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Metals
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Machinability Analysis and Modeling of Metal Cutting
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Machinability Analysis and Modeling of Metal Cutting

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Structural Integrity of Metals".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 12484

Special Issue Editor

Prof. Dr. Lida Zhu

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: machining stability and precision machining technology; hybrid robot and rehabilitation robot; intelligent diagnosis and signal processing; ultrasonic vibration machining; 3D printing; additive and subtractive hybrid manufacturing

Special Issue Information

Dear Colleagues,

With the rapid development of various types of high-end equipment, new requirements have been put forward for the efficient and high-precision manufacturing of key structural components; however, such structural components have the characteristics of high strength and high hardness and increase the difficulty of machining and shaping at the same time. This Special Issue is mainly oriented to the research of machining analysis and machining technology, including but not limited to the high-quality and high-efficiency machining of difficult-to-machine materials, in order to improve the component machining accuracy, surface quality, fatigue life, and machining efficiency for machining process analysis and detection, cutting and special machining technology, machine tools and tool technology, metal surface technology and other areas of research. Due to the multidisciplinary nature of machining analysis, research based on analysis technologies such as deep learning and visual fusion is also encouraged, as is research on micro-scale cutting technology and material technology. These research results will be conducive to improving the reliability and performance of high-end equipment, as well as the high-quality and high-efficiency forming of difficult-to-machine materials. Articles on the above research directions are welcome, and this is an excellent opportunity for metal cutting scientists and engineers around the world to share their latest research results. This Special Issue will cover, but is not limited to, the following basic and applied research topics:

  • Difficult-to-machine materials;
  • Titanium alloys;
  • High-strength steels;
  • Stainless steels;
  • High-speed cutting;
  • Cutting simulation;
  • Machining accuracy;
  • Surface quality;
  • Ultrasonic-assisted cutting;
  • Non-traditional machining;
  • Nano/micro/meso-cutting;
  • Milling;
  • Turning;
  • Drilling;
  • Grinding;
  • Optimization of machining trajectory;
  • Advanced cutting tools;
  • Cutting tool design;
  • Material modification;
  • Deep learning;
  • Online monitoring.

Prof. Dr. Lida Zhu
Guest Editor

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. Metals 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 2600 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

  • component machining accuracy
  • surface quality
  • fatigue life
  • machining efficiency
  • machining process analysis and detection
  • cutting and special machining technology
  • machine tools and tool technology
  • metal surface technology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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19 pages, 17096 KB  
Article
Evaluation of Machinability and Energy Consumption of CK45 Steel Using Synthetic-Based Nanofluid and Minimum Quantity Lubrication Cutting Fluid
by Emine Şap, Üsame Ali Usca, Ünal Değirmenci, Serhat Şap and Mahir Uzun
Metals 2025, 15(1), 36; https://doi.org/10.3390/met15010036 - 3 Jan 2025
Cited by 5 | Viewed by 1441
Abstract
CK45 steel has various industrial uses due to its durability, wear resistance and strength. It is generally used in machinery, automotive industry, hydraulic cylinders, bearings, gears and similar applications. It is important to investigate the machinability properties of CK45 steel, which is frequently [...] Read more.
CK45 steel has various industrial uses due to its durability, wear resistance and strength. It is generally used in machinery, automotive industry, hydraulic cylinders, bearings, gears and similar applications. It is important to investigate the machinability properties of CK45 steel, which is frequently used in the manufacturing industry, in different cooling/lubrication environments. This study focused on the effects of a synthetic-based nanofluid cooling strategy and different cutting parameters during the milling of CK45 steel. Additionally, Taguchi analysis was performed to reduce the number of experiments and costs. Sustainable cooling/lubrication techniques were used during milling. A three-axis computer-controlled machine was used for the milling process. According to the findings, flank wear, surface roughness, and energy consumption were reduced by machining in the nanofluid environment. It was observed that Cu nanoparticles added into the nanofluid increased the machinability properties. Furthermore, statistical analysis was employed to ascertain the predominant control variables influencing the response parameters. Machinability efficiency can be increased by using nanoparticulate fluids as a coolant during milling. In addition, costs can be reduced by identifying the most effective factors in the experiment through statistical analysis. Full article
(This article belongs to the Special Issue Machinability Analysis and Modeling of Metal Cutting)
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18 pages, 5000 KB  
Article
Improving Maraging Steel 350 Machinability via Wiper Insert-Enhanced Face Milling
by Adel T. Abbas, Mohamed O. Helmy, Khalid F. Alqosaibi, Shahid Parvez, Ali S. Hasan and Ahmed Elkaseer
Metals 2024, 14(5), 514; https://doi.org/10.3390/met14050514 - 28 Apr 2024
Cited by 1 | Viewed by 2130
Abstract
Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s [...] Read more.
Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing. Full article
(This article belongs to the Special Issue Machinability Analysis and Modeling of Metal Cutting)
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19 pages, 2201 KB  
Article
Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance
by Fatlume Zhujani, Fitore Abdullahu, Georgi Todorov and Konstantin Kamberov
Metals 2024, 14(2), 186; https://doi.org/10.3390/met14020186 - 2 Feb 2024
Cited by 21 | Viewed by 2597
Abstract
The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the [...] Read more.
The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the Taguchi-based design of experiments L9(34) in this study to test and find the best values for process parameters like cutting speed, feed rate, depth of cut, and nose radius. The Taguchi-based multi-objective grey relational approach (GRA) method was used to address the turning problem of Inconel 718 alloy to increase productivity, i.e., by simultaneously minimizing surface roughness, tool wear, and machining time. GRA and the S/N ratio derived from the Taguchi approach were utilized to combine many response characteristics into a single response. The grey relational grade (GRG) produces results such as estimations of the optimal level of input parameters and their proportional significance to specific quality characteristics. By employing ANOVA, the significance of parameters with respect to individual responsibility and the overall quality characteristics of the cutting process were ascertained. The single-objective optimization yielded the following results: minimal surface roughness of 0.167 µm, tool wear of 44.65 µm, minimum cutting time of 19.72 s, and maximum material speed of 4550 mm3/min. While simultaneously optimizing the Inconel 718 superalloy at a cutting speed of 100 m/min, depth of cut of 0.4 mm, feed rate of 0.051 mm/rev, and tool nose radius of 0.4 mm, the results of the multi-objective optimization showed that all investigated response characteristics reached their optimal values (minimum/maximum). To validate the results, confirmatory experiments with the most favorable outcomes were conducted and yielded a high degree of concurrence. Full article
(This article belongs to the Special Issue Machinability Analysis and Modeling of Metal Cutting)
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19 pages, 7214 KB  
Article
Investigation and Statistical Analysis for Optimizing Surface Roughness, Cutting Forces, Temperature, and Productivity in Turning Grey Cast Iron
by Magdy M. El Rayes, Adel T. Abbas, Abdulhamid A. Al-Abduljabbar, Adham E. Ragab, Faycal Benyahia and Ahmed Elkaseer
Metals 2023, 13(6), 1098; https://doi.org/10.3390/met13061098 - 10 Jun 2023
Cited by 4 | Viewed by 2164
Abstract
This paper investigated the influence of cutting parameters, including feed rate, cutting speed, tool nose radius, and wet or dry cutting conditions, on the resultant force, cutting edge/workpiece temperature, and surface roughness when turning grey cast iron. Results showed that increasing the feed [...] Read more.
This paper investigated the influence of cutting parameters, including feed rate, cutting speed, tool nose radius, and wet or dry cutting conditions, on the resultant force, cutting edge/workpiece temperature, and surface roughness when turning grey cast iron. Results showed that increasing the feed rate increased the resultant force, cutting temperature, and surface roughness. At the same time, increasing the cutting speed and nose radius increased the cutting temperature, which in turn reduced the resultant force. For practical applications, basic mathematical calculations based on the sole effect of each parameter on the output of the experiments were used to estimate the extent of percentage increase in cutting temperature due to increasing feed rate, cutting speed, and nose radius. Similarly, the same approach was used to estimate the effect of increasing feed rate, cutting speed, and nose radius on average surface roughness. Results showed that increasing the feed rate increases the cutting temperature by 5 to 11% depending on the nose radius and cutting speed. On the other hand, increasing the cutting speed was found to have limited effect on cutting temperature with small nose radius whereas this effect increases with increasing the nose radius reaching about 11%. Increasing the nose radius also increases the cutting temperature, depending mainly on cutting speed, reaching a maximum of 21% at higher cutting speeds. Results also showed that increasing the feed rate increased the average surface roughness considerably to about 120% at high cutting speeds and a large nose radius. On the other hand, increasing the cutting speed and nose radius reduced the surface roughness (i.e., improved surface quality) by a maximum of 29 and 23%, respectively. In order to study the combined effects of the cutting parameters on the three responses, namely, the resultant cutting force, cutting temperature, and surface roughness, full factorial design and ANOVA were used, where it was found to be in good agreement with mathematical calculations. Additionally, the desirability function optimization tool was used to minimize the measured responses whilst maximizing the material removal rate. Full article
(This article belongs to the Special Issue Machinability Analysis and Modeling of Metal Cutting)
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Review

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38 pages, 20368 KB  
Review
Metal Additive Manufacturing and Molten Pool Dynamic Characterization Monitoring: Advances in Machine Learning for Directed Energy Deposition
by Wentao He, Lida Zhu, Can Liu and Hongxiao Jiang
Metals 2025, 15(2), 106; https://doi.org/10.3390/met15020106 - 22 Jan 2025
Cited by 3 | Viewed by 3472
Abstract
Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found [...] Read more.
Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found extensive applications in critical industrial sectors such as aerospace and aviation. Despite its potential, challenges such as inconsistent part quality and low process repeatability continue to restrict its broader adoption. The core issue underlying these challenges is the complex, dynamic nature of the DED process, which involves the coupling of multiple physical fields. Within this context, the molten pool plays a pivotal role, serving as a key carrier that encapsulates abundant process characteristic information. The dynamic characteristics of the molten pool are intrinsically linked to the final part quality and the repeatability of the process. Consequently, integrating machine learning (ML) methodologies into the monitoring framework can offer robust data-driven support for enhancing both product quality and process consistency. This paper provides a comprehensive review of the research advancements and prospective trends in the dynamic monitoring and control of molten pool characteristics within DED processes underpinned by machine learning techniques. The review is structured around five key areas: an overview and fundamental principles of DED technology, methods for process information sensing during part monitoring, approaches for dynamically monitoring molten pool characteristics, the primary challenges currently faced in intelligent monitoring systems, and the potential future directions for further research and development. Through this detailed examination, the paper aims to shed light on the pivotal role of intelligent monitoring systems in advancing DED technology, ultimately contributing to more reliable and repeatable additive manufacturing processes. Full article
(This article belongs to the Special Issue Machinability Analysis and Modeling of Metal Cutting)
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