Chatter suppression techniques in metal cutting

In this paper, the use of the combined deterministic-stochastic subspace identification algorithm for the experimental modal analysis of mechanical structures is discussed. The algorithm requires artificial forces to be applied to the structure, so it can be used for experimental modal analysis (EMA). The algorithm can also be used for operational modal analysis (OMA), since the excitation level of the artificial force(s) can be low compared to the excitation level of the ambient forces. Both the modes that are artificially excited and those that are excited by the ambient forces are identified. This type of operational modal analysis is called an OMAX analysis (Operational Modal Analysis with eXogenous inputs) [1]. The main advantages of OMAX over OMA are that the modes that are excited by the artificial forces can be scaled to unity modal mass and that a higher number of modes can be identified.

Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis

This paper first reviews different methods of designing thermal error models, before concentrating on employing ANFIS models.The GM(1, N) model and fuzzy c-means clustering are used for variable selection, which is capable of simplifying the system prediction model.The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ?4µm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Intelligent spindles are core components of the next-generation of intelligent/smart machine tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent spindles and provide an in-depth review of the state-of-the-art of related technologies. A new integrated concept for intelligent spindles is proposed, followed by descriptions of required characteristics, key enabling technologies and expected intelligent functions. Relevant research that may be beneficial to the development of intelligent spindles is reviewed from six thrust areas, which include monitoring and control of tool condition, chatter, spindle collision, temperature/thermal error, spindle balance, and spindle health. Finally, current limitations and challenges are discussed, and future trends of intelligent spindles are prospected from various perspectives.

The concept and progress of intelligent spindles: A review

In modern digital manufacturing, nearly 79.6% of the downtime of a machine tool is caused by its mechanical failures. Predictive maintenance (PdM) is a useful way to minimize the machine downtime and the associated costs. One of the challenges with PdM is early fault detection under time-varying operational conditions, which means mining sensitive fault features from condition signals in long-term running. However, fault features are often weakened and disturbed by the time-varying harmonics and noise during a machining process. Existing analysis methods of these complex and diverse data are inefficient and time-consuming. This paper proposes a novel method for early fault detection under time-varying conditions. In this study, a deep learning model is constructed to automatically select the impulse responses from the vibration signals in long-term running of 288 days. Then, dynamic properties are identified from the selected impulse responses to detect the early mechanical fault under time-varying conditions. Compared to traditional methods, the experimental results in this paper have proved that our method was not affected by time-varying conditions and showed considerable potential for early fault detection in manufacturing.

Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification

The dynamic characteristics of joint interfaces affect the dynamic behaviors of a whole machine tool structure notably. An analytic method of virtual material hypothesis-based dynamic modeling on fixed joint interface in machine tools was conducted so as to improve the modeling accuracy of whole machine tools. The microcontact part of two contact surfaces in fixed joint interface was assumed as a virtual isotropic material, which is rigidly connected with two components composing fixed joint interface. The interaction between normal and tangential characteristics of fixed interface was taken into account, a set of analytic solutions of elastic modulus, shear modulus, Poisson ratio and density was deduced from virtual material by adopting Hertz contact theory and fractal theory. Using the finite element structural dynamic modeling approach in existence, when some parameters of material’s elastic modulus, Poisson ratio, density, etc. are known, the dynamic model for virtual material composing a component could be established; therefore, the dynamic model for whole structure, including joint interface, would be obtained. The theoretical mode shapes were compared with the experimental ones (qualitative comparison of similar mode shape and quantitative comparison of the corresponding natural frequency). The comparison results show that the theoretical mode shapes are in excellent agreement with the experimental ones. The relative errors between the theoretical natural frequencies and the experimental ones are less than 9%. The good agreements of theoretical results with experimental ones confirm analytic solutions for virtual material’s parameters. The present solutions would be useful to the precise dynamic modeling of fixed joint interfaces in CNC machine tools in practice.

A new method of virtual material hypothesis-based dynamic modeling on fixed joint interface in machine tools

Tool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, deep learning has been successfully applied to numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel deep learning data-driven modeling framework is presented, which includes a fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultraprecision machining. The proposed computational framework consists of two main structures. First, a training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features. Second, a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultraprecision machining case study with over 96% accuracy, while also outperforming comparable methodologies.

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Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

The dynamics of the machine tool structure are important in high precision machining. Some researchers have studied that the dynamics are expected to change under different machining conditions. However, the dynamic behaviors of the machine tool at different worktable feed speeds are rarely studied. In this paper, an output-only modal identification available to predict the dynamics of the machine tool at different feed speeds is proposed. The excitation of this method uses the inertia force sequence caused by random idle running of the worktable. The first six modes of the entire machine tool structure are estimated using the proposed method. The results indicate that the running state of the worktable can influence the modes in which the worktable vibrates. The estimated natural frequencies and damping ratios decrease obviously as the feed speed increases. Furthermore, because this method enable to determine modal parameters by measuring the response of machine tool structure without using any artificial excitation, it can be used to predict the dynamic behaviors of the machine tool in entire working space effectively.

A new approach to identifying the dynamic behavior of CNC machine tools with respect to different worktable feed speeds

A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting

Environmental temperature has an enormous influence on large machine tools with regards to thermal deformation, which is different from the effect on ordinary-sized machine tools. The thermal deformation has hysteresis effects due to environmental temperature, and the hysteresis time fluctuates with seasonal weather. This paper focused on the hysteresis nonlinear characteristic, analyzing the thermal effect caused by external heat sources. Fourier synthesis, time series analysis and the Newton cooling law were combined to build a time-varying analytical model between environmental temperature and the corresponding thermal error for a large machine tool. A multiple linear regression model based on the least squares principle was used to model the internal heat source effects simultaneously. The two models were united to make up a synthetic thermal error prediction model called the environmental temperature consideration prediction model (ETCP model). A series of experiments were performed using a large gantry type machine tool to verify the accuracy and efficiency of the predicted model under random environments, random times and random machining conditions throughout an entire year. The proposed model showed high robustness and universality, with over 85% thermal error, with up to 0.2 mm was predicted. The mathematical model was easily integrated into the NC system and could greatly reduce the thermal error of large machine tools under ordinary workshop conditions, especially for long-period cycle machining.

Hongqi Liu

Papers

A new method of virtual material hypothesis-based dynamic modeling on fixed joint interface in machine tools

Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

A new approach to identifying the dynamic behavior of CNC machine tools with respect to different worktable feed speeds

A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting

A thermal error model for large machine tools that considers environmental thermal hysteresis effects