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Application of Advanced Sensors Systems and Artificial Intelligence in Machining

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 37279

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Special Issue Editors

Danil Yurievich Pimenov

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Department of Automated Mechanical Engineering, South Ural State University, 454080 Chelyabinsk, Russia
Interests: metal cutting and cutting tools; increasing the efficiency of face milling operations by considering tool wear aspects; effect of tool wear and cutting parameters on tool life, cutting forces, the roughness of machined surfaces, and physical and mechanical processes in cutting materials; application of dynamometers, accelerometers, and power sensors for machining processes; artificial intelligence; mathematical modeling in machining processes; optimization of computer numerical control (CNC) and conventional machining processes
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Dr. Tadeusz Mikolajczyk

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Guest Editor

Department of Production Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland
Interests: machining processes, cutting tools, and robotics; investigation of machining processes in the field of orthogonal and oblique cutting, especially the phenomena that affect surface quality and tool life using image recognition; research in the field of new solutions of cutting tools for oblique cutting, with reduced edge volume, reconfigurable tools, and mechatronic tools controlled by stepper motor abrasive machining; controlling the oscillatory superfinishing process for increased machining efficiency and accuracy; robotics, including intelligent machining using a robot equipped with special tools and vision sensors to recognize the shape of the surface and surface condition testing systems; teaching tasks to mobile robots; innovative, patented walking robots with extremally low DOF numbers (3, 4); application of mechatronics in manufacturing and robotics with own software; application of artificial intelligence and optimization of manufacturing processes
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Dr. Munish Kumar Gupta

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Guest Editor

Faculty of Mechanical Engineering, Opole University of Technology, 45-271 Opole, Poland
Interests: sustainable manufacturing; metal cutting; metal casting; modelling and optimization; rapid prototyping
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Special Issue Information

Dear Colleagues,

The current level of Industry 4.0 is characterized by two main technologies—efficient use of new sensors and application of artificial intelligence (AI). This Special Issue deals with single- or multisensor systems used for various machining methods, such as dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, temperature sensors, ultrasonic sensors, optical sensors, laser sensors, and other sensors. The application of sensor systems can more effectively solve the problems of automation and modeling of technological parameters of the main types of machining, such as turning, milling, drilling and grinding, etc. Such systems are widely used in tool condition monitoring, monitoring of machined surfaces, machine dynamics, etc. In this Special Issue, modern methods of artificial intelligence for the analysis and prediction of data obtained by sensor systems are also considered, such as neural networks, image processing, fuzzy logic, adaptive neurofuzzy inference systems, Bayesian networks, support vector machines, ensembles, decision trees and regression, k-nearest neighbors, Markov models, singular spectral analysis, and genetic algorithms. The possibility of building diagnostic systems of machining processes based on complex sensor systems and their use in conjunction with artificial intelligence create opportunities for the development of efficient and reliable machining processes for Industry 4.0.

It is our pleasure to invite you to submit original research papers, short communications or state-of-the-art reviews which are within the scope of this Special Issue.

Danil Yurievich Pimenov
Dr. Tadeusz Mikolajczyk
Dr. Munish Kumar Gupta
Guest Editors

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Keywords

Industry 4.0
Sensor system
Vision system
Artificial intelligence
Machining
Tool condition monitoring
Turning
Milling
Drilling
Grinding

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Published Papers (7 papers)

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Research

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25 pages, 17385 KiB

Open AccessArticle

A Novel Multi-Task Learning Model with PSAE Network for Simultaneous Estimation of Surface Quality and Tool Wear in Milling of Nickel-Based Superalloy Haynes 230

by Minghui Cheng, Li Jiao, Pei Yan, Huiqing Gu, Jie Sun, Tianyang Qiu and Xibin Wang

Sensors 2022, 22(13), 4943; https://doi.org/10.3390/s22134943 - 30 Jun 2022

Cited by 3 | Viewed by 1630

Abstract

For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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13 pages, 3830 KiB

Open AccessArticle

Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography

by Nika Brili, Mirko Ficko and Simon Klančnik

Sensors 2021, 21(19), 6687; https://doi.org/10.3390/s21196687 - 8 Oct 2021

Cited by 16 | Viewed by 3195

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. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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17 pages, 16076 KiB

Open AccessArticle

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts

by Runqiong Wang, Qinghua Song, Zhanqiang Liu, Haifeng Ma, Munish Kumar Gupta and Zhaojun Liu

Sensors 2021, 21(17), 5779; https://doi.org/10.3390/s21175779 - 27 Aug 2021

Cited by 21 | Viewed by 3756

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. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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16 pages, 5874 KiB

Open AccessArticle

Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling

by Fatih Akkoyun, Ali Ercetin, Kubilay Aslantas, Danil Yurievich Pimenov, Khaled Giasin, Avinash Lakshmikanthan and Muhammad Aamir

Sensors 2021, 21(13), 4432; https://doi.org/10.3390/s21134432 - 28 Jun 2021

Cited by 33 | Viewed by 5215

Abstract

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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Review

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23 pages, 8751 KiB

Open AccessFeature PaperReview

A Comparative Review of Thermocouple and Infrared Radiation Temperature Measurement Methods during the Machining of Metals

by Emilios Leonidas, Sabino Ayvar-Soberanis, Hatim Laalej, Stephen Fitzpatrick and Jon R. Willmott

Sensors 2022, 22(13), 4693; https://doi.org/10.3390/s22134693 - 22 Jun 2022

Cited by 35 | Viewed by 6573

Abstract

During the machining process, substantial thermal loads are generated due to tribological factors and plastic deformation. The increase in temperature during the cutting process can lead to accelerated tool wear, reducing the tool’s lifespan; the degradation of machining accuracy in the form of dimensional inaccuracies; and thermally induced defects affecting the metallurgical properties of the machined component. These effects can lead to a significant increase in operational costs and waste which deviate from the sustainability goals of Industry 4.0. Temperature is an important machining response; however, it is one of the most difficult factors to monitor, especially in high-speed machining applications such as drilling and milling, because of the high rotational speeds of the cutting tool and the aggressive machining environments. In this article, thermocouple and infrared radiation temperature measurement methods used by researchers to monitor temperature during turning, drilling and milling operations are reviewed. The major merits and limitations of each temperature measurement methodology are discussed and evaluated. Thermocouples offer a relatively inexpensive solution; however, they are prone to calibration drifts and their response times are insufficient to capture rapid temperature changes in high-speed operations. Fibre optic infrared thermometers have very fast response times; however, they can be relatively expensive and require a more robust implementation. It was found that no one temperature measurement methodology is ideal for all machining operations. The most suitable temperature measurement method can be selected by individual researchers based upon their experimental requirements using critical criteria, which include the expected temperature range, the sensor sensitivity to noise, responsiveness and cost. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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32 pages, 7357 KiB

Open AccessReview

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

by Mustafa Kuntoğlu, Abdullah Aslan, Danil Yurievich Pimenov, Üsame Ali Usca, Emin Salur, Munish Kumar Gupta, Tadeusz Mikolajczyk, Khaled Giasin, Wojciech Kapłonek and Shubham Sharma

Sensors 2021, 21(1), 108; https://doi.org/10.3390/s21010108 - 26 Dec 2020

Cited by 181 | Viewed by 11921

Abstract

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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Other

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16 pages, 17611 KiB

Open AccessLetter

Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes

by Jarosław Kurek, Izabella Antoniuk, Bartosz Świderski, Albina Jegorowa and Michał Bukowski

Sensors 2020, 20(23), 6978; https://doi.org/10.3390/s20236978 - 6 Dec 2020

Cited by 17 | Viewed by 2536

Abstract

In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When the drill is not sharp enough, it can result in a poor quality product and therefore generate some financial loss for the company. In various approaches to this problem, usually, three classes are considered: green for a drill that is sharp, red for the opposite, and yellow for a tool that is suspected of being worn out, requiring additional evaluation by a human expert. In the above problem, it is especially important that the green and the red classes not be mistaken, since such errors have the highest probability of generating financial loss for the manufacturer. Most of the solutions analysing this problem are too complex, requiring specialized equipment, high financial investment, or both, without guaranteeing that the obtained results will be satisfactory. In the approach presented in this paper, images of drilled holes are used as the training data for the Siamese network. The presented solution is much simpler in terms of the data collection methodology, does not require a large financial investment for the initial equipment, and can accurately qualify drill wear based on the chosen input. It also takes into consideration additional manufacturer requirements, like no green-red misclassifications, that are usually omitted in existing solutions. Full article

(This article belongs to the Special Issue Application of Advanced Sensors Systems and Artificial Intelligence in Machining)

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