ensured. It is also shown experimentally that the detection algorithm is able to detect chatter before it is fully developed. Furthermore, the control strategy ensures that chatter is avoided, thereby ensuring a robust machining operation and a high surface quality. The second chatter control strategy is developed to design controllers that guarantee chatter-free cutting operations in an a priori defined range of process parameters (spindle speed and depth of cut) such that a higher productivity can be attained. Current (active) chatter control strategies for the milling process cannot provide such a strong guarantee of a priori stability for a predefined range of working points. The methodology is based on a robust control approach using µ-synthesis, where the most important process parameters (spindle speed and depth of cut) are treated as uncertainties. The proposed methodology will allow the machinist to define a desired working range (in spindle speed and depth of cut) and lift the sld locally in a dedicated fashion. Finally, experiments have been performed to validate the working principle of the active chatter control strategy in practice. Hereto, a milling spindle with an integrated active magnetic bearing is considered. Based on the obtained experimental results, it can be stated that the active chatter control methodology, as presented in this thesis, can indeed be applied to design controllers, which alter the sld such that a pre-defined domain of working points is stabilised. Results from milling tests underline this conclusion. By using the active chatter controller working points with a higher material removal rate become feasible while avoiding chatter. To summarise, the control strategies developed in this thesis, ensure robust chatter-free high-speed milling operations where, by dedicated shaping of the chatter stability boundary, working points with a higher productivity are attained

Tool Wear Prediction System Using Deep-Learning Techniques on High Precision Milling Process

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.O projeto visa implementar um sistema para predição de desgaste de ferramenta em processos de fresamento de alta precisão. A abordagem escolhida utiliza dados de sensores obtidos de testes reais realizados em uma fresadora CNC de alta precisão combinado com medições de desgaste no flanco da ferramenta realizadas via microscópio. Com objetivo de selecionar as melhores metodologias para o desenvolvimento do projeto, uma cuidadosa pesquisa do estado da arte foi realizada. Nesta fase, as arquiteturas de deep-learning e estudos envolvendo do fenômeno de desgaste foram revisadas no intuito de selecionar a abordagem mais apropriada para a resolução do problema. O sistema de aquisição implementado grava dados de: potência consumida pelo eixo-árvore, emissão acústica e hipersônica, forças aplicadas na ponta da ferramenta e vibração no corpo do eixo-árvore. Portanto, o documento aborda o setup de hardware e software para o sistema de aquisição utilizado na máquina, planejamento dos parâmetros do processo e análise dos dados coletados. Além disso, o projeto e implementação do sistema de aquisição é descrito. Depois disso, o projeto e implementação do módulo de pré-processamento é relatado. Depois que os dados dos experimentos são adquiridos, os arquivos são processados por este módulo, que extrai informação de emissão acústica, ultrassônica, e vibração para gerar os datasets. Nessa etapa, todos os datasets são gerados utilizando transformada de Fourier, considerando que um dos objetivos do projeto é comparar o desempenho de diferentes sensores no problema de predição de desgaste de flanco. Ao final, o módulo de predição é descrito. O documento discute o uso de diferentes arquiteturas de redes neurais, técnicas para extração de features e optimização do treinamento. Para o projeto, três diferentes arquiteturas de deep-learning foram escolhidas para a tarefa de predição. O projeto compara o desempenho de cada arquitetura e cada sinal usado. Os resultados mostraram uma performance superior para os dados de vibração em combinação com as redes LSTMs, alcançando 81% de precisão no modo de classificação, e 176 μm de erro quadrático médio no modo de regressão.The project is aimed to implement a system for tool wear prediction for high precision milling process. The approach chosen uses sensor data gathered from real test performed in a CNC machine center for precision milling combined with microscope measurement of tool flank wear. In order to select the best methodologies for the project development, a careful state of the art research was carried out. On this step, the deep-learning architectures and researches involving tool wear phenomena were revised in order to select the most appropriate approach to solve the problem. The acquisition software system implemented records data coming from: the spindle power consumption, acoustic and ultra-sonic emission, forces applied in the tool tip and vibration of the spindle body. Therefore, the document englobes the hardware and software setup for the acquisition system on the machine, process parameters planning and analysis of the data collected. Besides that, the project and implementation of the acquisition software is described. After that, the pre-processing module project and implementation is reported. After the experiment data is gathered, the data files are processed by the pre-processing module, which firstly extracts information from the acoustic, ultra-sonic emission, and vibration files in order to generate the datasets. On this steps, all datasets are generated using fast Fourier transformation, once one of the goals of this work is to compare the performance of different sensors in the flank wear prediction task. At last, the prediction module is described. The document discuss the use of different neural network architectures, feature extraction and training optimization techniques. For the project, three different deep-learning architectures were chosen for the prediction task. The project compares the performance between each architecture and each used signal. The results showed a superior performance for the vibration data in combination with LSTMs achieving 81% of accuracy on the classification approach and a mean squared error of 176 μm on the regression approach