Intelligent Process Monitoring Research Articles

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

Intelligent process monitoring of smart polymer composites using large area graphene coated fabric sensor.

Herein, we report the development of an online process monitoring system for vacuum-assisted resin transfer molding (VARTM) process using large area graphene coated in-situ fabric sensor. Besides imparting excellent mechanical properties to the final composites, these sensors provide critical information during the composite processing including detecting defects and evaluating processing parameters. The obtained information can be used to create a digital passport of the manufacturing phase to develop a cost-effective production technique and fabricate high-quality composites. The fabric sensor was produced using a scalable dip-coating process by coating 1-, 3- or 5-layers of thermally reduced graphene oxide (rGO) onto glass fabric surface according to the number of dips of the fabrics into GO solution. The electrical resistances from all electrode pairs were simultaneously and continuously recorded during distinct stages of the VARTM process to determine the relative conductance. During the vacuum cycle, the range of relative conductance increased with the number of coated rGO layers, with the 5-layer rGO-coated sensor showing the highest conductance range of 16.9 %. Additionally, it was observed that the 5-layer coated sensor showed a consistent decrease in conductance during the infusion phase due to the fluid flow pressure dominating the resin electrical conductivity.

Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard

AbstractIncreasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.

Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method.

Monitoring of machining process anomalies in diamond turning of Ti6Al4V alloy using transfer learning-based algorithms

In-process monitoring of anomalies in Ultra Precision Machining (UPM) can improve and ensure product quality and lower manufacturing costs, which is essentially important for industrial-scale ultraprecision manufacturing. UPM is found to be extremely sensitive towards minute instabilities; if such nascent anomalies are not detected can cause irrecoverable defects. However, the classification of the diamond-turning process through conventional monitoring techniques is challenging. This study explores the use of spectrogram-based deep learning to enable real-time, intelligent process monitoring in UPM. The vibrational signals obtained from machining are transformed into log-spectrogram images. These images obtained during machining allow the rendering of more accurate and richer features of signals, as most of the time domain signal obtained in UPM is susceptible to noise and exhibits several non-linearities. The current approach also uses Transfer Learning (TL) to address the feature selection problem. TL is adopted by using the deep learning (DL) models, which have already been developed for classifying different Images. DL pre-trained networks, including VGG19, ResNet50 and Densenet201, are studied for classifying the anomalies. These TL models are applied to the spectrogram images for the classification of normal and abnormal machining in UPM. Among the TL models, the VGG19 model yields the highest classification accuracy at 90%, which demonstrates the potential feasibility of the TL for monitoring process anomalies in UPM.

Graphical design based on digital twin and interaction generation

In the process of product design transformation, some problems arise, such as inefficient management, lack of accuracy in decision analysis of various operation and maintenance events, and the degree of intelligence to be improved, so this paper designs a graphical design based on digital twin and interaction generation. Digital twin is an important tool to achieve deep integration of graphic design, and the physical integration of complex product design and manufacturing information is the most important part of it, which focuses on shop floor operations and product O&M, but there is less research on the application of digital twin technology to complex product design and manufacturing integration. We analyze the problems of equipment monitoring and video monitoring, and systematically explain the feasibility, design ideas, technical routes and key technologies of applying digital twin technology to intelligent production process monitoring.

Consistency Regularization Auto-Encoder Network for Semi-Supervised Process Fault Diagnosis

Industrial processes are developing towards intelligence and complexity, which brings challenges to intelligent process monitoring. An effective fault diagnosis model plays a vital role in ensuring process safety. However, labeling process samples is time-consuming and costly, which make it hard to obtain enough labeled samples to train an effective diagnostic model. This motivates the development of semi-supervised learning which basic idea is to use unlabeled data to help limited labeled data for model training. In this paper, a consistency regularization auto-encoder (CRAE) framework based on encoder-decoder network is proposed to overcome the problem caused by limited labeled samples. The proposed CRAE captured temporal and spatial correlations from both labeled and unlabeled samples to realize fault diagnosis. Firstly, single process sample is processed as sample matrix using proposed data augmented strategy which can help the proposed method to extract more representative features. Secondly, local encoder and global encoder are proposed to extract local and global temporal and spatial features from sample matrices. Next, the local and global features are fused as the input of decoder network, which improve the ability of reconstruction. Finally, the consistency regularization method is introduced into the encoder-decoder framework to push the decision boundary to the low-density area, which make the distinction among different categories more obvious to help the model better achieve the classification task. Experiments on the Tennessee Eastman process show that the proposed method is effective for process fault diagnosis when labeled samples are limited compared to the other semi-supervised algorithms.

Intelligent Process Monitoring of Laser-Induced Graphene Production With Deep Transfer Learning

Three-dimensional graphene has been increasingly used in many applications due to its superior properties. The laser-induced graphene (LIG) technique is an effective way to produce 3-D graphene by combining graphene preparation and patterning into a single step using direct laser writing. However, the variation in process parameters and environment could largely affect the formation and crystallization quality of 3-D graphene. This article develops a vision and deep transfer learning-based processing monitoring system for LIG production. To solve the problem of limited labeled data, novel convolutional de-noising auto-encoder (CDAE)-based unsupervised learning is developed to utilize the available unlabeled images. The learned weights from CDAE are then transferred to a Gaussian convolutional deep belief network (GCDBN) model for further fine-tuning with a very small amount of labeled images. The experimental results show that the proposed method can achieve the state-of-art performance of precise and robust monitoring for the quality of the LIG formation.

A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification

Industrial processes are becoming increasingly large and complex, thus introducing potential safety risks and requiring an effective approach to maintain safe production. Intelligent process monitoring is critical to prevent losses and avoid casualties in modern industry. As the digitalization of process industry deepens, data-driven methods offer an exciting avenue to address the demands for monitoring complex systems. Nevertheless, many of these methods still suffer from low accuracy and slow response. Besides, most black-box models based on deep learning can only predict the existence of faults, but cannot provide further interpretable analysis, which greatly confines their usage in decision-critical scenarios. In this paper, we propose a novel orthogonal self-attentive variational autoencoder (OSAVA) model for process monitoring, consisting of two components, orthogonal attention (OA) and variational self-attentive autoencoder (VSAE). Specifically, OA is utilized to extract the correlations between different variables and the temporal dependency among different timesteps; VSAE is trained to detect faults through a reconstruction-based method, which employs self-attention mechanisms to comprehensively consider information from all timesteps and enhance detection performance. By jointly leveraging these two models, the OSAVA model can effectively perform fault detection and identification tasks simultaneously and deliver interpretable results. Finally, extensive evaluation on the Tennessee Eastman process (TEP) demonstrates that the proposed OSAVA-based fault detection and identification method shows promising fault detection rate as well as low detection delay and can provide interpretable identification of the abnormal variables, compared with representative statistical methods and state-of-the-art deep learning methods.

Manufacturing process monitoring using time-frequency representation and transfer learning of deep neural networks

On-line process monitoring increases product quality, improves process stability, and lowers costs in manufacturing. This paper presents a study of using time-frequency representation and deep neural networks to enable real-time, intelligent manufacturing process monitoring. Acoustic emission (AE) signals are obtained during machine turning operations, and transformed into a time-frequency (TF) representation (image) format. Deep neural networks are then applied to the images for process classification according to the operation's spindle speed, feed rate, and depth of cut. The signals are nonstationary with frequency content varying in time. Four commonly used time-frequency analysis methods are discussed and compared, such as the short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert-Huang transform (HHT), and Wigner–Ville distribution (WVD). The study finds that the multi-resolution capability of the CWT technique allows it to render more accurate and richer details of the signals. Classification of machine-turning-operation processes through conventional monitoring techniques is challenging. First, there are not theories directly relating the signal characteristics to the physical process. Also, important signal components associated with the physics of the process are masked by heavy noise from the lathe. These lead to difficulties in selecting features to be extracted, and obtaining reliable data of these features. These obstacles are overcome by deep neural networks that are capable of learning and extracting meaningful features in an automatous fashion. A transfer learning approach is adopted in this study by using high-performance, representative deep neural networks previously developed for image classification and recognition, including ShuffleNet, GoogLeNet, ResNet18, ResNet50, VGG16, and DenseNet201. They are applied to the TF images obtained during the machine turning operations for process classification. The VGG16 network yields the highest classification accuracy at 92.67% when applied to processes of 12 classes, which demonstrates the potential and feasibility of the proposed method for satisfactory monitoring performance. Lastly, it is shown that the classification accuracy can be improved by using shallower networks modified from the VGG-16 network to mitigate the overfitting issue. A classification accuracy of 95.58% is achieved by removing two convolutional layers.

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

AbstractIntelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self‐learning parameter optimization method named iterative gradient‐approximation adaptive optimization (IGAO) method, which adopts the batch‐to‐batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation. The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.

Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems

Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.

Deep Learning-Based Intelligent Process Monitoring of Directed Energy Deposition in Additive Manufacturing with Thermal Images

Additive manufacturing (AM) techniques have been successfully developed in the past years with the great potential of overcoming the existing obstacles in traditional manufacturing. In order to improve the quality of the manufactured parts and reduce costs, it is important to timely and accurately monitor the AM process during manufacturing. However, it remains a challenging task due to the high complexity of the AM process and the difficulty in processing the condition monitoring data. This paper proposes a deep learning-based process monitoring method for directed energy deposition in AM. The thermal images collected during manufacturing are used to identify the process condition, and a deep convolutional neural network model is proposed to build an end-to-end condition monitoring framework. Experiments on a real directed energy deposition dataset in AM are carried out for validation. The results suggest the proposed method offers a promising approach in process monitoring based on the industrial images. Furthermore, little prior knowledge on signal processing and AM is required, that largely facilitates the potential applications in the real industrial scenarios.

Intelligent data-driven monitoring of high dimensional multistage manufacturing processes

Recent advances in cyber-physical systems and the Internet of things (IoT) have enabled the possible development of smart production systems. However, the complexity of such a system has posed significant challenges for traditional quality engineering methods, especially in monitoring and diagnosis of system performance. The traditional practices for monitoring or controlling multistage systems either treat each stage as an individual entity or model all stages as a whole. The formal approach mainly focuses on the most critical stages while ignores information from the other stages. In contrast, the latter approach attempts to build one model to account for all stages. In a complex production system, this latter approach is impractical, if not impossible. This research provides a control strategy by proposing an intelligent process monitoring system for high dimensional multistage processes using predictive models built from historical data. A repository dataset is used to demonstrate the implementation of the proposed framework.

The practical exploitation of tacit machine tool intelligence

Manufacturing equipment embraces an increasing measure of tacit intelligence, in both capacity and value. However, this intelligence is yet to be exploited effectively. This is due to both the costs and limitations of developed approaches and a deficient understanding of data value and data origin. This work investigates the principal limitations of typical machine tool data and encourages consideration of such inherent limitations in order to improve potential monitoring strategies. This work presents a novel approach to the acquisition and processing of machine tool cutting data. The approach considers the condition of the monitored system and the deterioration of cutting tool performance. The management of the cutting process by the machine tool controller forms the basis of the approach, and hence, makes use of the tacit intelligence that is deployed in such a task. By using available machine tool controller signals, the impact on day-to-day machining operations is minimised while avoiding the need to retrofit equipment or sensors. The potential of the approach in the contexts of the emerging internet of things and intelligent process management and monitoring is considered. The efficacy of the approach is evaluated by correlating the actively derived measure of process variation with an offline measurement of product form. The potential is then underlined through a series of experiments for which the derived variation is assessed as a direct measure of the cutting tool health. The proposed system is identified as both a viable alternative and synergistic addition to current approaches that mainly consider the form and features of the manufactured component.

A Multiscale Feature Learning Scheme Based on Deep Learning for Industrial Process Monitoring and Fault Diagnosis

Considering that industrial data exhibit nonlinearity, high dimensionality and inherent multiscale characteristics, this paper proposes an intelligent industrial process monitoring and fault diagnosis method based on the discrete wavelet transform and deep learning. First, the discrete wavelet transform is used to present the multiscale representation of the raw data. Second, a multiscale convolutional neural network is used to extract the features at each scale, and then the extracted multiscale features are fused by the long short-term memory network to further reduce useless information and retain useful information. Finally, softmax classification is performed. The proposed method has two advantages: 1) the hierarchical learning structure with multiple pairs of convolutional and pooling layers can effectively learn nonlinear, high-dimensional fault features; and 2) the multiscale feature learning scheme can capture complementary diagnosis information at different scales. Detailed comparative studies between the proposed method and conventional methods have been carried out through the Tennessee Eastman benchmark process and the p-xylene oxidation reaction process.

Quality Oriented Self-Optimizing Control in Smart Machining Systems

In recent years, due to the hard competitiveness through globalization most manufacturing companies have focused on manufacturing higher value added products with low production costs. So, it is inevitable to apply the advanced technology to carry out it in effective and efficient way. Monitoring and control of machining processes are becoming increasingly important for keeping a consistent quality of machined parts. The paper presents in-process sensing and control techniques with applying information science for the next generation of quality control that eliminates the need for expensive post-process inspection. In this research, a machining process of machine tool is considered in terms of a quality oriented control. The self-optimizing control is proposed for enabling intelligent process monitoring and control which can detect the cutting conditions and status of machining operations. To prove the proposed self-optimizing control system, self-monitoring and self-adjusting, the machining process related to the tool condition such as tool wear was considered in this paper. The most crucial and determining factor to successful maximization of the manufacturing processes in any typical metal cutting process is tool wear. The evolutionary algorithm was developed to diagnose the tool wear. If the amount of tool wear is out of the allowed limit, the new cutting parameters should be generated and adjusted autonomously according to affecting the quality of the machined part through the tool wear.

A Discussion of the Prognostics and Health Management Aspects of Embedded Condition Monitoring System

This paper presents a review of embedded condition monitoring research carried out at Cardiff University. A variety of application areas are described, along with a discussion of the evolution of the hardware platforms used. The current operating philosophies of the Intelligent Process Monitoring and Management (IPMM) research group and the deployed hierarchical and distributed architectures are described. The paper sets out to discuss the on-going trend towards such monitoring systems needing to provide more than fault detection and diagnostic capabilities. System requirements such as tracking operational settings, performance and efficiency measures and providing limp-home facilities are seen to be consistent with prognostics and health management ideals. The paper concludes with a discussion of new and future developments and applications.

A partial Granger causality based method for analysis of parameter interactions in bioreactors

In this article, we utilize the concept of partial Granger causality to study the penicillin production process under several operating conditions. We propose a graph-theoretic template (causal network) based method for intelligent process monitoring. We validate our results with the aid of existing knowledge and available literature. The proposed method is quite general and can be extended to analyze several physical, chemical or biological systems.

Intelligent Asset Performance Management of a Uranium Solvent Extraction Circuit

Having recognized the need for an advanced condition monitoring system which could perform intelligent process monitoring and root-cause analysis, BHP Billiton (Base Metals) in association with Barrick Gold and SGS Advanced Systems formed a consortium in 2004 in order to develop a system known as AHEAD (Asset Health Effectiveness and Diagnostics). This system has been designed to support the near real-time monitoring of key business and asset performance indicators (KPI's) and to automatically detect and diagnose deviations from desired performance on the equipment, control and process level of operations. This paper describes the implementation of a full-scale roll-out of the AHEAD toolkit on the Uranium Solvent Extraction plant at BHP Billiton's Olympic Dam operation.