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Machines
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Advances in Tool Life Prediction in Machining
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Advances in Tool Life Prediction in Machining

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 20627

Special Issue Editors

Prof. Dr. Kondo Adjallah

E-Mail Website
Guest Editor
Ecole Nationale d’Ingénieurs, Université de Lorraine, 57000 Metz, France
Interests: intelligent data acquisition; risk; anticipation; resilience; RUL; machine health monitoring; maintenance decision support
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Cheng

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, London UB8 3PH, UK
Interests: design of high precision machines; air-bearings design; micro cutting; ultraprecision machining; smart tooling
Special Issues, Collections and Topics in MDPI journals
Dr. Lucas Equeter

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Mons, 7000 Mons, Belgium
Interests: tool life prediction; tool condition monitoring; statistical prognostic and health management; maintenance policy optimization; predictive maintenance

Special Issue Information

Dear Colleagues,

Machining produces manufactured goods with outstanding dimensional, geometrical, surface, and subsurface quality tolerances and specifications. Most industries, including major sectors such as automotive, aerospace, medical, consumer electronics, and railway, rely on machining performances to obtain quality workpieces contributing to the performance and reliability of machines, products, and devices.

Although continuous improvements occur in tool technology and condition monitoring, machining tools bear unique specificities due to their relatively short lifespan. The variability of tool degradation induces the necessity of producing updated predictions of the tool life in order to limit the risk of scrap, which is particularly problematic in high-value productions, such as aerospace industries. A balance should be sought between the quality of produced workpieces and tool replacement.

Several approaches to the prediction of tool life in machining include physical modeling, e.g., through finite element modeling, statistical methods, e.g., correlating the evolution of condition monitoring variables with the tool remaining life, and a diversity of other approaches, including artificial intelligence. Most of these advances are usually validated with specific tools and materials, but comprehensive approaches are still needed. Furthermore, to raise industrial interest, the developed solutions also need to provide tool life estimates with low computational time that allows replacing the tool before failure occurs, and also applicable to high throughput precision machining and industrial automation in the context of Industry 4.0 and beyond.

In this context, this Special Issue intends to draw attention to the newest advances in tool life prediction and enhancement techniques in machining and their applications in industrial scale machining systems.

The proposed Special Issue particularly fits the following scopes, but are not limited to

  • Applications of automation
  • Systems and control engineering
  • Mechanical engineering
  • Computer engineering
  • Industrial design
  • Human–machine interfaces
  • Mechanical systems, machines, and related components
  • Machine vision
  • Machine diagnostics and prognostics (condition monitoring)

Prof. Dr. Kondo Adjallah
Prof. Dr. Kai Cheng
Dr. Lucas Equeter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tool wear
  • machining
  • reliability engineering
  • numerical simulation
  • degradation
  • tool life prediction
  • tool geometry
  • smart cutting tools
  • smart machining

Published Papers (6 papers)

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Research

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25 pages, 6709 KiB  
Article
An Experimental and Numerical Study of Damage Due to Particle Impact on Sapphire Orifices Used in High-Pressure Water Jet Cutting
by Markus Mlinaric, Hassen Jemaa, Thomas Hassel and Hans Jürgen Maier
Machines 2022, 10(9), 756; https://doi.org/10.3390/machines10090756 - 01 Sep 2022
Cited by 1 | Viewed by 3072
Abstract
In the present study, the damage mechanisms that cause premature failure of sapphire water jet orifices were analyzed using a combined experimental and finite element modeling (FEM) approach. Depending on the operating behavior and local conditions, the service life of orifices for high-pressure [...] Read more.
In the present study, the damage mechanisms that cause premature failure of sapphire water jet orifices were analyzed using a combined experimental and finite element modeling (FEM) approach. Depending on the operating behavior and local conditions, the service life of orifices for high-pressure water jet cutting often deviates considerably from the manufacturer’s specifications. Literature states a typical service life of 50 to 100 h, while in some cases, premature failure after a few hours or even minutes of operation can be observed. The focus of this paper is on the interaction of particles that impact the orifice surface but also the effect of faulty orifice assembly is taken into account. To estimate the risk of failure, the stress distribution in critical parts of the orifice were calculated via FEM, which is fed with experimental data. The modified Mohr failure criterion was then used to evaluate the stress distributions with respect to the possible failure of the orifice jewel. The results revealed that the risk of damage caused by excessive assembly preload forces is marginal. The stress caused by the impact of particles of different sizes is up to four orders of magnitude higher than the stress caused by assembly forces and is therefore identified as the main risk for orifices to fail prematurely. Experimental data shows mainly particles of calcium carbonate and iron–aluminum silicates, which are compounds that originate from the process water itself. It is demonstrated that particles are more critical than formerly assumed in the literature. This paper identifies particles with a diameter of more than 10 µm as critical when there are no other loads present. In operation, even particles as small as 2 µm in diameter can cause damage to the orifice jewel. To prevent premature orifice failure due to foreign particles, water filtration with a 2 µm mesh is recommended, while future research needs to focus on the interior cutting head design to prevent precipitation from the process water. Full article
(This article belongs to the Special Issue Advances in Tool Life Prediction in Machining)
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28 pages, 116426 KiB  
Article
Tool Wear Rate and Surface Integrity Studies in Wire Electric Discharge Machining of NiTiNOL Shape Memory Alloy Using Diffusion Annealed Coated Electrode Materials
by Vinayak N. Kulkarni, Vinayak N. Gaitonde, Manjaiah Mallaiah, Ramesh S. Karnik and Joao Paulo Davim
Machines 2022, 10(2), 138; https://doi.org/10.3390/machines10020138 - 15 Feb 2022
Cited by 8 | Viewed by 2382
Abstract
Electrode material used in wire electric discharge machining (WEDM/wire EDM) plays a vital role in determining the machined component quality. In particular, when machining hard materials like nickel titanium/NiTi (NiTiNOL) shape memory alloy, the quality of electrode material is important as it may [...] Read more.
Electrode material used in wire electric discharge machining (WEDM/wire EDM) plays a vital role in determining the machined component quality. In particular, when machining hard materials like nickel titanium/NiTi (NiTiNOL) shape memory alloy, the quality of electrode material is important as it may have adverse effects on the surface properties of the alloy. Different electrode materials give different performances, as each electrode material is made up of different conductivity, compositions and tensile strength. Therefore, detailed experimental studies have been carried out to understand the effect of diffusion annealed coated wires (X-type and A-type) on NiTiNOL SMA during the wire EDM process. The tool wear rate and surface roughness responses have been studied for both the electrode materials against different wire EDM variables such as pulse time, pause time, wire feed and spark gap set voltage. The impact of these process parameters on the stated output responses has been analyzed and further surface and subsurface analysis of the machined component has been carried out to understand the impact of diffusion annealed electrode materials during the wire EDM process. The investigation reveals that an A-type diffusion annealed coated wire is found to be most suitable in terms of tool wear rate, surface roughness and surface integrity during machining of NiTiNOL shape memory alloy compared to X-type and traditional brass-based electrode materials. Surface topographical properties were studied using confocal microscopic analysis and scanning electron microscope (SEM) with energy-dispersive spectroscopy (EDS) analysis. The subsurface analysis like microhardness and recast layer thickness was also studied for both the wires against different machining conditions. Full article
(This article belongs to the Special Issue Advances in Tool Life Prediction in Machining)
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17 pages, 2574 KiB  
Article
Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms
by Yanqing Zhao, Kondo H. Adjallah, Alexandre Sava and Zhouhang Wang
Machines 2021, 9(12), 315; https://doi.org/10.3390/machines9120315 - 26 Nov 2021
Cited by 6 | Viewed by 1496
Abstract
Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the [...] Read more.
Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors. Full article
(This article belongs to the Special Issue Advances in Tool Life Prediction in Machining)
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12 pages, 1671 KiB  
Article
The Deterministic Nature of Sensor-Based Information for Condition Monitoring of the Cutting Process
by Rui Silva and António Araújo
Machines 2021, 9(11), 270; https://doi.org/10.3390/machines9110270 - 06 Nov 2021
Cited by 1 | Viewed by 1388
Abstract
Condition monitoring of the cutting process is a core function of autonomous machining and its success strongly relies on sensed data. Despite the enormous amount of research conducted so far into condition monitoring of the cutting process, there are still limitations given the [...] Read more.
Condition monitoring of the cutting process is a core function of autonomous machining and its success strongly relies on sensed data. Despite the enormous amount of research conducted so far into condition monitoring of the cutting process, there are still limitations given the complexity underlining tool wear; hence, a clearer understanding of sensed data and its dynamical behavior is fundamental to sustain the development of more robust condition monitoring systems. The dependence of these systems on acquired data is critical and determines the success of such systems. In this study, data is acquired from an experimental setup using some of the commonly used sensors for condition monitoring, reproducing realistic cutting operations, and then analyzed upon their deterministic nature using different techniques, such as the Lyapunov exponent, mutual information, attractor dimension, and recurrence plots. The overall results demonstrate the existence of low dimensional chaos in both new and worn tools, defining a deterministic nature of cutting dynamics and, hence, broadening the available approaches to tool wear monitoring based on the theory of chaos. In addition, recurrence plots depict a clear relationship to tool condition and may be quantified considering a two-dimensional structural measure, such as the semivariance. This exploratory study unveils the potential of non-linear dynamics indicators in validating information strength potentiating other uses and applications. Full article
(This article belongs to the Special Issue Advances in Tool Life Prediction in Machining)
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Review

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26 pages, 2875 KiB  
Review
Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review
by K Manjunath, Suman Tewary, Neha Khatri and Kai Cheng
Machines 2021, 9(12), 369; https://doi.org/10.3390/machines9120369 - 19 Dec 2021
Cited by 19 | Viewed by 4531
Abstract
The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are [...] Read more.
The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies. Full article
(This article belongs to the Special Issue Advances in Tool Life Prediction in Machining)
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54 pages, 1710 KiB  
Review
A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques
by Lorenzo Colantonio, Lucas Equeter, Pierre Dehombreux and François Ducobu
Machines 2021, 9(12), 351; https://doi.org/10.3390/machines9120351 - 10 Dec 2021
Cited by 29 | Viewed by 5819
Abstract
In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to [...] Read more.
In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance. Full article
(This article belongs to the Special Issue Advances in Tool Life Prediction in Machining)
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