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He J, Yuan L, Lei H, Wang K, Weng Y, Gao H. A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction. SENSORS (BASEL, SWITZERLAND) 2024; 24:1129. [PMID: 38400286 PMCID: PMC10892721 DOI: 10.3390/s24041129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple-feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson's Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.
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Affiliation(s)
- Jigang He
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
| | - Luyao Yuan
- School of Mathematics, Sichuan University, Chengdu 610065, China; (L.Y.); (H.L.); (K.W.)
| | - Haotian Lei
- School of Mathematics, Sichuan University, Chengdu 610065, China; (L.Y.); (H.L.); (K.W.)
| | - Kaixuan Wang
- School of Mathematics, Sichuan University, Chengdu 610065, China; (L.Y.); (H.L.); (K.W.)
| | - Yang Weng
- School of Mathematics, Sichuan University, Chengdu 610065, China; (L.Y.); (H.L.); (K.W.)
| | - Hongli Gao
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China;
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2
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Apostolou G, Ntemi M, Paraschos S, Gialampoukidis I, Rizzi A, Vrochidis S, Kompatsiaris I. Novel Framework for Quality Control in Vibration Monitoring of CNC Machining. SENSORS (BASEL, SWITZERLAND) 2024; 24:307. [PMID: 38203169 PMCID: PMC10781387 DOI: 10.3390/s24010307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/20/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024]
Abstract
Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.
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Affiliation(s)
- Georgia Apostolou
- Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece; (M.N.); (S.P.); (I.G.); (S.V.); (I.K.)
| | - Myrsini Ntemi
- Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece; (M.N.); (S.P.); (I.G.); (S.V.); (I.K.)
| | - Spyridon Paraschos
- Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece; (M.N.); (S.P.); (I.G.); (S.V.); (I.K.)
| | - Ilias Gialampoukidis
- Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece; (M.N.); (S.P.); (I.G.); (S.V.); (I.K.)
| | | | - Stefanos Vrochidis
- Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece; (M.N.); (S.P.); (I.G.); (S.V.); (I.K.)
| | - Ioannis Kompatsiaris
- Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece; (M.N.); (S.P.); (I.G.); (S.V.); (I.K.)
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3
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Sun H, Ding H, Deng C, Xiong K. Efficient Prediction of Stability Boundaries in Milling Considering the Variation of Tool Features and Workpiece Materials. SENSORS (BASEL, SWITZERLAND) 2023; 23:8954. [PMID: 37960653 PMCID: PMC10647373 DOI: 10.3390/s23218954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023]
Abstract
Theoretical stability analysis is a significant approach to predicting chatter-free machining parameters. Accurate milling stability predictions highly depend on the dynamic properties of the process system. Therefore, variations in tool and workpiece attributes will require repeated and time-consuming experiments or simulations to update the tool tip dynamics and cutting force coefficients. Considering this problem, this paper proposes a transfer learning framework to efficiently predict the milling stabilities for different tool-workpiece assemblies through reducing the experiments or simulations. First, a source tool is selected to obtain the tool tip frequency response functions (FRFs) under different overhang lengths through impact tests and milling experiments on different workpiece materials conducted to identify the related cutting force coefficients. Then, theoretical milling stability analyses are developed to obtain sufficient source data to pre-train a multi-layer perceptron (MLP) for predicting the limiting axial cutting depth (aplim). For a new tool, the number of overhang lengths and workpiece materials are reduced to design and perform fewer experiments. Then, insufficient stability limits are predicted and further utilized to fine-tune the pre-trained MLP. Finally, a new regression model to predict the aplim values is obtained for target tool-workpiece assemblies. A detailed case study is developed on different tool-workpiece assemblies, and the experimental results validate that the proposed approach requires fewer training samples for obtaining an acceptable prediction accuracy compared with other previously proposed methods.
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Affiliation(s)
- Huijuan Sun
- School of Mechanical Engineering and Automation, Chongqing Industry Polytechnic College, Chongqing 401120, China;
| | - Huiling Ding
- School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; (H.D.); (K.X.)
| | - Congying Deng
- School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; (H.D.); (K.X.)
| | - Kaixiang Xiong
- School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; (H.D.); (K.X.)
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4
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Jang WK, Kim DW, Seo YH, Kim BH. Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:1208. [PMID: 36772248 PMCID: PMC9921934 DOI: 10.3390/s23031208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of -7.5°, 0.0°, and +7.5° to the vertical plane, and three cameras are placed at 45° intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm × 100 mm × 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5~35.21 μm. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization.
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Affiliation(s)
- Woong-Ki Jang
- Department of Smart Health Science and Technology, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
| | - Dong-Wook Kim
- Electric Power Train R&D Department, Korea Automotive Technology Institute, 303 Pungse-ro, Pungse-myeon, Dongnam-gu, Cheonan 31214, Republic of Korea
| | - Young-Ho Seo
- Department of Smart Health Science and Technology, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
- Department of Mechatronics Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
| | - Byeong-Hee Kim
- Department of Smart Health Science and Technology, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
- Department of Mechatronics Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
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5
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Yang X, Yuan R, Lv Y, Li L, Song H. A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:8343. [PMID: 36366041 PMCID: PMC9657287 DOI: 10.3390/s22218343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/24/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the dynamical information of tool wear conditions. This paper proposes a novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network (1D CNN). Firstly, multivariate variational mode decomposition (MVMD) is used to process the multivariate cutting force signals. The multivariate band-limited intrinsic mode functions (BLIMFs) are obtained, which contain a large number of nonlinear and nonstationary tool wear characteristics. Afterwards, the proposed modified multiscale permutation entropy (MMPE) is used to measure the complexity of multivariate BLIMFs. The entropy values on multiple scales are calculated as condition indicators in tool wear condition monitoring. Finally, the one-dimensional feature vectors are constructed and employed as the input of 1D CNN to achieve accurate and stable tool wear condition monitoring. The results of the research in this paper demonstrate that the proposed approach has broad prospects in tool wear condition monitoring.
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Affiliation(s)
- Xu Yang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Rui Yuan
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Yong Lv
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Li Li
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Hao Song
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
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6
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Sio-Sever A, Lopez JM, Asensio-Rivera C, Vizan-Idoipe A, de Arcas G. Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling. SENSORS (BASEL, SWITZERLAND) 2022; 22:3807. [PMID: 35632214 PMCID: PMC9146282 DOI: 10.3390/s22103807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.
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Affiliation(s)
- Andrés Sio-Sever
- Grupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Juan Manuel Lopez
- Grupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Telemática y Electrónica, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
| | - César Asensio-Rivera
- Grupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Teoria de la Señal y Comunicaciones, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
| | - Antonio Vizan-Idoipe
- Departamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, 28006 Madrid, Spain;
| | - Guillermo de Arcas
- Grupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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7
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Teaching machines to optimizing machining parameters: using independent fuzzy logic controller and image data. SN APPLIED SCIENCES 2022; 4:107. [PMID: 35330957 PMCID: PMC8927008 DOI: 10.1007/s42452-022-04987-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 02/21/2022] [Indexed: 11/02/2022] Open
Abstract
AbstractOptimization of machining parameters like cutting speed, feed, and depth of cut is one of the extensively studied fields in the past two decades. While researchers agree optimization of these parameters is essential, there is no conscience as to what the objective of the optimization should be. The studies consider production cost, production time, surface finish, among others, as the objective of parameter optimization, but there are very few studies that consider the manufacturer prescribed tool life as the criteria for parament optimization. Among the methods that do consider tool life as an optimization objective, very few are closed-loop systems and these systems are facing challenges to generalizing when the application changes or the machining material changes or the tool geometry changes. Considering this, a novel image feedback using a convolution neural network-based method combined with principles of fuzzy logic is used to optimize machining parameters. Since the system is based on online feedback from the images of the inserts, it can be used for different materials, and the system is invariant to the different tool geometries and grades as the decisions are based on the wear mechanisms detected. The hybrid system is validated through experimentation for the turning application, but the methodology can be easily adapted for other machining applications.
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8
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Park B, Lee Y, Yeo M, Lee H, Joo C, Lee C. Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process. SENSORS 2022; 22:s22051975. [PMID: 35271122 PMCID: PMC8914842 DOI: 10.3390/s22051975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/27/2022] [Accepted: 03/01/2022] [Indexed: 01/25/2023]
Abstract
Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes.
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Affiliation(s)
- Byeonghui Park
- Department of Mechanical Design and Production Engineering, Konkuk University, Seoul 05030, Korea; (B.P.); (Y.L.); (M.Y.); (H.L.)
| | - Yoonjae Lee
- Department of Mechanical Design and Production Engineering, Konkuk University, Seoul 05030, Korea; (B.P.); (Y.L.); (M.Y.); (H.L.)
| | - Myeonghwan Yeo
- Department of Mechanical Design and Production Engineering, Konkuk University, Seoul 05030, Korea; (B.P.); (Y.L.); (M.Y.); (H.L.)
| | - Haemi Lee
- Department of Mechanical Design and Production Engineering, Konkuk University, Seoul 05030, Korea; (B.P.); (Y.L.); (M.Y.); (H.L.)
| | - Changbeom Joo
- Department of Mechanical Engineering, Stevens Institute of Technology, 1 Castle Pointe Terrace, Hoboken, NJ 07030, USA;
| | - Changwoo Lee
- Department of Mechanical and Aerospace Engineering, Konkuk University, Seoul 05030, Korea
- Correspondence: ; Tel.: +82-2-450-3570; Fax: +82-2-454-0428
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9
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Intelligent vision based wear forecasting on surfaces of machine tool elements. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-021-04839-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Abstract
To realize autonomous production machines it is necessary that machines are able to automatically and autonomously predict their condition. Although many classical as well as Deep Learning based approaches have shown the ability to classify faults, so far there are no approaches that go beyond the basic detection of faults. A complete, image based predictive maintenance approach for machine tool components has to the best of our knowledge not been investigated so far. In this paper it is shown how defects on a Ball Screw Drive (BSD) can be automatically detected by using a machine learning based detection module, quantified by using an intelligent defect quantification module and finally forecasted by a prognosis module in a combined approach. To optimize the presented method, it is shown how existing domain knowledge can be formalized in an expert system and combined with the predictions of the machine learning model to aid quality of the prediction system. This enables the practitioner to forecast the severity of failures on BSD and prevent machine breakdowns. The work is intended to set new accents for the development of practical predictive maintenance systems and bridging the fields of machine learning and production engineering. The code is available under: https://github.com/2Obe/Pitting_Pred_Maintenance.
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10
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Remaining Useful Life Prediction from 3D Scan Data with Genetically Optimized Convolutional Neural Networks. SENSORS 2021; 21:s21206772. [PMID: 34695985 PMCID: PMC8541140 DOI: 10.3390/s21206772] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/01/2021] [Accepted: 10/07/2021] [Indexed: 11/29/2022]
Abstract
In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, convolutional neural network-based deep neural network techniques are investigated for the remaining useful life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pretrained models, using a classic machine learning approach, i.e., support vector regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE = 0.058) to that of transfer learning, which, instead, remains at a lower or slightly higher level (MAPE = 0.416) than support vector regression (MAPE = 0.857).
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11
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Brili N, Ficko M, Klančnik S. Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography. SENSORS 2021; 21:s21196687. [PMID: 34641006 PMCID: PMC8512854 DOI: 10.3390/s21196687] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 11/21/2022]
Abstract
In turning, the wear control of a cutting tool benefits product quality enhancement, tool-related costs‘ optimisation, and assists in avoiding undesired events. In small series and individual production, the machine operator is the one who determines when to change a cutting tool, based upon their experience. Bad decisions can often lead to greater costs, production downtime, and scrap. In this paper, a Tool Condition Monitoring (TCM) system is presented that automatically classifies tool wear of turning tools into four classes (no, low, medium, high wear). A cutting tool was monitored with infrared (IR) camera immediately after the cut and in the following 60 s. The Convolutional Neural Network Inception V3 was used to analyse and classify the thermographic images, which were divided into different groups depending on the time of acquisition. Based on classification result, one gets information about the cutting capability of the tool for further machining. The proposed model, combining Infrared Thermography, Computer Vision, and Deep Learning, proved to be a suitable method with results of more than 96% accuracy. The most appropriate time of image acquisition is 6–12 s after the cut is finished. While existing temperature based TCM systems focus on measuring a cutting tool absolute temperature, the proposed system analyses a temperature distribution (relative temperatures) on the whole image based on image features.
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12
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Abstract
Fluid Pumps serve a critical function in hydraulic and thermodynamic systems, and this often exposes them to prolonged use, leading to fatigue, stress, contamination, filter clogging, etc. On one hand, vibration monitoring for hydraulic components has shown reliable efficiencies in fault detection and isolation (FDI) practices. On the other hand, signal processing techniques provide reliable FDI parameters for artificial intelligence (AI)-based data-driven diagnostics (and prognostics) and have recently attracted global interest across different disciplines and applications. Particularly for cost-aware systems, the choice of diagnostic parameters determines the reliability of an FDI/diagnostic model. By extracting (and selecting) discriminative spectral and transient features from solenoid pump vibration signals, accurate diagnostics across operating conditions can be achieved using AI-based FDI algorithms. This study employs a deep neural network (DNN) for fault diagnosis after a correlation-based selection of discriminative spectral and transient features. To solve the problem of hyperparameter selection for the proposed model, a grid search technique was employed for optimal search for parameters (number of layers, neurons, activation function, weight optimizer, etc.) on different network architectures.The results reveal the high accuracy of a three-layer DNN with ReLU activation function, with a test accuracy of 99.23% and a minimal false alarm rate on a case study.
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13
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Wang R, Song Q, Liu Z, Ma H, Gupta MK, Liu Z. A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts. SENSORS 2021; 21:s21175779. [PMID: 34502670 PMCID: PMC8434337 DOI: 10.3390/s21175779] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023]
Abstract
Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.
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Affiliation(s)
- Runqiong Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
| | - Qinghua Song
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
- National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
- Correspondence:
| | - Zhanqiang Liu
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
- National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
| | - Haifeng Ma
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
- National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
| | - Munish Kumar Gupta
- Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland;
| | - Zhaojun Liu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China;
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14
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Ostasevicius V, Karpavicius P, Paulauskaite-Taraseviciene A, Jurenas V, Mystkowski A, Cesnavicius R, Kizauskiene L. A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes. SENSORS 2021; 21:s21093137. [PMID: 33946491 PMCID: PMC8124530 DOI: 10.3390/s21093137] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 04/27/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022]
Abstract
There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn. A wireless transmission of electrical impulses from the capacitor is proposed, where the collected electrical energy is charged and discharged when a defined potential is reached. The frequency of the discharge pulses is directly proportional to the wear level of the tool and, at the same time, to the surface roughness of the workpiece. By employing these measures, we investigate the support vector machine (SVM) approach for wear level prediction.
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Affiliation(s)
- Vytautas Ostasevicius
- Institute of Mechatronics, Kaunas University of Technology, Studentu 56, LT-51424 Kaunas, Lithuania; (P.K.); (V.J.)
- Correspondence: ; Tel.: +370-37-300-909
| | - Paulius Karpavicius
- Institute of Mechatronics, Kaunas University of Technology, Studentu 56, LT-51424 Kaunas, Lithuania; (P.K.); (V.J.)
| | | | - Vytautas Jurenas
- Institute of Mechatronics, Kaunas University of Technology, Studentu 56, LT-51424 Kaunas, Lithuania; (P.K.); (V.J.)
| | - Arkadiusz Mystkowski
- Department of Automatic Control and Robotics, Bialystok University of Technology, 15-351 Bialystok, Poland;
| | - Ramunas Cesnavicius
- Faculty of Mechanical Engineering and Design, Kaunas University of Technology, Studentu 56, LT-51424 Kaunas, Lithuania;
| | - Laura Kizauskiene
- Department of Computer Sciences, Kaunas University of Technology, Studentu 50-210, LT-51368 Kaunas, Lithuania;
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15
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Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process. SENSORS 2021; 21:s21051917. [PMID: 33803442 PMCID: PMC7967223 DOI: 10.3390/s21051917] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/16/2022]
Abstract
This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.
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16
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Woźniak M. Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments. SENSORS (BASEL, SWITZERLAND) 2020; 21:s21010045. [PMID: 33374103 PMCID: PMC7795168 DOI: 10.3390/s21010045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 12/22/2020] [Indexed: 06/12/2023]
Abstract
The recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice [...].
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Affiliation(s)
- Marcin Woźniak
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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17
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A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10207298] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Tool condition monitoring is one of the classical problems of manufacturing that is yet to see a solution that can be implementable in machine shops around the world. In tool condition monitoring, we are mostly trying to define a tool change policy. This tool change policy would identify a tool that produces a non-conforming part. When the non-conforming part producing tool is identified, it could be changed, and a proactive approach to machining quality that saves resources invested in non-conforming parts would be possible. The existing studies highlight three barriers that need to be addressed before a tool condition monitoring solution can be implemented to carry out tool change decision-making autonomously and independently in machine shops around the world. First, these systems are not flexible enough to include different quality requirements of the machine shops. The existing studies only consider one quality aspect (for example, surface finish), which is difficult to generalize across the different quality requirements like concentricity or burrs on edges commonly seen in machine shops. Second, the studies try to quantify the tool condition, while the question that matters is whether the tool produces a conforming or a non-conforming part. Third, the qualitative answer to whether the tool produces a conforming or a non-conforming part requires a large amount of data to train the predictive models. The proposed model addresses these three barriers using the concepts of computer vision, a convolution neural network (CNN), and transfer learning (TL) to teach the machines how a conforming component-producing tool looks and how a non-conforming component-producing tool looks.
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18
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Wiciak-Pikuła M, Felusiak-Czyryca A, Twardowski P. Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling. SENSORS 2020; 20:s20205798. [PMID: 33066308 PMCID: PMC7602040 DOI: 10.3390/s20205798] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/07/2020] [Accepted: 10/10/2020] [Indexed: 11/16/2022]
Abstract
This article deals with the phenomenon of tool wear prediction in face milling of aluminum matrix composite materials (AMC), class as hard-to-cut materials. Artificial neural networks (ANN) are one of the tools used to predict tool wear or surface roughness in machining. Model development is applicable when regression models do not give satisfactory results. Because of their mechanical properties based on SiC or Al2O3 reinforcement, AMCs are applied in the automotive and aerospace industry. Due to these materials’ abrasive nature, a three-edged end mill with diamond coating was selected to carry out milling tests. In this work, multilayer perceptron (MLP) models were used to predict the tool flank wear VBB and tool corner wear VBC during milling of AMC with 10% SiC content. The signals of vibration acceleration and cutting forces were selected as input to the network, and the tests were carried out with three cutting speeds. Based on the analysis of the developed models, the models with the best efficiency were selected, and the quality of wear prediction was assessed. The main criterion for evaluating the quality of the developed models was the mean square error (MSE) in order to compare measured and predicted value of tool wear.
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