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Background: Due to its qualities, There is a lot of use of the titanium alloy (Ti-6Al-4V) in gas turbines and other aero engines. It is difficult to determine machining parameters such cutting force, temperature, and surface roughness, and it is difficult to calculate these values using analytical methods. The finite element method (FEM) is a particularly useful platform for studies since it predicts the machining parameters. Methods: The advantage of this method is taken for the purpose of linking the mechanical and thermal equations based on a step of the dynamic, temperature-displacement, explicit of the Lagrangian formulation in a new model that is fully thermomechanically connected. Three mesh areas were created for optimizing the cutting zone during the cutting simulation process. The machining process by using a face milling tests was carried out. There are two variable factors of cut such as feed rate, cutting speed are created randomly when depth is a constant parameter. High-speed camera used to capture the machining process which determines the important details of spark generated. Findings and interpretation: There is a very excellent agreement between the experimental data and the simulation results from the finite element modeling (FEM). By raising the feed rate in the cutting zone, the cutting temperature can be raised and a spark can be generated. This led to the conclusion that surface roughness trends can be predicted using feed cutting force measurements. Surface roughness rose in direct proportion to the magnitude of the feed cutting force, and vice versa.
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The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.
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The article presents the results and process analysis of the face milling of aluminium alloy 2017A with the CoroMill 490 tool on an AVIA VMC 800 vertical milling centre. The study analysed the effects of the cutting speed, the feed rate, the actual number of teeth involved in the process, the minimum thickness of the cut layer (hmin), and the relative displacement in the tool-workpiece system D(ξ) on the surface roughness parameter Ra. To measure relative displacement, an original bench was used with an XL-80 laser interferometer. The analysis of relative displacement and surface roughness allowed these factors to be correlated with each other. The purpose of this article is to determine the stable operating ranges of the CoroMill 490-050Q22-08M milling head with respect to the value of the generated relative displacement w during the face-milling process and to determine its influence on surface roughness. The research methodology presented in this paper and the cutting tests carried out allowed the determination of the optimum operating parameters of the CoroMill 490-050Q22-08M tool during the face milling of aluminium alloy 2017A, which are vc 300 m/m and fz0.14 mm/tooth. Working with the defined cutting parameters allows all the cutting inserts in the tool body to be involved in shaping the geometrical structure of the surface, while maintaining a low vibration level D(ξ) > 1 µm, a low value of the parameter hmin > 1.5 µm, and the desired value of the parameter Ra > 0.2 µm
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In this study, sintered silicon carbide is machined on a high-precision milling machine with a high-speed spindle, closed-loop linear drives and friction-free micro gap hydrostatics. A series of experiments was undertaken varying the relevant process parameters such as feedrate, cutting speed and chip thickness. For this, the milled surfaces are characterized in a process via an acoustic emission sensor. The milled surfaces were analyzed via confocal laser scanning microscopy and the ISO 25178 areal surface quality parameters such as Sa, Sq and Smr are determined. Moreover, scanning electron microscopy was used to qualitatively characterize the surfaces, but also to identify sub-surface damages such as grooves, breakouts and pitting. Raman laser spectroscopy is used to identify possible amorphization and changes to crystal structure. We used grazing incidence XRD to analyze the crystallographic structure and scanning acoustic microscopy to analyze sub-surface damages. A polycrystalline diamond tool was able to produce superior surfaces compared to diamond grinding with an areal surface roughness Sa of below 100 nm in a very competitive time frame. The finished surface exhibits a high gloss and reflectance. It can be seen that chip thickness and cutting speed have a major influence on the resulting surface quality. The undamaged surface in combination with a small median chip thickness is indicative of a ductile cutting regime.
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The paper presents an original method concerning the problem of vibration reduction in the general case while milling large-size and geometrically complex details with the use of an innovative approach to the selection of spindle speed. A computational model is obtained by applying the so-called operational approach to identify the parameters of the workpiece modal model. Thanks to the experimental modal analysis results, modal subsystem identification was performed and reliable process data for simulation studies were obtained. Next, simulations of the milling process, for successive values of the spindle speed, are repeated until the best vibration state of the workpiece is obtained. For this purpose, the root mean square values of the time plots of vibration displacements are examined. The effectiveness of the approach proposed for reducing vibrations in the process of face milling is verified on the basis of the results of appropriate experimental investigations. The economic profitability of the implementation of the operational technique in the production practice of enterprises dealing with mechanical processing is demonstrated as well.
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Lightweight alloys made from aluminium are used to manufacture cars, trains and planes. The main parts most often manufactured from thin sheets requiring the use of milling in the manufacturing process are front panels for control systems, housing parts for electrical and electronic components. As a result of the final phase of the manufacturing process, cold rolling, residual stresses remain in the surface layers, which can influence the cutting processes carried out on these materials. The main aim of this study was to verify whether the strategy of removing the outer material layers of aluminium alloy sheets affects the surface roughness after the face milling process. EN AW-6082-T6 aluminium alloy thin plates with three different thicknesses and with two directions relative to the cold rolling process direction (longitudinal and transverse) were analysed. Three different strategies for removing the outer layers of the material by face milling were considered. Noticeable differences in surface roughness 2D and 3D parameters were found among all machining strategies and for both rolling directions, but these differences were not statistically significant. The lowest values of Ra = 0.34 µm were measured for the S#3 strategy, which asymmetrically removed material from both sides of the plate (main and back), for an 8-mm-thick plate in the transverse rolling direction. The highest values of Ra = 0.48 µm were measured for a 6-mm-thick plate milled with the S#2 strategy, which symmetrically removed material from both sides of the plate, in the longitudinal rolling direction. However, the position of the face cutter axis during the machining process was observed to have a significant effect on the surface roughness. A higher surface roughness was measured in the areas of the tool point transition from the up-milling direction to the down-milling direction (tool axis path) for all analysed strategies (Ra = 0.63-0.68 µm). The best values were obtained for the up-milling direction, but in the area of the smooth execution of the process (Ra = 0.26-0.29 µm), not in the area of the blade entry into the material. A similar relationship was obtained for analysed medians of the arithmetic mean height (Sa) and the root-mean-square height (Sq). However, in the case of the S#3 strategy, the spreads of results were the lowest.
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High feed Milling is a new milling method, which allows to apply high feed rates and increase machining efficiency. The method utilizes face cutters with a very small entering angle, of about 10°-20°. Thus, the cut layer cross-section is different than in traditional milling. In order to examine the high feed milling (HFM), experimental tests were conducted, preceded by an analysis of cutting zones when milling with an HF face cutter. The face milling tests of 42CrMo4 steel with the use of an HF cutter characterized by an entering angle, dependent on axial depth of cut ap and insert radius r values, as well as with a conventional face cutter with the entering angle of 45° were performed. The study focused on analyzing the vibration amplitude, cutting force components in the workpiece coordinate system, and surface roughness. The experimental tests proved, that when milling with constant cut layer thickness, the high feed cutter allowed to obtain twice the cutting volume in comparison with the conventional face cutter. However, higher machining efficiency resulted in an increase in cutting force components. Furthermore, the results indicate significantly higher surface roughness and higher vibration amplitudes when milling with the HF cutter.
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This article analyzes the temperature data obtained for an aluminum alloy face milled using four different cutting strategies. The workpiece temperature was measured at six points with K-type thermocouples. The heat transfer taking place in the cutting zone was also simulated numerically using the finite element method (FEM) and the finite difference method (FDM). The calculation results concerning the distribution of temperature on the workpiece surface were compared with the experimental data. The numerically simulated distribution of temperature on the workpiece surface after face milling was considered in relation to the surface flatness. The findings suggest that the flatness deviations at the workpiece ends were dependent on the depth of cut. Another reason was the cutting strategy selected for the specific thermophysical properties of the workpiece material. Measurement of the workpiece temperature is extremely important because of the thermoelastic behavior and thermal expansion of the material. The isotropic properties of the aluminum alloy make it expand in all directions during milling.
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The paper studies the potential to improve the surface roughness in parts manufactured in the Selective Laser Melting (SLM) process by using additional milling. The studied process was machining of samples made of the AlSi10Mg alloy powder. The simultaneous impacts of the laser scanning speed of the SLM process and the machining parameters of the milling process (such as the feed rate and milling width) on the surface roughness were analyzed. A mathematical model was created as a basis for optimizing the parameters of the studied processes and for selecting the sets of optimum solutions. As a result of the research, surface with low roughness (Ra = 0.14 µm, Rz = 1.1 µm) was obtained after the face milling. The performed milling allowed to reduce more than 20-fold the roughness of the SLM sample surfaces. The feed rate and the cutting width increase resulted in the surface roughness deterioration. Some milled surfaces were damaged by the chip adjoining to the rake face of the cutting tool back tooth.
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The contemporaneous examination focuses on the impact of spindle speed as well as an eco-pleasing cooling strategy in the midst of processing of AISI316. Investigations were executed at three different machining approaches viz. dry, wet and cryogenic (LN2) using cemented carbide inserts. Water dissolvable oil was used as a cutting fluid for flood cooling approach. The workpiece was processed under three distinctive cutting speeds i.e. 1000, 2000 and 3000 rpm however feed rate and depth of cut were kept consistent at 450 mm/min and 1mm separately. An exhaustive investigation on the cooling impacts of LN2 technique on a segment of the significant machinability perspective, for instance, cutting force (Fx), insert wear, surface quality and processing temperature is delineated. The beforehand specified processing responses were documented and contrasted all together to demonstrate the reasonableness and achievability of LN2 approach in examination with dry and wet machining methodology. The outcomes accomplished in the midst of the examination obviously settled the commonness of realizing LN2 for achieving upgraded machinability inside a predetermined scope of process parameters.
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It is hypothesized that the orientation of tool maneuvering in the milling process defines the quality of machining. In that respect, here, the influence of different path strategies of the tool in face milling is investigated, and subsequently, the best strategy is identified following systematic optimization. The surface roughness, material removal rate and cutting time are considered as key responses, whereas the cutting speed, feed rate and depth of cut were considered as inputs (quantitative factors) beside the tool path strategy (qualitative factor) for the material Al 2024 with a torus end mill. The experimental plan, i.e., 27 runs were determined by using the Taguchi design approach. In addition, the analysis of variance is conducted to statistically identify the effects of parameters. The optimal values of process parameters have been evaluated based on Taguchi-grey relational analysis, and the reliability of this analysis has been verified with the confirmation test. It was found that the tool path strategy has a significant influence on the end outcomes of face milling. As such, the surface topography respective to different cutter path strategies and the optimal cutting strategy is discussed in detail.