Cooling/Lubrication Methods in Surface Engineering and Machining Applications of Different Materials

A special issue of Lubricants (ISSN 2075-4442).

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 13484

Special Issue Editors


E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the release of a new Special Issue, “Cooling/Lubrication Methods in Surface Engineering and Machining Applications of Different Materials” in the journal Lubricants. Original research papers, short communications, and state-of-the-art reviews which are within the scope of this Special Issue are invited.

This Special Issue presents micro-machining and macro-machining methods with various cooling methods and lubrication of the most demanded materials, such as metals, alloys, composites, polymers, etc. The present issue also covers different cutting processes such as turning, milling, drilling, boring, shaping, gear hobbing, gear-tooth shaping, etc. and machining with abrasive tools (such as grinding, honing, polishing, super-finishing, abrasive belt machining, abrasive machining of flexible tool, shaving, etc.). In addition, surface engineering applications such as coatings with different methods, wear phenomena, slurry erosion wear, and the tribological mechanism of different materials are covered in this Special Issue.

Priority is also given to research into machining techniques using various cooling and lubricating methods, such as dry, conventional cooling systems, minimum quantity of lubricants (MQL), cryogenic lubrication (CL), and high-pressure cooling (HPC). The application of various machining methods and the study of such parameters as surface integrity (machined surface quality and surface topography, surface layer stresses, grain size and microstructures, microhardness, etc.), tool wear, cutting forces, chip shape, elastic deformations, rigidity of technological systems (machine–device–tool–workpiece), etc. are of interest to this Special Issue. Also of interest to the Special Issue will be the study of sustainable production using various methods of cooling and lubrication for machining, in particular considering aspects such as energy consumption, carbon emissions, cost modeling, etc.

We would like to invite all researchers interested in the broadly understood research of machining processes and surface texturing with various cooling methods and lubrication to present their results in papers related to both experimental and theoretical research. This will create a collaborative study between researchers of various cooling and lubrication methods for machining and sustainability, useful for further work in understanding and developing this area of science.

Dr. Danil Yurievich Pimenov
Dr. Munish Kumar Gupta
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. Lubricants 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

  • macro and micro machining processes
  • turning, milling, drilling, boring, shaping, gear hobbing, gear-tooth shaping
  • grinding, honing, polishing, superfinishing, abrasive belt machining, abrasive machining of flexible tool, shaving
  • cooling’s and lubricant’s techniques: dry, conventional cooling system, minimum quantity of lubricant (MQL), cryogenic lubrication (CL), and high-pressure cooling (HPC)
  • tool life, cutting forces, cutting power, surface integrity and topography
  • sustainability manufacturing (energy consumption, carbon emissions, cost modelling, etc.)
  • surface science, slurry erosion behavior, arc spray coatings, surface wear, coefficient of friction, surface cracks, ceramics, three body wear mechanism, etc.
  • dry sliding wear, lubricating conditions, self lubrications, pin on disc, etc.

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 7337 KiB  
Article
Selected Aspects of Lubrication in Die Forging Processes at Elevated Temperatures—A Review
by Marek Hawryluk, Łukasz Dudkiewicz, Jan Marzec, Marcin Rychlik and Roger Tkocz
Lubricants 2023, 11(5), 206; https://doi.org/10.3390/lubricants11050206 - 07 May 2023
Cited by 2 | Viewed by 1661
Abstract
The paper concerns selected aspects of the application of cooling–lubricating agents as well as methods and devices assigned to lubrication in hot die forging processes realized at elevated and high temperatures in the context of their effect on the quality of the forgings [...] Read more.
The paper concerns selected aspects of the application of cooling–lubricating agents as well as methods and devices assigned to lubrication in hot die forging processes realized at elevated and high temperatures in the context of their effect on the quality of the forgings and the durability of the forging instrumentation. An analysis was made of the currently used lubricants and their properties and applications in selected industrial forging processes, and a review was conducted of the presently applied cooling–lubricating systems and devices. The article also presents the authors’ own studies referring to the effect of the application of lubricating and cooling agents, the volume of the lubricant portion, the times and directions of its application, and other factors affecting tribological conditions. It also presents lubricating devices constructed based on the knowledge and experience of the authors. The elaborated systems, introduced into selected forging processes, make it possible to examine the effect of the volume and time-frequency of the applied lubricant dose on the wear of the tools and also to select and ensure the optimal tribological conditions in the process with respect to durability. The obtained research results, which were confirmed in the industrial process, indicate the great potential of implementing such devices also in other forging processes because the proposed solutions ensure greater repeatability and stability of working conditions. This increases the efficiency of production and thus significantly reduces the unit production costs, as a two-fold increase (from 8000 to 16,000 forgings) in tool life has been observed. Full article
Show Figures

Figure 1

17 pages, 8733 KiB  
Article
Experimental Investigation into the Friction Coefficient of Ball-on-Disc in Dry Sliding Contact Considering the Effects of Surface Roughness, Low Rotation Speed, and Light Normal Load
by Qi Wen, Mingming Liu, Zenglei Zhang and Yunyun Sun
Lubricants 2022, 10(10), 256; https://doi.org/10.3390/lubricants10100256 - 13 Oct 2022
Cited by 7 | Viewed by 2277
Abstract
The friction coefficient is one of the key parameters in the tribological performance of mechanical systems. In the condition of light normal load and low rotation speed, the friction coefficients of ball-on-disc with rough surface in dry sliding contact are experimentally investigated. Friction [...] Read more.
The friction coefficient is one of the key parameters in the tribological performance of mechanical systems. In the condition of light normal load and low rotation speed, the friction coefficients of ball-on-disc with rough surface in dry sliding contact are experimentally investigated. Friction tests are carried out under normal load 2–9 N, rotation speed 20–48 rpm at room temperature, and surface roughness 0.245–1.010 μm produced by grinding, milling, and turning. Results show that the friction coefficient increases first and then becomes stable, in which the running-in and steady-state periods are included. With the growth of normal load and rotation speed, or the decline of surface roughness, the duration and fluctuation of the running-in period verge to reduce. The whole rising slope of the friction coefficient in the running-in period goes up more quickly with the increment of rotation speed, and it ascends more slowly as normal load enlarges. In terms of the steady-state period, the deviation of the friction coefficient shows a dwindling trend when normal load or rotation speed grows, or surface roughness descends. As normal load or rotation speed rises, the value of the friction coefficient rises first and then drops. Additionally, the mean value of the friction coefficient in steady-state is approximately independent of surface roughness. Full article
Show Figures

Figure 1

40 pages, 6365 KiB  
Article
Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions
by Salah Al-Zubaidi, Jaharah A.Ghani, Che Hassan Che Haron, M. N. Mohammed, Adnan Naji Jameel Al-Tamimi, Samaher M.Sarhan, Mohd Shukor Salleh, M. Abdulrazaq and Oday I. Abdullah
Lubricants 2022, 10(10), 236; https://doi.org/10.3390/lubricants10100236 - 25 Sep 2022
Cited by 5 | Viewed by 1609
Abstract
Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when [...] Read more.
Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were divided randomly into three subsets for training, validation, and testing the developed hybrid intelligent model. ANOVA results revealed that feed rate is highly affected by the surface roughness followed by the depth of cut. One-way ANOVA, including a Post-Hoc test, was used to evaluate the performance of three developed models. The hybrid model of Artificial Neural Network-Gravitational Search Algorithm (ANN-GSA) has outperformed Artificial Neural Network (ANN) and Artificial Neural Network-Genetic Algorithm (ANN-GA) models. ANN-GSA achieved minimum testing mean square error of 7.41 × 10−13 and a maximum R-value of 1. Further, its convergence speed was faster than ANN-GA. GSA proved its ability to improve the performance of BPNN, which suffers from local minima problems. Full article
Show Figures

Figure 1

17 pages, 6314 KiB  
Article
Wear Behavior of Bronze vs. 100Cr6 Friction Pairs under Different Lubrication Conditions for Bearing Applications
by Recep Demirsöz
Lubricants 2022, 10(9), 212; https://doi.org/10.3390/lubricants10090212 - 02 Sep 2022
Cited by 7 | Viewed by 1999
Abstract
Damage due to a shortage or excess of or the pollution of lubricating oil is often cited as one of the most significant issues confronted by the rolling mill sectors. Lubrication can be provided by either central lubrication systems or individual lubrication systems. [...] Read more.
Damage due to a shortage or excess of or the pollution of lubricating oil is often cited as one of the most significant issues confronted by the rolling mill sectors. Lubrication can be provided by either central lubrication systems or individual lubrication systems. In this study, the wear characteristics of the mono-block rolling plain bearing material that is utilized in wire rod rolling mills were evaluated under conditions where the lubricating oil medium included either 2.5% of scale, 5% of scale, or no scale at all. In this experimental study, a unique ball-on-flat experimental setup similar to the one used in the ASTM G133-05 standards was used. Bronze was used as the bearing material and 100Cr6 roller-bearing steel was used as a steel ball of 6 mm in diameter. The experiments were carried out at room temperature, at three different sliding speeds of 5 mm/s, 10 mm/s, and 15 mm/s, and with three different loads of 10 N, 20 N, and 30 N. The wear mechanisms were analyzed visually and elementally using Scanning Electron Microscope (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) methods. An Analysis of Variance (ANOVA) and the Response Surface Method (RSM) were used to analyze the test results, such as volumetric material loss, the coefficient of friction, and the surface profile. In this study, which was carried out in a lubricant environment containing solid particles, the most effective parameter was the environmental parameter. The increase in the number of solid particles caused an increase in volume loss and friction coefficient. Full article
Show Figures

Figure 1

13 pages, 3758 KiB  
Article
Enhancement of Deep Drilling for Stainless Steels by Nano-Lubricant through Twist Drill Bits
by Tien-Dat Hoang, Thu-Ha Mai and Van-Du Nguyen
Lubricants 2022, 10(8), 173; https://doi.org/10.3390/lubricants10080173 - 29 Jul 2022
Cited by 4 | Viewed by 1817
Abstract
This paper represents a new lubricant method which is able to one-stroke drill deep holes with a length-to-diameter of 8, on the AISI SUS 304 stainless steel. By adding graphene nanosheet into typical soluble emulsion and then mixing with water, a nano fluid [...] Read more.
This paper represents a new lubricant method which is able to one-stroke drill deep holes with a length-to-diameter of 8, on the AISI SUS 304 stainless steel. By adding graphene nanosheet into typical soluble emulsion and then mixing with water, a nano fluid can be made. The results revealed that using nanofluid can provide a reduction of 4.4-fold of the drilling torque, and thus expand the tool life as many as 20 times, compared with using typical emulsion lubricant. The proper set of cutting parameters was found by using Taguchi L9 experiments as 550 rpm spindle speed and 0.05 mm/rev. The results can be expanded to apply in other deep drilling of hard-to-cut material, using inexpensive devices and avoiding peck-drilling. The proposed lubricant can also be promissing for other machining operations. Full article
Show Figures

Figure 1

16 pages, 1775 KiB  
Article
Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid
by Vineet Dubey, Anuj Kumar Sharma and Danil Yurievich Pimenov
Lubricants 2022, 10(5), 81; https://doi.org/10.3390/lubricants10050081 - 02 May 2022
Cited by 28 | Viewed by 3058
Abstract
Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined [...] Read more.
Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy. Full article
Show Figures

Figure 1

Back to TopTop