Narrow Results

Super-duplex stainless steel (SDSS) 2507 is well known for its complex microstructure and alloying components, which contribute to its exceptional mechanical qualities and resistance to corrosion. Nevertheless, these features provide considerable difficulties during machining. In order to address these challenges and advance sustainability, this work presents a novel machining technique that combines minimal quantity lubrication (MQL) with surface-textured tools. Two different kinds of texture tools were examined: one with linear grooves and the other with a hybrid texture that combined both circular pits and holes. A CNC center lathe machine operating under MQL conditions was used for the experiments. It had two distinct cooling conditions, different tool textures, and cutting parameters like depth of cut, feed rate, and speed. Cutting speed, followed by feed rate, was the most important factor affecting cutting force and tool flank wear, according to analysis of variance (ANOVA). Comparing the hybrid-textured tool under MQL to dry machining conditions, the cutting force was reduced by 40%. A cutting speed of 40m/min, feed rate of 0.1mm/rev, and depth of cut of 0.4mm were found to be the optimal machining parameters employing MQL and hybrid-textured tools for minimizing cutting force and tool wear, the optimal values are 119.57N and 113.875$\mu$m respectively. These findings provide notable enhancements in machinability and sustainability for SDSS 2507, demonstrating the efficiency and usefulness of the suggested approach in MQL machining.

This paper presents the performance of uncoated carbide cutting tool when machining cast iron in dry cutting conditions. Experiments were conducted at various cutting speeds, feed rates, and depths of cut according to Taguchi method design of experiment using a standard orthogonal array L₉(3⁴). The effects of cutting speeds (100-146 m/min), feed rates (0.20-0.35 mm/tooth) and depths of cut (1.0-2.0 mm) on the tool life, surface roughness and cutting forces were evaluated using ANOVA. Results showed that the effects of cutting speed, depth of cut and the feed rate were similar affecting the failure of the carbide cutting tools within the range of tested machining parameters. The contribution of cutting speed, feed rate, and depth of cut in controlling the tool life were 32.12%, 38.56% and 29.32% respectively. Whereas, the cutting speed was the main factor influencing the average surface roughness (R_a) value followed by feed rate. These factors contribute 60.53% and 35.59% respectively to the R_a value. On the other hand, cutting forces generated were greatly influenced by the depth of cut (66.52%) and the feed rate (32.6%). Cutting speed was found insignificant in controlling the generated cutting forces.

Whirling is a cutting process in which a series of cutting edges remove material by turning over the rotating workpiece. In this process, the whirling ring with a number of cutting teeth combined with the rotation and advancement of workpiece, produces pitches of worm. Mechanics of chip formation of the process, however, has not been fully estabilished. To estimate the cutting force during the process, the kinematics and the maximum undeformed chip thickness to be removed by each cutting edge should be thoroughly analyzed.

In this study, using the recently developed model of undeformed chip thickness and the DEFORM software, cutting forces of the whirling process are estimated. The effects of cutting forces on tool are analyzed using the ADAMS software. The validity of the simulations has been verified with a series of cutting experiments.

In the realm of machining, the optimization of cutting conditions stands as a paramount pursuit for enhancing both efficiency and precision. This study embarks on a pioneering investigation delving into the intricate interplay among workpiece temperature, Thermal-Assisted High-Speed Machining (TA-HSM), cutting force dynamics, and vibration amplitudes during the milling process of SKD11 steel. Beyond a mere exploration, this research endeavors to unveil not only the impact of temperature and velocity on machining dynamics but also to delineate regions characterized by elevated temperature and velocity that exhibit the potential to mitigate cutting forces and vibrations, thereby refining machining methodologies. Experimental inquiries encompassing diverse temperature regimes, coupled with variations in cutting speeds, offer valuable insights into the nexus among temperature, cutting force and vibration amplitude. Of particular significance are the high-speed milling trials conducted under the most elevated admissible support temperature, which furnish elucidation on the ramifications of high speeds on cutting forces and vibrations. This inquiry constitutes a substantive contribution to the field by elucidating the correlation between cutting force and vibration amplitude under the thermal influence and high-speed milling within thermally demanding environments. Moreover, this study extends practical utility by proffering actionable insights into the optimal temperature range and cutting speeds requisite for effecting desired enhancements in machining productivity. By discerning and delineating these optimal parameters, this research endeavors to furnish tangible guidelines for practitioners seeking to optimize their machining processes, thus fostering advancements in both efficiency and precision within the machining domain.

Analysis of the cutting forces during machining operations is an important issue. The rough end mill with the serrated profile is broadly used for reduction of cutting forces during milling operation. Since cutting force changes in random behavior during end milling, in this paper we employ fractal theory to analyze the complex structure of cutting force signal. For this purpose, we investigated the influence of variations of cutting depth on variations of fractal structure of cutting forces in wet and dry machining conditions. The results of our analysis showed the variations of fractal structure of cutting forces between different cutting depths, in wet and dry conditions. The employed methodology in this research is not limited to rough end milling and can potentially be applied to other types of machining operations, where the variations of cutting forces is an important issue.

It is known that geometry of cutting tool affects the cutting forces in machining operations. In addition, the value of cutting forces changes during machining operations and creates a chaotic time series (signal). In this paper, we analyze the variations of the complex structure of cutting force signal in rough end milling operation using fractal theory. In fact, we analyze the variations of cutting force signal due to variations of tool geometry (square end mill versus serrated end mill). In case of each type of end mill, we did the machining operation in wet and dry conditions. Based on the results, the fractal structure of cutting force signal changes based on the type of milling tool. We also did the complexity analysis using approximate entropy to check the variations of the complexity of cutting force signal, where the similar behavior of variations between different conditions was obtained. The method of analysis that was used in this research can be applied to other machining operations to study the influence of different machining parameters on variations of fractal structure of cutting force.

Analysis of cutting forces in machining operation is an important issue. The cutting force changes randomly in milling operation where it makes a signal by plotting over time span. An important type of analysis belongs to the study of how cutting forces change along different axes. Since cutting force has fractal characteristics, in this paper for the first time we analyze the variations of complexity of cutting force signal along different axes using fractal theory. For this purpose, we consider two cutting depths and do milling operation in dry and wet machining conditions. The obtained cutting force time series was analyzed by computing the fractal dimension. The result showed that in both wet and dry machining conditions, the feed force (along $x$-axis) has greater fractal dimension than radial force (along $y$-axis). In addition, the radial force (along $y$-axis) has greater fractal dimension than thrust force (along $z$-axis). The method of analysis that was used in this research can be applied to other machining operations to study the variations of fractal structure of cutting force signal along different axes.

Analysis of the surface quality of workpiece is one of the major works in machining operations. Variations of cutting force is an important factor that highly affects the quality of machined workpiece during operation. Therefore, investigating about the variations of cutting forces is very important in machining operation. In this paper, we employ fractal analysis in order to investigate the relation between complex structure of cutting force and surface roughness of machined surface in end milling operation. We run the machining operation in different conditions in which cutting depths, type of cutting tool (serrated versus square end mills) and machining conditions (wet and dry machining) change. Based on the obtained results, we observed the relation between complexity of cutting force and surface roughness of generated surface of machined workpiece due to engagement with the flute surface of end mill, in case of using square end mill in dry machining condition, and also in case of using serrated end mill in wet machining condition. The fractal approach that was employed in this research can be potentially examined in case of other machining operations in order to investigate the possible relation between complex structure of cutting force and surface quality of machined workpiece.

The Ti-6Al-4V alloy is known as materials with high hardness and good mechanical properties. However, the machining process of this material faces many difficulties due to unfavorable machining conditions. When the machining parameters are not selected in accordance with the Ti-6Al-4V alloy, the surface quality of workpieces does not meet technical requirements. Therefore, the appropriate machining parameters are needed to upgrade the surface roughness of workpieces in high-speed machining (HSM) conditions. The main cutting forces Py and Pz, and surface roughness Rz during turning of Ti-6Al-4V alloy in HSM were investigated and evaluated in this study. The response surface method (RSM) was used to conduct experiments and establish the models of Rz, Py, and Pz under various cutting conditions of feed rate (S), tool nose radius (R), and approach angle ($\phi$). As a result, the S value has the largest impact on the Pz. Moreover, the machining parameters such as S, R, and $\phi$ have a great influence on the Py. The magnitude of Rz is mainly affected by R value. Based on the optimal parameters, the average value of the force Pz has decreased from 238N to 169N, corresponding reduction of about 29%. The value of the force Py has reduced from 184N to 118N, corresponding reduction of about 36%. In addition, the surface roughness of the workpiece has been improved significantly from Rz of 5.6 $\mu$m to 2.7 $\mu$m under optimal parameters condition. The surface roughness of the workpiece after processing in high speed machining was reduced of 52% compared with cutting under non-optimal parameters condition. The best surface quality and minimum cutting force could be achieved under optimal processing parameters in high speed cutting method.

Machining quality depends on numerous factors such as speed, feed rate, quality of the materials, the cutting fluids used and so on. The quality of machining components can also be improved by using appropriate cutting fluids. In this study, the three different types of eco-friendly cutting fluids based on coconut oil with nano boric acid particles were synthesized with nanoadditives and characterized during the lathe-turning operation of mild steel. The obtained results were compared between the dry/plain turning (without the cutting fluid) and the turning with the cutting fluids like coconut oil and mineral oil with nanoparticles. In industries, a wide variety of cutting fluids are used; however, most of these cutting fluids are made up of synthetic materials which may affect the environment significantly. Hence, it is essential to develop eco-friendly cutting fluids for environmental sustainability. Here, the cutting fluids were characterized by the morphological study on nanoparticles (400nm) and the machined surface using scanning electron microscope (SEM), viscosity test, flash and fire point, surface roughness on machined part, tool tip-workpiece interface temperature, cutting force and flank wear measurement. The results showed that cutting fluids with 0.5% of boric acid had better performance.

This study focuses on optimization of cutting conditions and modeling of cutting force ($F_{\mathrm{\text{c}}}$), power consumption ($P$), and surface roughness ($R_{\mathrm{\text{a}}}$) in machining AISI 1040 steel using cutting tools with 0.4mm and 0.8mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds $V_{\mathrm{\text{c}}}$ (150, 210 and 270m/min), three different feed rates $f$ (0.12 0.18 and 0.24mm/rev), and constant depth of cut (1mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ($F_{\mathrm{\text{c}}}$) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ($F_{\mathrm{\text{c}}}$, $P$ and $R_{\mathrm{\text{a}}}$). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on $F_{\mathrm{\text{c}}}$, $P$ and $R_{\mathrm{\text{a}}}$. Then, the mathematical models for the output parameters ($F_{\mathrm{\text{c}}}$, $P$ and $R_{\mathrm{\text{a}}}$) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting $F_{\mathrm{\text{c}}}$ and $R_{\mathrm{\text{a}}}$, whereas the cutting speed is the most important factor affecting $P$. Moreover, the validation tests indicate that the system optimization for the output parameters ($F_{\mathrm{\text{c}}}$, $P$ and $R_{\mathrm{\text{a}}}$) is successfully completed with the Taguchi method at a significance level of 95%.

This paper presents the Toolox 44 steel’s experimental and statistical machinability optimizations conducted for achieving minimum output parameters (energy consumption ($E_c$), cutting force ($F_c$), surface roughness ($R_a$), and vibration (Vib)). Turning experiments have been performed under dry machining conditions on a CNC lathe at different cutting parameters according to the Taguchi L27 orthogonal array. Taguchi-based gray relational analysis (GRA) has also been used to optimize output parameters simultaneously. Moreover, analysis of variance (ANOVA) has been applied to determine the effects of cutting parameters on output parameters. As a result, the lowest Ec was measured 0.06 kW at the 1.5 mm depth of cut, 220 m/min cutting speed and 0.4 mm/rev feed rate. At 0.5 mm depth of cut, 220 m/min cutting speed and 0.1 mm/rev feed rate, the lowest $F_c$, $R_a$ and Vib are measured at 132.28 N, 0.69 $\mu$m and 2.25 m/s², respectively. According to GRA, the best machining combination in terms of energy consumption ($E_c$), cutting force ($F_c$), surface roughness ($R_a$), and vibration (Vib) was A1B3C1, and the percentage of improvement in the gray relational degree has been calculated to be 25.25%. Validation test results showed that the optimization of cutting parameters was successful in the machining process of Toolox 44 steel according to the multiple output parameter values.

In recent times, mechanical and production industries are facing increasing challenges related to the shift towards sustainable manufacturing. In this work, the machining was performed in dry cutting condition with the newly developed AlTiSiN-coated carbide inserts coated through scalable pulsed power plasma technique, and a dataset was generated for different machining parameters and output responses. The machining parameters are speed, feed and depth of cut, while the output responses are surface roughness, cutting force, crater wear length, crater wear width and flank wear. Abrasion and adhesion were found to be the two dominant wear mechanisms. With speed, the tool wear was found to increase. Cutting force was found to increase rapidly for higher speed ranges (70, 80 and 90m/min). But reduction of cutting force was observed in both low and medium ranges of cutting speed (40, 50, 55 and 60m/min). With higher values of feed and depth of cut, higher cutting force was also noticed. With the variation of depth of cut, the machined surface morphology and machined surface roughness were found to deteriorate. With higher feed and depth of cut values, more surface damages were observed compared to low values of feed and depth of cut. Both the feed rate and tool–chip contact length contributed significantly to the formation of crater on the tool rake surface. At high speed, continuous chips were observed. Both chip sliding and sticking were observed on the tool rake face. The data collected from the machining operation was used for the development of machine learning (ML)-based surrogate models to test, evaluate and optimize various input machining parameters. Different ML approaches such as polynomial regression (PR), random forests (RF) regression, gradient boosted (GB) trees and adaptive boosting (AB)-based regression were used to model different output responses in the hard machining of AISI D6 steel. Out of the four ML methodologies, RF and PR performed better in comparison to the other two algorithms in terms of the $R^2$ values of predictions. The surrogate models for different output responses were used to prepare a complex objective function for the germinal center algorithm-based optimization of the machining parameters of the hard turning operation.

In this work, initially, the raw AISI 52100 bearing steel was heat-treated to obtain 40 HRC and 45 HRC workpiece hardness. Further, dry hard turning tests were carried out to study the impact of workpiece hardness ($H$), cutting speed ($v$), feed ($f$), and depth of cut ($a$) on cutting force (Fy), surface roughness (Ra), and sound intensity (SI). An economically viable PVD-coated carbide turning tool was implemented for the experiments. The Taguchi L${}_{18}$ (2–3 mixed level) design of experiments was employed to establish the experimental plan in order to save the experimental time, energy, and cost of manufacturing. The results disclosed that the feed has the prevailing consequence on surface roughness with a 96.3% contribution, while it also significantly affects the cutting force with a contribution of 13.8%. The contribution of cutting speed and workpiece hardness on the cutting force was reported as 48.3% and 35.1%, respectively. Higher workpiece hardness required more energy for plastic deformation as a result the cutting force increases with leading hardness. The sound intensity was dominantly influenced by depth of cut (53.3%) and cutting speed (40%). Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was performed to determine the optimum machining parameters. According to the TOPSIS, the optimum level of cutting parameters was predicted as 40 HRC hardness ($H$), 150m/min cutting speed ($V$), 0.15mm/rev feed ($f$), and 0.1mm depth of cut ($a$) while the optimal result of Fy, SI, and Ra were noted as 27.66N, 70.7dB, and 0.86$\mu$m individually.

This study focused on determining the effects of the cooling condition in the conventional and ultrasonic-assisted turning of AISI 52100 steel. AISI 52100 steel has been widely used in the bearing, mold, and automotive industries because of its superior wear resistance and mechanical properties. However, its high wear resistance caused poor machinability in conventional machining methods. Low surface quality, rapid tool wear, high cutting temperature, and high cutting force are the main difficulties in the conventional machining of AISI 52100 steel. On the other hand, ultrasonic-assisted machining has become widely used in the past decades for the machining of materials that have low machinability in conventional methods. Conventional turning and ultrasonic-assisted turning were selected as machining methods. Ultrasonic-assisted turning experiments were conducted under 20 and 30 kHz vibration frequencies. Cubic Boron Nitride (CBN) cutting tools were used in machining experiments. In machining operations 50, 100, and 150 m/min cutting speeds were selected, and feed rate (0.1 mm/rev) and depth of cut (0.5 mm) were kept constant in experiments. Minimum quantity lubrication technique which used 1% Al₂O₃ nanoparticle additives cutting fluid and dry cooling methods were used. CBN insert was used as a cutting tool. The effects of cutting speed, cutting method, and cooling condition on surface quality, cutting force, cutting zone temperature, and cutting tool wear were studied experimentally. Analysis of Variance (ANOVA) was carried out to determine the significance of the effects of input variables on process outputs. As a result of this study, it is determined that combined UAT and nanoMQL have significant effects on the process outputs.

In this paper, a numerical procedure for obtaining a cutting performance "signature" for a bur is proposed. This procedure is applied to a comparative analysis of the cutting performance of a surgical bur under various operating conditions. An experimental method, which uses a constant feed rate and measures feed force, is utilized to perform tests leading to the data needed for the aforementioned procedure. Several proof-of-concept tests are performed and their results are presented. The proposed approach shows capability for tracking the cutting performance of a bur as it gets duller.

The rotational motion has been utilized in several medical needle technologies to enhance the capability of cutting tissue. The needle rotation helps significantly reduce the tissue cutting force, which improves procedure outcome and pain. However, the needle rotation can also incur tissue winding that intensifies tissue damage, which results in complications of bleeding and hematoma. Some histological observations showed that bidirectional needle rotation could reduce the tissue damage caused by tissue winding. In this study, we established a cohesive surface based finite element model to evaluate the cutting force in needle insertion with unidirectional and bidirectional rotation. The simulation results suggested that the frequency of switching direction of needle rotation insignificantly influences the cutting force. The Latin Hypercube method was used to generate a response surface of cutting force and locate the minimum at the insertion speed of 1mm/s combined with the slice/push ratio of 1.9. In clinical use, we suggested that the needle speeds can be first selected to optimize the cutting force according to the type of target tissue. If the desired needle rotation is high, a proper switching frequency can be applied to reduce the tissue winding damage without increasing the cutting force.

Various cutting fluids are available in the cutting fluid market to provide good machining performances for metal cutting industries. Incidentally, most of the cutting fluids are synthetic and semisynthetic in nature, and although they are beneficial to the industries, they are posing health and environmental issues. Even if these cutting fluids have sufficient properties required for good machining, the major constraints associated with these fluids are their nature of nonbiodegradability and nonfriendliness to the environment. To overcome these difficulties, intense research is carried out to develop biodegradable and effective cutting fluids. In this research, a novel castor oil-based cutting fluid infused with nanomolybdenum (MoS₂) particles has been developed and its various machining properties have been investigated. Various important cutting parameters like surface roughness, tool life, and cutting force were investigated using this newly developed biodegradable nanofluid as a cutting fluid. Comparative experimental studies have also been undertaken with sunflower oil blend and conventional synthetic oil. Observed results validated that the newly developed castor oil-based nanofluid improves the surface finish and tool life by minimizing the cutting force developed to the considerable extent.

In the automotive industry, sealing quality between two flat joint surfaces is directly affected by the surface flatness. To know how much flatness is caused by machining operations, a traditional trial-and-error method has been used. The prediction for machined surface error/distortion can help to assess the integrity of the structural design as well as develop fixturing scheme to optimize machining quality. In this paper, a finite element method is applied to extract the compliance matrix of milling surface of a workpiece, such as the cylinder deck face, and an encoding MatLab program is used to compute the flatness due to milling forces. The paper focuses on deriving analytical models for evaluating the flatness of the cylinder deck face and optimizing the manufacturing process. Some special considerations have been taken to manufacturing cutting force evaluations according to analysis results of the deck face flatness. Emphasis is also placed on the optimization of machining parameters by iterations of flatness results so that minimization of surface deformations under machining loads can be achieved. The methodology introduced in the paper is the closed-loop iteration by combining structural finite element analysis (FEA) simulation, tooling kinematic simulation, and MatLab data modeling.

A series of experiments were conducted to study the performance of a coated cemented carbide tool in high speed milling of Ti-6Al-4V alloy. Experimental measurements of three components of the cutting forces were performed by using a three-component dynamometer. The cutting temperature was measured by using an infrared thermal imager. The variation of cutting forces and cutting temperature with the cutting parameters are investigated. The influence of cutting speed, axial depth of cut, and feed rate on the cutting forces and cutting temperature are analyzed and discussed. The wear patterns of the tool were investigated using scanning electron microscope (SEM) and analysis of energy spectrum, and the wear mechanism is discussed. It is found that abrasive wear and adhesive wear are the dominant wear mechanism of the tool.