Machine Tool Condition Monitoring Research Articles

CNN-Based Machine Tool Monitoring with STFT Image Analysis

Abstract: In the realm of machine tool operation, effective condition monitoring holds predominant importance to ensure operational reliability and safety. Leveraging deep learning methodologies, particularly convolutional neural networks (CNN), for defect identification has gained significant attention. However, inherent challenges persist, including the extraction of salient features and potential information loss while extracting the features from raw vibration signals. In response, this study proposes an intelligent approach for condition monitoring in machine tools, integrating short-time Fourier transform and convolutional neural networks (STFT-CNN). The process entails using STFT to convert one-dimensional vibration signals into time-frequency pictures, which are then fed into the STFT-CNN model to acquire and identify fault features. Furthermore, the study explores optimizing STFT parameters, such as window type, window width, and translation overlap width, across various typical window functions to improve the effectiveness of the transformation process. Within the STFT-CNN model, the utilization of stacked double convolutional layers aims to augment the model's nonlinear expression capacity, thereby facilitating robust fault diagnosis capabilities in machine tool condition monitoring applications

Classification of the machine state in turning processes by using the acoustic emission

Processing digital information stands as a crucial foundation of Industry 4.0, facilitating a spectrum of activities from monitoring processes to their understanding and optimization. The application of data processing techniques, including feature extraction and classification, coupled with the identification of the most suitable features for specific purposes, continues to pose a significant challenge in the manufacturing sector. This research investigates the suitability of classification methods for machine and tool state classification by employing acoustic emission (AE) sensors during the dry turning of Ti6Al4V. Features such as quantiles, Fourier coefficients, and mel-frequency cepstral coefficients are extracted from the AE signals to facilitate classification. From this features the 20 best are selected for the classification to reduce the dimension of the feature space and redundancy. Algorithms including decision tree, k-nearest-neighbors (KNN), and quadratic discriminant analysis (QDA) are tested for the classification of machine states. Of these, QDA exhibits the highest accuracy at 98.6 %. Nonetheless, an examination of the confusion matrix reveals that certain classes, influenced by imbalanced training data, exhibit a lower prediction accuracy. In summary, the study affirms the potential of AE sensors for machine state recognition and tool condition monitoring. Although QDA emerges as the most acurate classifier, there remains an avenue for refinement, particularly in training data optimization and decision-making processes, to augment accuracy.

Digital Twin Framework for Lathe Tool Condition Monitoring in Machining of Aluminium 5052


 
 
 Digital Twin (DT) is a virtual representation of a product system that exhibits the properties and analyzes the system’s functions. The significant impact of DT extends to several fields, which increases productivity and reduces wastage. This article focuses on developing a Digital twin model of a Lathe machine for Tool Condition Monitoring (TCM). DT implementation in industries is challenging due to simulating online cutting forces and wear. Even though several pieces of research have been carried out in the prediction of tool conditions using machine learning, Artificial Neural network models, only a few pieces of research have been made in digital twins for TCM. This article provides the technique for implementing the DT model of a lathe tool. The feasibility of the DT Model framework is verified by a case study of the turning process with a CNC Lathe machine while machining of Aluminium 5052 workpiece using Titanium Nitride coated tool inserts. The sensor’s data are acquired and fed to the microcontroller for real-time data acquisition. The real-time dataset is processed in the DT model for monitoring and predicting the tool conditions. The tool wear classification using the DT model is achieved. Developing the Digital Twin model in machining increases productivity and assists in predictive maintenance.
 
 

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

Deep transfer residual variational autoencoder with multi-sensors fusion for tool condition monitoring in impeller machining

The data-driven deep learning models show incomparable superiority in tool wear monitoring. However, facing the monitoring environment of real-time changes in tool model, workpiece size, and cutting parameters in the machining of centrifugal compressor impeller, the flexibility of the model between different tools is rarely considered. In this manuscript, a novel deep transfer learning model based on residual variation autoencoder (Tresvae) with multi-sensors fusion signals is proposed. The sound pressure signals, acceleration signals, and spindle motor current signals are collected and confused into images as monitoring samples. Moreover, the residual network is used to optimize the encoding part of the variational autoencoder network while the decoding part remain unchanged. Then the parameter transfer strategy is adopted to realize the model transfer diagnosis between different tools. Experiments show that the proposed model achieves superior identification results than other methods on the same types and different types tools monitoring in impeller machining. Among them, the maximum recognition accuracy of the same type of tools is 97.25%, and that of different types of tools is 92%.

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

Cutting Force Embedded Manifold Learning for Condition Monitoring of Vertical Machining Center

Cutting condition monitoring is essential for managing the operation of machine tools in manufacturing. The t-distributed stochastic neighbor embedding (t-SNE) method has been adopted to learn the underlying relationship between cutting parameters in a manifold. This paper focuses on cutting force embedding in the t-SNE, termed Ft-SNE, for applications in feature selection to achieve better performance for condition monitoring of machine tools. In the method, the measured vibration signals are used to build the fundamental feature space in the high-dimensional space with cutting force signals being introduced to improve its separability. Further analysis is performed to optimize the classification performance of the Ft-SNE by selecting the appropriate weighting coefficient by minimizing the Kullback-Leibler divergence. The experiments were implemented on a 3-axis vertical machining center. Vibration signals and cutting force signals under different cutting conditions were collected from the vertical machining center. The visualization results of the Ft-SNE and t-SNE were analyzed in low-dimensional space. By introducing the tangential cutting force, the classification performance is improved with classification evaluation indicators being around 98%, with an improvement of over 41% compared to the traditional t-SNE for cutting condition classification in machining process.

Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring

Data-based methods are capable to monitor machine components. Approaches for semi-supervised anomaly detection are trained using sensor data that describe the normal state of machine components. Thus, such approaches are interesting for industrial practice, since sensor data do not have to be labeled in a time-consuming and costly way. In this work, an ensemble approach for semi-supervised anomaly detection is used to detect anomalies. It is shown that the ensemble approach is suitable for condition monitoring of ball screws. For the evaluation of the approach, a data set of a regular test cycle of a ball screw from automotive industry is used.

Application of Multisensor Information Fusion Technology in the Measurement of Dynamic Machining Errors of Computer Numerical Control (CNC) Machine Tools

A CNC machine tool is process control equipment integrating machine, electricity, and liquid, which makes its fault diagnosis complex and special due to its own advanced, complex, and intelligent characteristics. Traditional diagnostic methods rely on the engineering experience of technical personnel, which incorporates human subjective factors, and can only perform qualitative analysis, resulting in low diagnostic efficiency. And through a single sensor to detect and diagnose the machine tool, the accuracy and credibility of the decision are low, and the system is also weak against interference. In this paper, we first summarize the composition and working principle of CNC machine tools and analyze the working condition signals generated by CNC machine tools and the sensors that collect the signals and decide to use a multisensor multisignal fusion-based approach to monitor the machine tool status. It is possible to obtain more effective and valuable information from the observed information through multiple sensors so that the goal of fusion can be achieved. In this paper, a multisensor fusion technique based on wavelet transform and neural network fusion is applied to a machine tool condition monitoring system. The theoretical basis of wavelet analysis and neural network is introduced, and the composition of the condition monitoring system and the process of applying multisensor fusion technology based on wavelet analysis and neural network in the condition monitoring system are given. A complete software and hardware system for online monitoring of CNC machine tools is established. In order to improve the accuracy of the mathematical model, the use of a neural network to fit the nonlinear data and the use of coarse set theory to simplify the relevant data can effectively solve the accurate establishment of the mathematical model in the error compensation method. The thermal error compensation method for CNC machine tools is proposed based on rough set theory, ant colony algorithm, and neural network. This paper first investigates the current development of error compensation technology for CNC machining centers, analyzes the various error sources of CNC machine tools, and finds out the influencing factors affecting the errors of CNC machine tools.

Error motion trajectory-driven diagnostics of kinematic and non-kinematic machine tool faults

Error motion trajectory data are routinely collected on multi-axis machine tools to assess their operational state. There is a wealth of literature devoted to advances in modelling, identification and correction using such data, as well as the collection and processing of alternative data streams for the purpose of machine tool condition monitoring. Until recently, there has been minimal focus on combining these two related fields. This paper presents a general approach to identifying both kinematic and non-kinematic faults in error motion trajectory data, by framing the issue as a generic pattern recognition problem. Because of the typically-sparse nature of datasets in this domain – due to their infrequent, offline collection procedures – the foundation of the approach involves training on a purely simulated dataset, which defines the theoretical fault-states observable in the trajectories. Ensemble methods are investigated and shown to improve the generalisation ability when predicting on experimental data. Machine tools often have unique ‘signatures’ which can significantly-affect their error motion trajectories, which are largely repeatable, but specific to the individual machine. As such, experimentally-obtained data will not necessarily be easily defined in a theoretical simulation. A transfer learning approach is introduced to incorporate experimentally-obtained error motion trajectories into classifiers which were trained primarily on a simulation domain. The approach was shown to significantly improve experimental test set performance, whilst also maintaining all theoretical information learned in the initial, simulation-only training phase. The ultimate approach represents a viable and powerful automated classifier for error motion trajectory data, which can encode theoretical fault-states with efficacy whilst also remain adaptable to machine-specific signatures.

Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review

Machine vision based condition monitoring and fault diagnosis of machine tools (MVCMFD-MTs) is a vital technique of condition-based maintenance (CBM) in both metal removal manufacturing and metal additive fabrication. In these domains, many methods utilize information from imaging matrices of machined surfaces to extract sensitive features and obtain potential degradation tendencies. Over recent years, no comprehensive review covers the whole monitoring or diagnostic procedures. To fill this gap, this paper systematically summarizes MVCMFD-MTs, which aims to provide researchers and engineers with a theoretical basis and roadmap to further study or build MVCMFD-MTs using information from the machined surface texture. Firstly, two data acquisition systems and several institutional public datasets are revisited. Secondly, the methodologies are illustrated in two aspects, feature descriptors and diagnostic decision-making. Thirdly, an intuitive illustration on applications is provided from the perspective of surface quality monitoring (i.e., roughness evaluation, surface defect inspection) and indirect tool condition monitoring (i.e., tool wear monitoring, chatter identification). Finally, this paper discusses current challenges and potential research directions in nowadays intelligent manufacturing.

Chip segmentation frequency based strategy for tool condition monitoring during turning of Ti-6Al-4V

Tool condition monitoring in machining reduces downtimes, maximizes productivity rate and improves the quality of the end-product. However, it still poses a challenge due to the complex non-stationary character of the tool wear and the several uncertainties coming from the machining processes. Recent studies provide new strategies for indirect tool monitoring. Unfortunately due to the unbalance between big amounts of data, low accuracy and high complexity they are not feasible in an industrial environment. The present research work proposes a strategy for tool condition monitoring during turning of Ti-6Al-4V using acoustic emission signals and the chip segmentation frequency as measurement variable. Three different approaches for wear estimation using different AE-data processing methods are presented. Through their combination, a strategy for qualitative and quantitative tool wear monitoring is proposed.

Chasing the Cut: A Measurement Approach for Machine Tool Condition Monitoring

Often, the condition of a machine tool is detected indirectly in the reduced quality of manufactured parts upon visual inspection. Reliable and efficient machine tool condition monitoring is indispensable for manufacturing. Furthermore, issues affecting machine tools are closely related to pathologies associated with many other industrial electromechanical systems. An instrumentation and measurement solution for tool condition monitoring is presented in this article. A signal processing algorithm and instrumentation hardware are proposed to avoid intrusive sensor installations or modifications of the machine under test. The cyclostationary properties of machine vibration signals drive fault-detection approaches in the proposed sensing hardware and signal processing chain. A sample of end mills from an industrial facility is used to validate the tool condition monitoring system.

Review of tool condition monitoring in machining and opportunities for deep learning

Tool condition monitoring and machine tool diagnostics are performed using advanced sensors and computational intelligence to predict and avoid adverse conditions for cutting tools and machinery. Undesirable conditions during machining cause chatter, tool wear, and tool breakage, directly affecting the tool life and consequently the surface quality, dimensional accuracy of the machined parts, and tool costs. Tool condition monitoring is, therefore, extremely important for manufacturing efficiency and economics. Acoustic emission, vibration, power, and temperature sensors monitor the stability and efficiency of the machining process, collecting large amounts of data to detect tool wear, breakage, and chatter. Studies on monitoring the vibrations and acoustic emissions from machine tools have provided information and data regarding the detection of undesirable conditions. Herein, studies on tool condition monitoring are reviewed and classified. As Industry 4.0 penetrates all manufacturing sectors, the amount of manufacturing data generated has reached the level of big data, and classical artificial intelligence analyses are no longer adequate. Nevertheless, recent advances in deep learning methods have achieved revolutionary success in numerous industries. Deep multi-layer perceptron (DMLP), long-short-term memory (LSTM), convolutional neural network (CNN), and deep reinforcement learning (DRL) are among the most preferred methods of deep learning in recent years. As data size increases, these methods have shown promising performance improvement in prediction and learning, compared to classical artificial intelligence methods. This paper summarizes tool condition monitoring first, then presents the underlying theory of some of the most recent deep learning methods, and finally, attempts to identify new opportunities in tool condition monitoring, toward the realization of Industry 4.0.

Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering

The combination of artificial intelligence with data, computing power, and new algorithms can provide important tools for solving engineering problems, such as machine-tool condition monitoring. However, many of these problems require algorithms that can perform in highly dynamic scenarios where the data streams have extremely high sampling rates from different types of variables. The unsupervised learning algorithm based on Gaussian mixture models called Gaussian-based dynamic probabilistic clustering (GDPC) is one of these tools. However, this algorithm may have major limitations if a large amount of concept drifts associated with transients occurs within the data stream. GDPC becomes unstable under these conditions, so we propose a new algorithm called GDPC+ to increase its robustness. GDPC+ represents an important improvement because we introduce: (a) automatic selection of the number of mixture components based on the Bayesian information criterion (BIC), and (b) concept drift transition stabilization based on Cauchy–Schwarz divergence integrated with the Dickey–Fuller test. Thus, GDPC+ can perform better in highly dynamic scenarios than GDPC in terms of the number of false positives. The behavior of GDPC+ was investigated using random synthetic data streams and in a real data stream-based condition monitoring obtained from a machine-tool that produces engine crankshafts at high speed. We found that the initial temporal window size can be used to adapt the algorithm to different analytical requirements. The clustering results were also investigated by induction of the rules generated by the repeated incremental pruning to produce error reduction (RIPPER) algorithm in order to provide insights from the underlying monitored process and its associated concept drifts.

Ultra low power wireless multi-sensor platform dedicated to machine tool condition monitoring

The H2020 PreCoM project aims at developing and deploying a predictive cognitive maintenance decision-support system for production equipment such as machine tools. The PreCoM system intends to identify and localize damage, predict its evolution, assess remaining lifetime, and increase in-service efficiency of machines by conducting preventive maintenance actions. In this PreCoM system, additional sensors are needed to gather measurements close to critical parts/tools in order to improve failure detection.Commercial wireless sensor nodes are too restrictive regarding requirements for condition monitoring on industrial machine tools (50 g acceleration, 50 m long wireless range, several month lifetime, presence of cooling fluid and hot metal chips projection). Therefore, a dedicated wireless multi-sensor platform has been designed and developed in the frame of this project in order to provide additional measurements on highly moving parts, such as spindle head.The PreCoM wireless sensor nodes are integrating a 50 g/10 kHz - 3 axes accelerometer and a temperature sensor. They have been designed to be standalone devices with lifetime of around 1 year, and to fit all the machine tool environment requirements. The wireless sensor nodes are based on ultra-low power electronic devices and communication protocols to minimize their power consumption during all phases: sensing, communication and sleeping mode.Those sensor nodes and their associated wireless platform have been successfully developed and tested in representative environment in laboratory, and then deployed on pilot machine tools within the PreCoM maintenance system.

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

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.

Research on fault early warning and the diagnosis of machine tools based on energy fault tree analysis

At present, the status of the monitoring of computerized numerical control machine tools is still in the initial stage, and there is no uniform standard for fault early warning and diagnosis of machine tools. A standardized and comprehensive machine tool condition monitoring method based on energy fault tree analysis is proposed. The power, current and temperature inside the numerical control system and the signal of the external vibration sensor are collectively referred to as energy. A new energy fault tree is used to identify the location of the fault. At the same time, the client of computerized numerical control system internal information collection and analysis is developed. The characteristic frequency of each key component is analysed by theoretical mechanics, and an overall monitoring method is proposed to improve the monitoring efficiency. Finally, the reliability of the monitoring method is verified by experiments. The surface roughness of the parts is reduced by approximately 50%, which lays the foundation for the application of the engineering.

Manufacturing apps and the Dynamic House of Quality: Towards an industrial revolution

Modern manufacturing is becoming more powerful and flexible due to the decreasing cost of smart sensors and data processing platforms. Modularized functions related to machine tool condition monitoring, signal processing and adaptive control can be packaged into standalone manufacturing apps for a better understanding of machine operations. Based on the Service Oriented Architecture (SOA), manufacturing apps can be structured to work either independently or as a group for performing more complicated tasks. The Dynamic House of Quality (HoQ) provides a platform that companies, app users and app writers can benefit from in understanding what apps customers need the most, and what apps are more likely to be bundled in app groups. Potential applications of the Dynamic HoQ for manufacturing apps are discussed in this paper.

Acoustic emission testing of the machining performance of SiC grinding wheel shaped Al2O3 ceramics

Abstract Engineering ceramic materials are one among the key components of technology due to their outstanding physico-mechanical properties like hardness, toughness, wear resistance and chemical stability at elevated temperatures. On the other hand, the stated unique properties of ceramics exhibit complexity during machining. Under such circumstances, online monitoring of cutting tools plays a major role. The present study involves acoustic emission (AE) testing technique for condition monitoring of machine tools. Various grades of alumina ceramics had been machined using SiC grinding wheel. Grinding process parameters like depth of cut and feed rate were varied. Introducing AE technique, the following parameters like energy, amplitude of grinding wheel wear and surface roughness of alumina ceramics were analyzed.