Milling Depth Research Articles

Research on distresses detection, evaluation and maintenance decision-making for highway pavement in reconstruction and expansion project

Artificial walking survey, ground penetrating radar (GPR) and falling weight deflectometer (FWD) were used to test the actual condition of the highway pavement in reconstruction and expansion project for better maintenance decision-making. Therefore, a decision-making system with the application of four evaluation indexes including deflection value (DV), pavement surface condition within 20 m(PCI20), maintenance effect (ME) and internal crack rate (ICR) was constructed. Through the pavement empirical-mechanical calculation and the Delphi method analysis, the corresponding threshold for the DV, PCI20 and ICR were 27 (0.01 mm), 80, and 20 m/100 m2 respectively. Then, the decision-making system was applied for the 5.5 km section with the detection and evaluation results, and the advised maintenance plans of different lanes were determined. Lastly, the effect of the decision-making system was analyzed. Results show that the decision-making system can scientifically decide the treatment plans and save the maintenance cost by about 30.1 %. The deflection near the grouting holes significantly decreases resulting from the high-density cement slurry distribution. The milling and repaving have a high structural reinforcement ability of reducing deflection up to more than 66 %, which is positively related to the milling depth.

Multi-Response Optimization Analysis of the Milling Process of Asphalt Layer Based on the Numerical Evaluation of Cutting Regime Parameters

The present study aimed to optimize the process parameters (milling depth and advanced speed) for an asphalt milling operation using a multi-response approach based on Taguchi design of experiments (DOE) and Grey Relational Analysis (GRA). Nine simulations tests were conducted using Discrete Element Method (DEM) in order to determine the forces acting on the cutting tooth support and tip. The considered performance characteristics were cutting forces (smaller is better category) and chip section area (larger is better category). A Grey Relational Grade (GRG) was determined from GRA, allowing to identify the optimal parameter levels for the asphalt milling process with multiple performance characteristics. It was found that that the optimal milling parameters for multi-response analysis are a milling depth of 200 mm and an advanced speed of 30 mm/min. Furthermore, analysis of variance (ANOVA) was used to determine the most significant factor influencing the performance characteristics. The analysis results revealed that the dominant factor affecting the resultant cutting force was milling depth, while the main factor affecting chip section area was the advanced speed. Optimizing milling efficiency is essential in machining operations. A key factor in this direction is comprehending the interplay between chip removal and cutting forces. This understanding is fundamental for achieving increased productivity, cost-effectiveness, and extended tool lifespan during the milling process.

Production of Workpieces from Martensitic Stainless Steel Using Electron-Beam Surfacing and Investigation of Cutting Forces When Milling Workpieces.

The aim of this study was to investigate cutting force when milling 40 × 13 stainless steel samples obtained via electron-beam surfacing. The samples were obtained by surfacing the wire made from the martensitic 40 × 13 stainless steel. The microstructure of the samples and the hardness are discussed in the present study. Emphasis is placed on the study of cutting forces when handling the samples. The structure of the samples obtained by electron-beam surfacing consisted of tempered martensite. The average hardness of the samples was similar to the hardness obtained after quenching and tempering the samples-576 HV for horizontally printed workpieces and 525 HV for vertically printed workpieces. High-speed milling, high-efficiency milling, and conventional milling have been proven to be suitable for handling such workpieces. This study shows that an increase in milling width leads to a gradual decrease in specific cutting force. As the milling depth increases, the specific cutting force decreases intensively at first but then more slowly with time. Machining the workpieces made of the martensitic stainless steel and produced by electron-beam surfacing requires the use of purely carbide mills with a diameter of at least 12 mm. Using a high-speed steel as a tool material results in the rapid failure of the tool. The cutting conditions during the investigation allowed for a decrease in the temperature of the cutting edge, cutting force, and the low-rigid end mill bending. Therefore, this study has made it possible to select modes that allow for a reduction in the vibration of the lathe-fixture-tool-part system.

Analysis and Optimization of Milling Deformations of TC4 Alloy Thin-Walled Parts Based on Finite Element Simulations

TC4 (DIN3.7164/5) alloy thin-walled parts are widely used in aviation and aerospace industries. However, due to their special structure, shape and poor machinability, large milling forces and milling deformation often occur in the milling process, which cannot guarantee the machining quality and accuracy. The milling processing parameters and milling geometric parameters have a significant impact on the milling force and the deformation, and optimization of the influence factors of milling deformations is important for milling quality. Considering that performing milling experiments under multiple conditions is often costly and time-consuming, this paper provides a finite-element-simulation-based method to study effects of the factors on the forces and deformations during milling thin-walled parts. Firstly, using ABAQUS, a finite element simulation model of the milling process of thin-walled parts is established. Additionally, an orthogonal experimental scheme is designed for optimization of the milling parameters, so as to determine the optimized experimental scheme, and then the optimized experimental scheme is verified to reduce the milling force and deformation by finite element simulations. The optimal parameters for a minimal milling force are a spindle speed of 2000 r/min, a feed rate per tooth of 0.04 mm/z, a milling depth of 1.6 mm, a milling width of 1 mm, a diameter of 6 mm, a rake angle of 20°, a tilt angle of 45°, and two teeth. Similarly, the optimal parameters for minimal node deformations are a spindle speed of 4800 r/min, a feed rate per tooth of 0.18 mm/z, a milling depth of 1 mm, a milling width of 1 mm, a diameter of 16 mm, a rake angle of 20°, a tilt angle of 40°, and four teeth. In addition, this paper uses an optimization algorithm to fit the empirical function with a certain practical value, which can provide a reference for the machining of TC4 titanium alloy. By doing so, we can optimize the milling parameters to obtain the desired machining quality and accuracy, while also saving on time and resources.

Study of Surface Integrity of SiCp/Al Composites Using High-Speed Milling under Cryogenic Liquid Nitrogen Conditions

In order to study surface roughness, surface morphology, surface microhardness, and surface residual stress, single-factor and central combination high-speed milling testing of SiCp/Al composites was carried out using a PCD tool under cryogenic liquid nitrogen cooling conditions. The test results show that the surface roughness value gradually increases with an increase in feed or milling depth, and the interaction between the two can make this phenomenon more serious. When the milling speed changes at 200~360 m/min, the surface microhardness and surface residual stress first increase, and then, become smaller, so it is recommended to use a speed above 240 m/min for milling under cryogenic liquid nitrogen cooling conditions. With an increase in milling depth and feed, the degree of surface microhardness is significantly improved, and the residual compressive stress also has a tendency to convert to residual tensile stress. In addition, it can be seen from the simulation results that as the milling depth and feed per tooth increase, the interference effect of the SiC particles on internal residual stress transfer also increases. Therefore, it is not recommended to use both high milling depths and high feed per tooth.

On modelling Nd:Yag nanosecond laser milling process by neural network and multi response prediction methods

In this paper, the milling effects on AISI 316 L steel by nanosecond pulsed laser was studied under different parameters as laser pulse peak power, laser repetition rate, scanning speed and focal point. Based on a full factorial design, 108 tests were conducted. MD varied from 15.58 to 60.14 µm; MRR from 0.0028 to 0.0195 mm3/s; Ra from 0.13 to 1.49 µm. The study showed that the laser power and scanning speed have a great influence on material removal. When the laser spot was focused on the upper sample surface, the removing material process was improved. Moreover, Response Surface Methodology and Artificial Neural Network were used to analyze and predict the milling depth, the material removal rate, and the surface roughness. The predictive capabilities of the methods were compared. The predicted R-square for RMS model were 99.70%, 77.79% and 67.73%, while for FF-BPNN were 99.99%, 99.85% and 81.98% for MD, MRR and Ra respectively. Therefore, both models demonstrated strong ability to match the responses. Neural network model exhibited superior predictive power.

Numerical Control Machining Process Parameters of Nickel-based Alloy Casing

As a major component of engine, aviation casing is usually machined from superalloy blanks because it needs to withstand high temperature, high pressure and other environments during use. Surface integrity indexes such as surface roughness and residual stress will seriously restrict the performance and service life of such parts. In this paper, by studying the processing technology of the casing, Deform-3d software was applied to conduct three-dimensional dynamic modelling for the rough milling process of the casing made of nickel-based alloy GH4169, and the milling simulation analysis was carried out. With milling force and milling temperature as evaluation indexes, the variation rules of force and temperature under different coaxial and radial milling depths were obtained through simulation analysis. At the same time, the single factor optimization was carried out for different coaxial and radial milling depths according to evaluation indexes, and a set of optimal milling parameters were obtained, which provided theoretical guidance for practical machining.

Neuro-fuzzy assessment of machined wood fibre–reinforced magnesium oxide composite

ABSTRACT High quality processing key to improving product quality and enterprise benefits. In this work, an adaptive network–based fuzzy inference system (ANFIS) was combined with milling experiments to understand the effects of tool geometry and milling parameters on the surface quality of wood fibre–reinforced magnesium oxide composite (WRMC). Specifically, changes in surface roughness (Ra) and damage of WRMC at different milling conditions were assessed using ANFIS and micro-analysis methods. Development of ANFIS models were confirmed to be reliable for predicting surface roughness. Changes in surface roughness at different milling conditions were determined, and the lowest surface roughness was obtained at the highest rake angle, highest cutting speed, and smallest milling depth. Furthermore, pitting-type damage irregularly distributed on the machined surface is attributed to the pulling out and debonding of wood fibres. Overall, high cutting speed, shallow cutting depth, and high rake angle is recommended for fine machining of WRMC where a smooth surface is desired. This study showcases how neuro-fuzzy models can be combined with conventional micro-analysis to optimize milling parameters for WRMC to minimize surface damage, and paves the way for future studies to optimize cutting tool life and energy consumption.

Optimization method for rough-finish milling allowance based on depth control of milling affected layer

The optimization of machining allowance has an important influence on the machining quality of workpieces. This paper determined an optimization method for rough-finish milling allowance based on depth control of milling affected layer. Firstly, the coupling influence of rough-finish milling cutting depth on milling affected layer depth was studied by experiment. Secondly, the influence rule of rough milling and finish milling on the affected layer depth was studied by experiment, and the prediction model of the milling affected layer depth based on the cutting depth of rough milling and finish milling was established, as well as the surface roughness prediction model of the finish milling cutting depth. Finally, the effectiveness of the optimization results was verified by experiments. The experimental results show the optimized machining parameters can increase the machining efficiency by 31.2%, and the milling affected layer is 91 μm, which indicates that the depth of milling affected layer is effectively controlled.

Investigation on the mechanism of micro-milling CoCrFeNiAlX high entropy alloys with end milling cutters

This paper aims to explore the influence mechanism of different Al contents on the mechanism of CoCrFeNiAlX high entropy alloy micro-milling by studying CoCrFeNi (Al0), CoCrFeNiAl0.6 (Al0.6) and CoCrFeNiAl (Al1). The law of changes in the temperature field, cutting force, residual stress, and surface roughness of Al0, Al0.6 and Al1 is investigated by the simulation method. The research results showed that the chips produced during the milling of three workpieces with different Al contents are different in the milling process. In particular, those produced by Al0 are shaped like long flakes and easy to adhere to the milling part, thus leading to short-duration chip accumulation. This is also the reason why the milling temperature of Al0 is obviously higher than that of Al0.6. The milling temperature of each workpiece gradually increases with the increase in milling speed, reaching the maximum when the milling speed reaches 450 mm/s. At the same milling speed, the milling temperature of each workpiece is sequenced as follows: Al1>Al0>Al0.6. Also, the milling temperature of the three types of workpieces shows an upward trend within a complete milling cycle, with the maximum milling temperature being far lower than that of traditional milling. With the increase in milling speed, the changes in the cutting forces of Y and Z directions are virtually the same for the three types of workpieces with different Al contents. However, the influence of milling speed on X-direction cutting force is relatively complex. The X-direction cutting force of Al1 and Al0 changes little, almost forming a horizontal line, while that of Al0.6 tends to increase first and then decrease. With the increase in milling speed, all the three-way cutting forces of three types of workpieces increase. With the increase in the feed rate, for each workpiece with three different Al contents, the X-direction cutting force first rises and then falls; the Z-direction cutting force tends to decrease gradually and the Y-direction cutting force almost forms a horizontal line. Also, the cutting force of Al0.6 is the smallest among the three under the same parameter and its peak value in the X direction is 68% of that of Al1 and 93% of that of Al0. With the increase in milling speed and milling depth, the changes of the residual stress for the workpieces with three different Al contents tend to be “spoon” shaped and the peak value of the residual stress for each workpiece is sequenced as follows: Al1>Al0.6>Al0. Among them, the peak value of residual stress for Al0.6 is 57% of that of Al0. The machined surface roughness of Al0.6 is the smallest after the whole micro-milling process is completed for the three types of workpieces with different Al contents.

Optimization of cutting power and power efficiency during particleboard helical milling

AbstractWith the aim of providing scientific guidance for the application of spiral cutters in particleboard machining, this work studied the influence of milling parameters on milling power and power efficiency during helical milling of particleboard. And the response surface methodology was applied to optimize the milling parameters to reduce machining energy consumption and improve energy efficiency. The factors of milling depth, spindle speed and helical angle were selected as input parameters, and the mathematical models between the input parameters and the response parameters were established. Then, the significant influence of each factor and the interaction of two factors were determined by variance analysis, and the change trend of milling power and power efficiency was studied by response surface methodology. Results showed that the milling depth had the greatest impact on milling power and power efficiency, followed by the spindle speed and helical angle. An increase in the milling depth and spindle speed resulted in an increase in milling power and power efficiency, while the increased helical angle resulted in a decrease in milling power and power efficiency. The optimized values of helical angle, spindle speed and milling depth were 54°, 5650 min−1 and 1.3 mm, respectively.

Study on the effect of air and argon assisted short electric arc machining on Ti6Al4V titanium alloy

The use of an argon–water medium rather than air–water medium is proposed to increase the material removal rate (MRR) and decrease surface oxide production for short electric arc machining. To provide a more stable discharge state during high-temperature processing, this approach uses argon to reduce the production of highly resistant refractory materials (e.g. titanium oxide). For short-arc machining of Ti6Al4V titanium alloy, the effects of the voltage, milling depth, electrode speed and gas flow rate on the MRR, relative electrode loss rate and surface roughness were compared for air–water and argon–water mixtures. It was discovered that the milling depth and voltage had the greatest impact on the MRR, voltage and electrode rotational speed had the greatest impact on the relative tool electrode wear rate (REWR), and voltage had the greatest impact on the surface roughness. In contrast to the air–water mixture, the MRR with the argon–water working medium was as high as 9391 mm3/min, with a 13% improvement in MRR, as well as a smooth and flat surface of the machined workpiece. Nevertheless, the REWR increased to 2.46% and the surface roughness was at least 432 μm with the argon–water medium, increasing by 59 μm compared with the air–water medium. Moreover, the addition of argon reduced the types of surface compound on the workpiece and decreased the residual stress defects, including the recast layer, microcracks and holes.

Depth Model and 5-Axis Variable-Angle Laser Engraving Experiment Based on the Energy Conservation Principle.

To ensure the consistency of laser engraving depth in chemical milling, the precise control of 5-axis variable-angle laser engraving was the focus of research. Based on the energy conservation principle, the depth model of 5-axis variable-angle laser engraving is established, and the relationships among the laser engraving depth, laser power, scanning velocity, and beam axis angle are proposed. A depth-constraint real-time adaptive control method of laser power is proposed considering the variable scanning velocity and beam axis angles. The depth model parameters are identified by an orthogonal experiment, and a variable-angle laser engraving experiment with adaptive control of laser power is carried out. The coefficient of determination of the proposed depth model is 0.977, which means that the engraving depth model established in this paper predicts the engraving depth effectively and reliably. The depth-constraint adaptive control method of laser power obtains stable and uniform machining results under abrupt changes in scanning velocity and beam axis angles.

Milling chatter detection of thin-walled parts based on GA-SE-SCK-VMD and RCMDE

In the milling process, it is easy to produce chatter due to the low rigidity of the thin-walled structure, which leads to the deterioration of workpiece surface quality and reduces the service life of cutting tools and machine tools. Therefore, a new chatter detection method for thin-walled parts based on optimal variational mode decomposition (OVMD) and refined composite multi-scale dispersion entropy (RCMDE) is proposed in this paper. Firstly, to solve the problem that the decomposition effect of the variational mode decomposition (VMD) algorithm is greatly affected by its parameter, a genetic algorithm (GA) is used to iteratively optimize the parameter of the VMD algorithm, and a new index, square envelope spectral correlated kurtosis (SE-SCK), is introduced as the fitness function of the genetic algorithm. Then, the energy ratio of the decomposed signal is calculated as the principle of selecting sub-components, and the sub-components with rich chatter information are selected for signal reconstruction. To solve the problem that the multi-scale dispersion entropy (MDE) will miss some information in the multi-scale process, RCMDE is introduced to detect milling chatter. Finally, the experiment of the variable cutting depth in side milling of titanium alloy thin-walled parts is carried out. The experimental results show that the OVMD algorithm proposed can solve the problem of difficult separation of chatter frequency bands caused by mode aliasing and lay a foundation for subsequent chatter feature extraction. RCMDE is more conducive to chatter detection than the single-scale DE when the scale factor is 4. The distinguishing effect of RCMDE on the machining state is more than 50% higher than that of MDE when the scale factor is 4.

An Approach on V-Shaped Milling for Rotor Balancing of Armatures

In order to improve the dynamic balancing accuracy of the micromotor armature, a method of V-shaped milling based on a discrete vector model for unbalance correction is proposed. The discrete vector model is fitted according to the parameters of the milling cutter and rotor, and then all the unit unbalance vectors in the discrete vector model are added to the milling center. The numerical relationship between the milling depth and the removal of the mass unbalance vector is obtained, and the accuracy of the model is verified via comparison with the data of the simulation experiments. The complexity of the integral formula of the numerical milling model makes it difficult to apply in practice. The discrete vector model does not require integration of the numerical formula and only considers the milling area as being composed of countless discrete blocks, which greatly simplifies the process of solving the unbalance vector. In view of the different thicknesses of the tooth surface of the armature, in order to avoid damage to the armature during milling, the unbalanced vector is decomposed at the center of the tooth surface by force decomposition. The experimental results show that this proposed method can effectively improve the dynamic balancing accuracy of the micromotor armature.

Study on parameter optimization of noise-vibration-force-metal removal rate in TC4 titanium alloy milling

Aiming at the influence of noise, vibration, and force on machining efficiency and machining quality in the process of milling, this paper proposed an improved particle swarm optimization algorithm. The algorithm could optimize the multi-objective and multi-parameter integration model and obtain the optimal milling parameters under the specified milling conditions, so as to reduce milling noise, vibration, and force and improve machining efficiency. First, based on the theory of all factor experiment, the milling experiment of TC4 titanium alloy was carried out, and the relevant milling noise, vibration, and force signals were obtained. Then, based on the response surface analysis method, the influence of milling parameters on noise, vibration, and force was studied. Finally, a multi-objective optimization model based on maximum metal removal rate, minimum noise, minimum vibration, and minimum force was established, and the improved particle swarm optimization algorithm was used to solve the optimal milling parameters. The result shows that when the milling speed is 24.9588 m/min, the feed speed is 9.9324 mm/min, and the milling depth is 1 mm, the optimal milling state within the constraint conditions can be achieved.

Evaluation and Improvement of Greenness for Milling AL6061 Alloy through Life Cycle Assessment and Grey Relational Analysis.

Modern manufacturing industries thrive on greenness, which means ensuring acceptable environmental impacts and required surface quality of the products during the manufacturing process. However, there is a conflict between surface quality and environmental performances in the milling process. The current research only considers energy consumption rather than total environmental impacts. In this respect, this research focuses on the multiobjective optimization of machining parameters for balancing the surface quality (i.e., surface roughness, Ra) and total environmental impact (TEI), which includes raw materials usage, energy consumption, and output pollutant emission during the milling of AL6061 alloy. First, life cycle assessment (LCA) of the milling process is used for evaluating the TEI. Then, multiobjective optimization is conducted using Grey Relational Analysis. The results indicated that the improvement of Ra and TEI can be achieved with higher cutting speed, higher depth, and wet conditions in milling. The optimization work showed that cutting speed of 165 m/min, feed rate of 0.28 mm/rev, depth of cut of 2 mm, and width of cut of 3 mm are the optimal combination among existing experiments. Compared to single objective optimization results, multiple responses (Ra and TEI) can be improved simultaneously.

Study of the Machinability of an Inconel 625 Composite with Added NiTi-TiB2 Fabricated by Direct Laser Deposition

This work studies the process feasibility of milling a metal-matrix composite based on Inconel 625 with added NiTi-TiB2 fabricated by direct laser deposition. The composite is intended for manufacturing turbine blades and it has strength characteristics on par with those of Inconel 625. However, the addition of TiB2 has improved its heat and wear resistance. This material is new, and its machinability has not been studied. The new composite was milled with end mill cutters, and recommendations were worked out on the cutting speed, feed per tooth, cutter flank angle, as well as depth and width of milling. The wear of cutter teeth flank was more intense. After the flank wear land on the back surface of a tooth had reached 0.11–0.15 mm, there was a sharp increase in the forces applied which was followed by brittle fracture of the tooth. Milling at a speed of 25 m/min ensured 28 min of stable operation. However, afterwards the critical wear value of 0.11 mm was quickly approached at a cutting speed of 50 m/min, and critical wear followed after 14 min. Dependencies of the cutting forces vs. time for all the selected cutting speeds and throughout the entire testing time period have a tendency to increase, which indicates the influence of cutter wear on the cutting forces. It was found that the durability of the cutters increases with an increase in the milling width and a decrease in the milling depth.

Effects of Nanosecond-Pulsed Laser Milling on the Surface Properties of Al2O3 Ceramics

The effect of nanosecond-pulsed laser milling on the surficial microstructure of Al2O3 ceramics is studied in this work. The macrostructure, microstructure, surface roughness, and milling depth of the ceramics are analyzed via scanning electron microscopy and confocal laser scanning microscopy. The results reveal that the surface roughness and the milling depth of Al2O3 ceramics increase with increasing laser power, laser repetition rate, and scanning times. After nanosecond-pulsed laser milling, cavities as well as molten and granular solidified structures are observed on the Al2O3 ceramic surface, which indicate that the main mechanism of nanolaser milling is the melting and gasification caused by the interaction between the laser and the material.

Effect of chemical post-processing on surfaces and sub-surface defects in electron beam melted Ti-6Al-4V

Surfaces after chemical post-processing treatments of electron beam melting (EBM) produced Ti-6Al-4V have been studied. Targeted chemical treatment allowed the study of variation in surface quality with material removal depth. Characterization of surface and defect morphologies were made, comparing two chemical post-processing methods, Hirtisation® and chemical milling with different milling depths. Surface topography was characterized using white light interferometry and subsurface defect distribution was studied using X-ray computed tomography (XCT). The morphology of the surface at different milling depths was compared to the sub-surface information from XCT scans of the as-built material. Furthermore, Hot Isostatic Pressing (HIP) treated material was documented for comparison. Results show that post-processed surfaces contain a number of different defects of mixed morphology, position and origin. Post-processing deteriorates the surface quality with increased removal depth due to the presence of sub-surface defects. The position of sub-surface defects in relation to the material surface coincides with the depth at which contour-hatch interactions are likely to have occurred during the EBM building process. The distribution of this sub-surface defect population is anisotropic in the building (horizontal) plane and reasons for this are explored. Hirtisation® produces surfaces morphologically different from chemically milled surfaces. This difference was found to contribute to Hirtisation® producing surfaces with higher roughness (Sa) than chemically milled surfaces at comparable removal depth. HIP did remove all detectable sub-surface defects but microstructural artefacts indicating healed porosity were found.