Wet Machining Conditions Research Articles

INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut. The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.

In process temperature and tool wear for the machining of aeronautic aluminum under different lubrication conditions

The use of aluminum-lithium alloys in aeronautics is an efficient solution for lightweight structures, but its machinability can cause increased tool wear. The cutting temperature is one of the most important parameters controlling the tool wear and the quality of machined surfaces. Therefore, its measurement is of great interest to analyze the heat generation during the cut and then being able to limit it in the cutting edge. This article presents an original experimental study of the cutting temperature of two aluminum alloys (7000 series and Li-containing 2000 series), for dry, MQL: Minimum Quantity of Lubrication and wet machining conditions. The interactions between tool wear and cutting temperature are also investigated. To be able to observe tool wear, the milling of very large volumes of aluminum alloys has been necessary since the tool lifetime is several hours when machining this kind of material. To measure temperature as close as possible the cutting zone, an original set-up with machinable thermocouples was used, enabling measurements on a large frequency bandwidth. Complementary investigations by Second Ion Mass Spectroscopy revealed a diffusion of lithium in the cutting insert, which can reduce its lifetime (divided by 8, with the Li-containing 2000 series compared to the 7000 series).

Cooling Capacity of Oil-in-Water Emulsion under wet Machining Conditions

Many industrial machining operations are carried out under wet machining conditions. Modelling and simulating fluid-structure-interactions and conjugate heat transfer are still a challenge nowadays. In this paper, temperature dependent heat transfer coefficients (HTC) h(T) are experimentally estimated for wet machining-like conditions in a jet cooling experiment. The transient temperature is thereby used to solve an Inverse Heat Transfer Problem for HTC function estimation. Determined HTC are applied as input in related jet cooling simulation using the Finite-Pointset-Method (FPM) to validate the modeling approach. Additionally, wet cutting simulations numerically highlight the influence of determined HTC h(T) on turning.

Evaluation of Machinability on duplex stainless steel 2205 under dry and Wet condition

The examination of machinability in terms of surface roughness on duplex stainless steel under dry and wet machining conditions with a TiAlN coated solid carbide drill is presented in this paper. The experiments were carried out utilizing a CNCNC milling machine for drilling and a Taguchi design L8 set of orthogonal array(OA) for parametric optimization. Following the experimental optimization, it was discovered that dry machining produces a better surface quality than wet machining, with an R2 value of 0.990 for surface roughness. Finally, it was discovered that dry machining produces a superior surface finish than wet machining, and that the ideal process parameter for surface roughness (Ra) is a dry machining environment, a high spindle speed (900 RPM), and a low feed rate (0.03). After confirmation test were conducted for validation of experimental results and it was observed that experimental results and model results are close to each other.

Investigation on effect of TiN, TiAlN & DLC-triple layer coated carbide tool in machining of Al-Si 4032 alloy

Aluminium is a widely used metal in the world due to its abundance and easy to work with properties. One of the major users of aluminium are the auto and aero industries, since it reduces the weight of the vehicle significantly. The properties of aluminium can be customized with certain alloying components and there are plenty of combinations, among which Aluminium 4032 alloy is taken for this investigation. This Al -Si 4032 alloy is enriched in silicon content up to 13%. High presence of silicon leads to more amount of friction in machining hence the machining of this particular alloy has to be done with coated materials for enhanced results. The machining is carried out with the carbide inserts triple layer coated with Titanium Nitride (TiN), Titanium Aluminium Nitride (TiAlN) and Diamond like Carbon (DLC). These coated inserts are used to machine Al-Si alloy workpiece under various machining variables of speed, feed, depth of cut as well as in both dry and wet environments. Various parameters are premeditated from the experiments like surface roughness, tool wear, tool life and material removal rate. And the responses were recorded, optimization process is carried out with Minitab software tool to find the finest combinations for machining. The better results are found to be in wet environment machining condition.

Theoretical and experimental study of the chatter vibration in wet and MQL machining conditions in turning process

Turning is one of the most commonly used cutting processes for manufacturing components in production engineering. The turning process, in some cases, is accompanied by intense relative movements between tool and workpiece, which is called chatter vibrations. Chatter has been identified as a detrimental problem that adversely impacts surface finish, tool life, process productivity, and dimensional accuracy of the machined part. Cooling/Lubrication in the turning process is normally done for some reasons, including friction and force reduction, temperature decrement, and surface finish improvement. Wet cooling is a traditional cooling/lubrication process that has been used in machining since the past. Besides, a variety of new cooling and lubricating approaches have been developed in recent years, such as the minimum quantity lubrication (MQL), cryogenic cooling, nanolubrication, etc., due to ecological issues. Despite the importance of cooling/lubrication in machining, there is a lack of research on chatter stability in the presence of cutting fluid in cutting processes. In this study, the chatter vibration in turning process for two cooling/lubrication conditions of conventional wet and MQL is investigated. An integrated theoretical model is used to predict both the metal cutting force and the chatter stability lobe diagram (SLD) in turning process. This model involves deriving a math equation for predicting metal cutting force for both wet and MQL conditions using experimental training force data and a Genetic Expression Programming (GEP)-based regression model. Also, the traditional single degree of freedom chatter model is used here for predicting the SLDs. The chatter model is discussed and verified with experimental tests. Then, the experimental results of the tool's acceleration signal, work surface texture, surface roughness, chip shape, and tool wear are presented and compared for wet and MQL conditions. The results of this study show that the cooling/lubrication systems such as wet or MQL have a considerable effect on the SLDs. Also, the predicted results of metal cutting force and SLD for both wet and MQL techniques are in good agreement with the experimental data. Therefore, it is recommended that for each lubrication condition including wet, or MQL, the SLD be determined to achieve higher machinability.

Influence of Support Vector Regression (SVR) on Cryogenic Face Milling

The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2‐liquid nitrogen). Furthermore, “one parametric approach” was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is “multiple linear regression.” Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid‐bias (BNN‐SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN‐SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid‐bias (BNN‐SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.

Cutting Forces and Chip Shaping When Finish Turning of 17-4 PH Stainless Steel under Dry, Wet, and MQL Machining Conditions

This paper analyses three components of total cutting force and chip shape changes when finish turning 17-4 PH (precipitation hardening) stainless steel. A Finite Element Method (FEM) simulation of cutting forces was also performed using the Johnson–Cook constitutive model. The results were compared with those obtained from experimental studies. Variable feeds of 0.05–0.4 mm/rev and depth of cut of 0.2–1.2 mm with a cutting speed of 220 m/min were used. The studies were carried out under dry and wet cooling conditions and with the use of minimum quantity lubrication (MQL). This research was realized based on the Parameter Space Investigation (PSI) method. Statistical analysis of the obtained results was carried out using Statistica-13 software. It was found that the cutting force Fc and feed force Ff depend on the depth of cut and feed, and the passive force Fp depends mainly on the feed. Compared to dry cutting conditions, a reduction of 43% and 39% of the cutting force Fc was achieved for wet machining and MQL machining, respectively. Regardless of the cooling conditions, a favorable chip shape was registered for ap = 1–1.1 mm and f = 0.25–0.3 mm/rev. Compared to the experimental studies, the FEM simulation showed differences of ~13% for the cutting force Fc and of ~36% for the feed force Ff.

Experimental investigation of cryogenic end milling on maraging steel using cryogenically treated tungsten carbide-cobalt inserts

The cryogenic machining and cryogenic treatment have already emerged as the sustainable manufacturing process of the future generation. The cryogenic treatment improves the cutting tool life, but the high cutting temperature developed during high-speed machining reduces the effect of cryogenic treatment of cutting tool. This study investigates the possible improvements in cutting tool life by combining cryogenic cooling and cryogenic treatment. The authors believe that these two techniques can replace conventional machining approaches using dry and wet machining conditions using coated carbide tools. The tungsten carbide-cobalt inserts are cryogenically treated (CT) at a soaking temperature of − 195.8 °C for a period of 24 h and are used to evaluate milling performance under dry, wet, and cryogenic cutting environments. The machining experiments are conducted on maraging steel MDN 250 using one factor at a time approach by varying spindle speed and keeping feed rate and depth of cut as constant. The cutting force, surface roughness, tool wear, and subsurface microhardness are some of the machining responses evaluated and compared with an untreated cutting tool (UT). The tool life improved up to 24% during cryogenic machining using CT tools at a spindle speed of 270 rpm.

COMPLEXITY-BASED ANALYSIS OF THE INFLUENCE OF MACHINING PARAMETERS ON THE SURFACE FINISH OF DRILLED HOLES IN DRILLING OPERATION

Drilling is a famous and widely used machining operation to make holes in the workpiece. The size and surface quality of drilled hole are two factors that should be considered mainly. In this research, we examine the effect of different machining parameters and conditions on the surface quality of generated hole in drilling operation. For this purpose, we employ fractal theory and investigate how the variations of depth of cut and spindle speed affect the complexity of surface texture of drilled holes in wet and dry machining conditions. Based on the obtained results, the increment of depth of cut and spindle speed in case of wet and dry machining causes lower complexity on the generated surface from drilling. In addition, the generated surface from dry machining is more complex than the generated surface from wet machining. The obtained method in this research can be applied to other machining operations in order to investigate the effect of machining parameters and conditions on the surface quality of machined workpiece.

COMPLEXITY-BASED DECODING OF THE EFFECT OF MACHINING PARAMETERS ON THE MACHINED SURFACE IN MILLING OPERATION

Acquiring the desired surface quality is one of the major efforts in machining of materials. Milling operation is a widely used machining operation to shape the material in different forms. Machining parameters and conditions are two major factors that affect the surface quality of machined workpiece in milling operation. In this paper, we analyze the surface finish of machined workpiece under the variations of machining parameters and conditions (wet and dry conditions) in milling operation. For our analysis, we use fractal dimension as the indicator of complexity of structure. Based on the obtained results, in the case of wet machining condition, by increasing the depth of cut, feed rate and spindle speed in separate experiments, the fractal dimension of machined surface increases. However, the obtained results in the case of dry machining condition are not consistent with the variations of different machining parameters. The obtained results will be discussed in terms of complex structure of machined surface. The method of analysis employed in this research can be investigated with other machining operations to check how the machining parameters and conditions affect the surface quality of machined surface.

Effect of Machining Parameters on Tool Wear and Surface Roughness In Dry and Wet Machining of Aisi 316 Austenitic Stainless Steel by Taguchi Method

Stainless steels are widely used to manufacture mechanical components due to excellent mechanical properties. Machining is considered as one of the most critical manufacturing processes in mechanical industry to produce desired shapes and dimensional accuracy of the components. It also affects the performance of the components in its functional requirement. This paper deals with the optimization of cutting parameters in machining operation for AISI 316 austenitic steel with dry and wet environment conditions. The chosen machining parameters in this research are cutting speed, feed rate, and depth of cut as input variables, whereas the response factors are surface roughness and wear rate. Taguchi method with the L9 orthogonal array was used to analyze the process parameters based in dry and wet machining conditions. The Taguchi approach provides the best setting with lower values of surface roughness and wear rate. The regression analysis is performed to obtain a mathematical model of responses in terms of the process parameter. The composite regression optimization gives best setting for dry condition (cutting speed 173 rpm, feed 0.25 mm/rev, and 0.87 mm of the depth of cut) and for wet condition (cutting speed 173 rpm, feed 0.3 mm/rev, and 0.57 mm of the depth of cut). The results show that surface roughness and wear rate are lower in the wet environment than the dry environment.

FRACTAL-BASED ANALYSIS OF THE EFFECT OF MACHINING PARAMETERS ON SURFACE FINISH OF WORKPIECE IN TURNING OPERATION

Analysis of workpiece surface quality is one of the major issues in manufacturing engineering. Turning operation is a famous machining operation that is widely used in machining of materials. In this research, we investigate the surface finish of machined workpiece from turning operation. For this purpose, we employ fractal theory to study the complex structure of machined workpiece’s surface in different conditions. The applied parameters include the variations of cutting depth, feed rate and spindle speed in wet and dry machining conditions. Based on the obtained results, we found the correlation between the increment of fractal dimension of machined surface and the increment of cutting depth, feed rate and spindle speed in wet machining condition. The obtained results will be discussed in relation with the complexity of machined surface. The employed method of analysis in this research can be widely applied to the analysis of the effect of different machining parameters and conditions on the surface quality of machined workpiece in case of different machining operations.

DECODING OF THE RELATION BETWEEN FRACTAL STRUCTURE OF CUTTING FORCE AND SURFACE ROUGHNESS OF MACHINED WORKPIECE IN END MILLING OPERATION

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.

ANALYSIS OF THE CORRELATION BETWEEN FRACTAL STRUCTURE OF CUTTING FORCE SIGNAL AND SURFACE ROUGHNESS OF MACHINED WORKPIECE IN END MILLING OPERATION

Analysis of the machined surface is one of the major issues in machining operations. On the other hand, investigating about the variations of cutting forces in machining operation has great importance. Since variations of cutting forces affect the surface quality of machined workpiece, therefore, analysis of the correlation between cutting forces and surface roughness of machined workpiece is very important. In this paper, we employ fractal analysis in order to investigate about the complex structure of cutting forces and relate them to the surface quality of machined workpiece. The experiments have been conducted in different conditions that were selected based on cutting depths, type of cutting tool (serrated versus. square end mills) and machining conditions (wet and dry machining). The result of analysis showed that among all comparisons, we could only see the correlation between complex structure of cutting force and the surface roughness of machined workpiece in case of using serrated end mill in wet machining condition. The employed methodology in this research can be widely applied to other types of machining operations to analyze the effect of variations of different parameters on variability of cutting forces and surface roughness of machined workpiece and then investigate about their correlation.

FRACTAL-BASED ANALYSIS OF THE VARIATIONS OF CUTTING FORCES ALONG DIFFERENT AXES IN END MILLING OPERATION

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 [Formula: see text]-axis) has greater fractal dimension than radial force (along [Formula: see text]-axis). In addition, the radial force (along [Formula: see text]-axis) has greater fractal dimension than thrust force (along [Formula: see text]-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.

FRACTAL-BASED ANALYSIS OF THE INFLUENCE OF CUTTING DEPTH ON COMPLEX STRUCTURE OF CUTTING FORCES IN ROUGH END MILLING

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.

Experimental investigation on hard turning of AISI 4340 steel using cemented coated carbide insert

Hard turning is a developing technology that offers many potential advantages compared to grinding, which remains the standard finishing process for critical hardened surfaces. In this work, an attempt has been made to experimentally investigate hard turning of AISI 4340 steel under wet and dry condition using cemented coated carbide insert. Hardness of the workpiece material is tested using Brinell and Rockwell hardness testers. CNC LATHE and cemented coated carbide inserts of designation CNMG 120408 are used for conducting experimental trials. Significant cutting parameters like cutting speed, feed rate and depth of cut are considered as controllable input parameters and surface roughness (Ra), tool wear are considered as output response parameters. Design of experiments is carried out with the help of Taguchi’s L9 orthogonal array. Results of response parameters like surface roughness and tool wear under wet and dry condition are analysed. It is found that surface roughness and tool wear are higher under dry machining condition when compared to wet machining condition. Feed rate significantly influences the surface roughness followed by cutting speed. Depth of cut significantly influences the tool wear followed by cutting speed.

An Experimental Study of Surface Roughness Variation in End Milling of Super Duplex 2507 Stainless Steel

This study focuses on surface roughness evaluation of SUPER DUPLEX 2507 stainless steel in dry and wet machining condition. Dryand wet milling experiments were conducted with three levels of cutting speed, feed rate and constant axial depth of cut. The effects of these cutting parameters on the surface roughness in dry and wet machining condition are evaluated. The multiple regression analysis has been applied to find the relationship between surface roughness and cutting parameters. The regression equation revealed that the feed rate is the most dominant factor that influences the surface roughness followed by the cutting speed. It is also concluded that surface finish obtained in wet machining is much better compared to dry machining.

Machining and Machinability of Tool Steels: Effects of Lubrication and Machining Conditions on Tool Wear and Tool Life Data

The machinability of materials depends on various factors including the workpiece materials and condition, the cutting tool data and the machining conditions (wet or dry). Most tool life data found in literature are established using dry machining conditions. This makes it difficult to make suitable comparison with wet machining conditions, to compute cost effective machining conditions or to simulate the machining processes, especially for newly developed alloys. In the present paper, the machinability of tool steels is investigated using dry and wet milling process. Tools steels with different compositions and hardness were milled using carbide inserts and the tool wear/tool life performance studied. The coefficients of the Taylor model of tool life were established for each steel and then used to compute the economically speeds and the maximum productivity speeds. It was found that the dry milling is more advantageous for the tested steels, especially for roughing operations, but the tool life data in dry machining conditions was very sensitive to materials compositions and hardness compared to wet machining conditions.