Milling Parameters Research Articles

Influence of milling parameters on the microstructural evolution mechanism of 7075-T6 aluminum alloy

This study evaluates the influence of milling parameters on the milling process and microstructural changes in 7075-T6 aluminum alloy using experimental and simulation methods. Key findings include a direct correlation between milling parameters and higher chip temperatures, particularly influenced by milling speed which at its third level can cause temperature elevations up to 36 °C, ordered by impact as ap > f > v.Increasing milling depth markedly affects grain recrystallization, especially near the tool's trailing face (P3), leading to a 45.8% reduction in dynamic recrystallization, whereas lower depths expedite this process. Additionally, grain size and number at the leading face (P1) are also depth-dependent, with larger grains diminishing in volume yet increasing in quantity as depth increases. Enhanced milling speeds up to 500 mm/min significantly refine grains to near 1 μm at P1, with an 80.7% rise in recrystallization numbers, while at P3, grain size initially contracts then enlarges at 416 mm/min. Feed rate primarily impacts the quantity of recrystallized grains at the tool tip (P2), with a notable 60.4% increment and a swift transition from larger to smaller grains. Both P1 and P2 experience about a 20% reduction in grain growth due to feed rate, with more pronounced effects at P1 and the fragmentation of larger boundary grains into smaller, irregular shapes.

Quality enhancement of micro-milled channels with automated laser assistance

AbstractMicrochannels are utilised on material surfaces of a body, allowing coolant to pass through them and enabling heat dissipation by increased contact area. Fabrication of metal surface microchannels is primarily achieved by employing a micro-milling process, which has drawbacks such as excessive cutting forces, top burrs, tool wear, and lower tool life. Alternatively, it is also realised by using Laser micro milling, which has problems associated with lower quality of surface finish, un-desired taper, heat-affected zone, and spatters. The existing literature, after due review of the current state of the art, has brought out gaps needing attention. These gaps are limited capability to reduce surface roughness, unaddressed burr width, and irregular bottom surface morphology, which affect microchannel quality. These gaps motivate this research work to improve and sustain the microchannel quality. To achieve the goals, this research work performs the fabrication of microchannels by micro-milling with automated laser assistance being achieved in two ways (a) sequentially, (b) non-sequentially, termed as LASMM and LPCMM, which are novel for the scientific community. The effects of micro milling parameters, spindle speed and feed on the quality were analysed while machining commercially pure titanium (cp-Ti). Results show that laser assistance to micro-milling provides a lower generation of undesired forces and lesser top burrs compared to micro-milling alone. In sequential laser assistance, the channels have a mean down burr width ~ 58% lower and a maximum down burr width ~ 38% lower than the channels done non-sequentially. In the case of up-burr width, a mean value ~ 60% lower and a maximum value ~ 73% lower is achieved in channels done non-sequentially as compared to those done sequentially. In the case of surface roughness, channels done sequentially have a maximum Sa value of 1.508 µm, a maximum Sq value of 1.912 µm whereas non-sequentially, they show a maximum Sa value of 3.495 µm, maximum Sq value of 4.59 µm. Steady tool wear is observed sequentially, whereas in non-sequential, rapid tool wear occurs after 500 mm of cutting length.

Numerical simulation and tool parameters optimization of aluminum alloy transmission intermediate shell

Due to its challenging manufacturing and intricate morphology, the aluminum alloy transmission intermediate shell used in vehicle transmission has been the focus of many academic studies. In this study, the three-dimensional cutting model is condensed to a two-dimensional cutting model and utilized to simulate the finishing process of an aluminum alloy workpiece using the finite element modeling program DEFORM-3D. Through orthogonal testing and range analysis, the impact of integral end mill side edge parameters on cutting performance was investigated. It is determined that tool chamfering has a greater impact on cutting performance than tool rake and relief angles, that chamfering width has the most impact on cutting force, and that chamfering angle has the greatest impact on cutting temperature. The workpiece's surface roughness is tested during a cutting experiment, and an analysis of the data reveals that the finite element simulation model is accurate and the orthogonal test method is reasonable. The tool chamfer has a greater impact on roughness than the tool rake angle and relief angle. The tool settings are further optimized using the firefly method. By examining the data, it is determined that the prediction model is correct and the optimization model is reasonable. The cutting efficiency is higher and the surface quality is better when the chamfer width is 0.17 mm and the chamfer angle is 7.3° or 18.3°. Therefore, optimizing the side edge parameters of the integral end mill during the finishing process of a thin-walled aluminum alloy shell has practical technical value.

Exploring the Impact of Different Milling Parameters of Fe/SiO2 Composites on Their Structural and Magnetic Properties.

This research focuses on the production process of soft magnetic composites in the form of 3D bulk compacts made from insulated powder particles using different milling parameters, aiming to enhance their magnetic properties and to study an innovative method of the powder surface "smoothing" technique. A structure analysis using scanning electron microscopy (SEM), EDS, and optical microscopy is also included. We found out that the samples made by the innovative method have lower density values. This can be caused by a more consistent SiO2 insulation layer on highly pure iron powder particles. A correlation between the mechanical smoothing method and better insulation of powder particles can help to provide eco-friendlier solutions for the preparation of soft magnetic composites, such as less usage of reagents and more consistent coverage of powder particles with lower final insulation thickness. The magnetic properties of these compacts are evaluated by coercive field, permeability, and loss measurements. The particle-level smoothing technique in some cases can reduce the value of coercivity up to 20%. For some samples, the ball-to-powder ratio has a bigger impact on magnetic properties than surface treatment, which can be caused by an increased amount of insulation in the SMC compacts.

Research on the End-Milling Surface Quality of Paulownia Based on Response Surface Model in Terms of Force and Chip Morphology

To investigate the impact of different milling parameters on milling forces, chip morphology, and machined surface quality during the Paulownia milling process, we conducted experiments using cemented carbide single-tooth shank milling cutters. Additionally, we established a response surface model (RSM) to analyze milling surface quality. The key findings are as follows: milling forces along the parallel and tangential axes decrease with an increased tool rake angle and spindle’s rotational frequency, but they exhibit a positive correlation with milling depth. The effect of spindle’s rotational frequency on the milling force along the lateral axis differs due to the complex fiber characteristics of Paulownia. As milling depth decreases, chip morphology transitions from a block structure to a sheet structure, eventually becoming fragmented with shallow milling. Higher spindle’s rotational frequency and tool rake angle lead to a more fragmented direction in Paulownia chip morphology, while machined surface quality improves. Notably, under specific conditions, a striped chip morphology significantly enhances machined surface quality compared to similar milling parameters. The established RSM for machined Paulownia surface roughness is reliable and holds reference value for inhibiting surface damage in Paulownia machining.

Prediction of surface roughness and depth of cut in abrasive waterjet milling of alumina ceramic using Machine learning algorithms

In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (Ra) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation.In the prediction of surface roughness (Ra) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as ‘True’, and a minimum impurity decrease of 0.The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (Ra). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (Ra) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources.

Fabrication of high aspect ratio grooves on aluminium nitride by laser and chemical milling enhanced micro milling

Aluminium nitride (AlN) ceramic is a typically difficult-to-machine material used in electronic packaging. Laser and chemical milling enhanced micro milling (LCMEM), a high-quality and efficient processing method for AlN, is proposed in this study. Rough machining is completed by repeated alternating laser ablation and chemical removal; the final finish is achieved by micro milling (MM). To achieve precise laser and chemical milling parameters based on the target structure, a laser and chemical milling (LCM) prediction model is established and verified; this can be used to predict the morphology of the grooves after LCM. The change trend of the grooves after LCM with the laser parameters is investigated. It was found that LCMEM can improve the surface quality by 60 %, and an optimal finishing surface with a surface roughness Ra value of 54 nm can be obtained when the feed per tooth is 0.4 µm/z. Lastly, this study uses LCMEM to produce a high aspect ratio groove with aspect ratio of 2.5 and depth of 1250 µm. Compared with conventional MM, LCMEM reduces the allowance of MM by 71.95 %, improves the groove accuracy, and reduces tool wear.

Optimizing milling parameters based on full factorial experiment and backpropagation artificial neural network of lamina milling temperature prediction model.

Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature Tc and bone surface temperature Tb for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R= 0.99661, validation set R= 0.85003, testing set R= 0.90421, all temperature data set R= 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety.

Surface quality related to machining parameters in 3D-printed PETG components

Fused Deposition Modeling (FDM) finds extensive application across various fields due to its cost-effectiveness and user-friendly nature. However, it does come with certain limitations, including challenges in achieving high surface quality, precise dimensional tolerance, and the characteristic anisotropic mechanical properties it exhibits.The aim of this paper is to investigate the machinability of 3D-printed PETG and analyze the roughness and burr formation that occurs as a result. A Design of Experiments (DoE) was developed with three factors: rotational speed, feed rate, and depth of cut. Each factor had different levels: rotational speed at 3000, 5500, and 8000 rpm; feed rate at 400, 600, and 800 mm/min; and depth of cut at 0.2, 0.4, 0.6, and 0.8 mm. The machinability was evaluated based on two response parameters: roughness (Sa), determined on the milled surface, and burr height, measured using a profilometer on both sides of the milled surface. The findings indicate that milling parameters strongly affect roughness and burr formation. However, the optimal conditions for minimizing roughness and burr formation are not coincident. The results were also compared to the machinability of PLA machined under similar conditions.

EXPERIMENTAL INVESTIGATION AND COMPARATIVE STUDY OF CNC MILLING PROCESS PARAMETER BY FACTORIAL METHODS WITH TAGUCHI METHOD ON ALUMINUM ALLOY

This study explores the comparative effectiveness of the One Factor at a Time (OFAT) and Taguchi methods in optimizing CNC milling parameters for aluminum alloy 6061. The primary aim is to enhance the material removal rate (MRR) and minimize surface roughness (Ra) and temperature under both coolant and non-coolant conditions. A systematic approach is employed, involving a series of CNC milling experiments where depth of cut, feed rate, and spindle speed are varied. By utilizing both one factor at a time and Taguchi methods, this research seeks to identify the most influential factors affecting MRR, Ra, and temperature. The Taguchi method is applied for its robust design capabilities, while the one factor at a time method isolates individual parameters to assess their impact. The Percentage of Variation in Ra and temperature, with and without coolant, is analyzed for both methods to determine their efficacy in process optimization. Through nine experimental trials, the study aims to ascertain which method provides superior results in terms of optimizing MRR and reducing Ra and temperature. The findings will offer significant insights into the effectiveness of each method, contributing to the improvement of CNC milling processes. This research not only advances the understanding of process optimization in CNC machining but also provides practical recommendations for selecting the most suitable optimization approach based on specific machining objectives.

A new grinding trajectory adaptive adjustment algorithm for circular-arc end mill flank based on grinding contact zone control

As one of the key structural features of the circular-arc end mill, the flank directly affects its service performance such as tool life and milling efficiency. Due to the curvature discontinuity of the flank edge curves and the inflexible posture of the grinding wheel, the existing grinding trajectory algorithms are difficult to accurately control the important end mill parameters such as the relief angle and flank width, and grinding interference is easy to occur. Therefore, this paper proposes a new grinding trajectory adaptive adjustment algorithm for circular-arc end mill flanks based on grinding contact zone control. Firstly, the edge curve equations are constructed through structural feature decomposition of the end mill, and the second flank is modeled based on uniform offset densification of edge curves. Then, the grinding wheel posture model is established and the contact zone between the grinding wheel and the first flank of the end mill is calculated. A grinding trajectory adaptive adjustment algorithm is then proposed, which iteratively modifies the lifting angle and swing angle of the grinding wheel and can avoid the abrupt change of grinding postures and the grinding interference, thus realizing accurate flank grinding. Finally, the corresponding algorithm module is developed on the VC++ platform, and the effectiveness of the proposed algorithm is verified by machining simulation and grinding experiments, therefore providing technical support for the accurate grinding of circular-arc end mills.

Is roller milling – the low energy wet bead media milling – a reproducible and robust milling method for formulation investigation of aqueous suspensions?

Long-acting injectables have shown to offer a prolonged release of a drug compound up to several months, providing the opportunity to increase patient compliance for treatment of long-term and chronic conditions. Different formulation technologies have already been utilized for long-acting injectables, and especially aqueous suspensions with crystalline drug particles in the sub-micron range have sparked an interest for future development of long-acting injectables. Wet bead milling is a common top-down process used to prepare nano- and microsuspensions of crystalline drug particles with the addition of surfactants in the dispersion medium, which are working as stabilizers to prevent agglomeration or crystal growth that ultimately may influence the physical stability of nano- and microsuspensions. To examine the reproducibility of the suspensions manufactured and the behavior of their physical stability, i.e., changes in particle sizes over time, low-energy roller mill was utilized for the manufacturing of nano- and microsuspensions in the present study. Investigated formulation parameters was stabilizer type and concentration and milling parameters varied in bead size and duration of milling. The obtained results demonstrated that the physical stability of suspensions containing the two model compounds, cinnarizine and indomethacin, was highly affected by the constitution of surfactant and processing. Various size classes were obtained and accompanied by high variations between the individual samples that indicated uneven and unpredictable milling by the low-energy roller mill, limiting the possibility to prepare reproducible and physical stable suspensions. Short-term stability studies revealed clear tendencies towards reversed Ostwald ripening of suspensions stabilized with poloxamer 188 that contained cinnarizine as the drug compound, and to a smaller extent suspensions containing indomethacin. Furthermore, X-ray Powder Diffraction confirmed no alteration of the drug compounds crystal structure after roller milling for multiple days.

Milling Stability Prediction under Multi-effect Synergy

During the milling process, chatter vibration is prone to occur when the spindle, tool, and workpiece interact with each other under certain parameters, which seriously affects machining efficiency and reduces machining quality. Therefore, it is necessary to study the mechanism and influencing factors of chatter vibration, predict the stability of the milling system through a multi-effect synergistic dynamic model, and obtain the machining parameters of stable milling through analysis. Based on the traditional milling model, this article considered the regeneration effect, process damping effect, and modal coupling effect, and also considered the friction effect of the front cutting surface for friction-induced chatter, to make the model’s prediction range more accurate. Then, a milling dynamic model was established combining multiple effects, and its stability lobe diagrams were solved by the full-discretization method. The accuracy of the model was verified through milling stability experiments. The experimental results are basically consistent with the predicted stability lobe diagrams, indicating that the milling stability prediction model considering multi-factor coupling of the milling model is correct. Finally, the influence of various effects on milling stability was analyzed, with a focus on the influence of friction effects.

Online Monitoring Dynamic Characteristics in Thin-Walled Structure Milling: A Physics-Constrained Bayesian Updating Approach

Accurate estimation of modal parameters of thin-walled structures during the milling process is crucial since it determines the accuracy of deformation and vibration prediction and then the feasibility of selected machining parameters. However, time-varying dynamic characteristic due to material removal brings a great challenge in the pursuit. This article presents an online monitoring strategy for the estimation of modal parameters of thin-walled structures with measured cutting forces and single acceleration signal. The detailed physical constraints are presented, and the analytical form of the modal shape of thin-wall structures is derived. A Bayesian filter framework with slack variables is established for updating the modal parameters by simultaneously considering the physical constraints and the measured signals. Validation experiments are conducted on two kinds of thin-walled structures with different structures. The results of the proposed method are compared with the results of the standard modal test at certain stages. It indicates that the proposed approach is effective for accurately online monitoring of the spatiotemporally varying modal parameters in milling of thin-walled structures.

Multi-objective Optimization of Milling Parameters for Carbon Fiber Reinforced Polymer Based on MOEA / D

In order to study the optimal milling parameters of carbon fiber reinforced polymer, this paper takes carbon fiber composite unidirectional plate as the research object, and the effects of cutting parameters on cutting forces and surfaces roughness were analyzed by high-speed cutting experiments. Experimental tests have validated the effectiveness of high speed cutting in reducing cutting forces, improving cutting efficiency, and improving machined surface quality. With the minimum surface roughness and maximum material removal rate as optimization goals, and the spindle speed, feed speed, and radial feed as design variables, the MOEA/D algorithm is used for multi objective optimization on, and to give the optimal solution set of carbon fiber composite milling.

Tuning Parameters of a Ball-Rolling Mill for Rolling of Grinding Balls

Tuning Parameters of a Ball-Rolling Mill for Rolling of Grinding Balls

State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes

AbstractIn conventional machining, one of the main tasks is to ensure that the required dimensional accuracy and the desired surface quality of a part or product meet the customer needs. The successful accomplishment of these parameters in milling, turning, milling, drilling, grinding and other conventional machining operations directly depends on the current level of tool wear and cutting edge conditions. One of the proven non-contact methods of tool condition monitoring (TCM) is measuring systems based on image processing technologies that allow assessing the current state of the machined surface and the quantitative indicators of tool wear. This review article discusses image processing for tool monitoring in the conventional machining domain. For the first time, a comprehensive review of the application of image processing techniques for tool condition monitoring in conventional machining processes is provided for both direct and indirect measurement methods. Here we consider both applications of image processing in conventional machining processes, for the analysis of the tool cutting edge and for the control of surface images after machining. It also discusses the predominance, limitations and perspectives on the application of imaging systems as a tool for controlling machining processes. The perspectives and trends in the development of image processing in Industry 4.0, namely artificial intelligence, smart manufacturing, the internet of things and big data, were also elaborated and analysed.

Investigation of computer numerical control wood milling parameters for occupationally safer by the minimization of produced wood dust

ABSTRACT Computer numerical control (CNC) wood machining produces wood dust, harming the operator's health. In this research, the effective parameters of the amount of produced wood dust (PM) in CNC milling are investigated. The design of the experiment was done based on the response surface method (RSM) and subsequently, 27 experimental tests were performed. The results of the analysis of variance show that the parameters of the depth of cut (ap ), step over (ae ), feed rate (vw ) and cutting speed (vc ) have a great impact on PM, respectively. Changing these machining parameters leads to severe changes in the PM (0.043–11.200 mg/m3). Also in 78% of experimental tests, the amount of PM is higher than the National Institute for Occupational Safety and Health (NIOSH) standard value (1 mg/m3). In the next step, for the reduction and minimization of PM, a quadratic regression equation was derived. After model validation, the model was minimized by the distance-based optimality method at Minitab software. Optimization result shows that by choosing the optimum parameters ap = 3 mm, vw = 50 mm/s, vc = 15,393 rpm and ae = 6.270 mm, PM is reduced to 0.011 mg/m3, which is much lower (about 89%) than the NIOSH standard value. This prediction was tested and validated by the experimental test.

Analysis and prediction of surface topography characteristics and influence factors of tool passive vibration in milling process

The surface topography of the processed workpiece has a significant impact on its service performance, and the tool undergoes passive vibration due to the influence of milling forces during the machining process. This article focuses on the influence of milling parameters and tool passive vibration on the formation process of surface topography. Firstly, the forming mechanism of surface topography during passive vibration of cutting tools was studied, and a cutting edge motion trajectory model considering milling parameters and passive vibration of cutting tools was established; And the influence of milling parameters on surface topography with and without tool passive vibration was analyzed through experiments and simulations; A prediction model for the maximum height S z and areal arithmetic mean height S a of surface topography was established using least squares support vector machine (LSSVM). We used the Improved Particle Swarm Optimization (PSO) algorithm to search for optimal solutions for kernel width coefficients and regularization parameters in LSSVM, and wrote a program to improve the PSO-LSSVM prediction model. The results indicate that the proposed prediction model can provide a certain basis for the selection of actual milling experimental parameters.

Investigation on active vibration control to improve surface quality in precision milling process

The tool-workpiece vibration in the precision milling process plays a pivotal role in influencing the surface quality. To solve the machining problem coming with the process vibration, the active vibration control model as well as the corresponding platform are developed, and the active vibration control algorithms are applied to reduce the relative vibrations and improve the surface quality. Firstly, the milling vibration reduction and surface quality improvement are modeled based on the active control algorithms and the system dynamic characteristics. Then, applying the different algorithm control strategies, such as PID, Fuzzy PID, BP neural network, and BP neural network PID control, the control effect is simulated and analyzed. Finally, an experimental platform is established to validate the system’s reliability. The efficiency of various active control methods is compared in terms of frequency vibration control and surface finish roughness improvement. The results indicate that under different milling parameters, the four algorithm control strategies exhibit optimal effects of 13.5%, 30.4%, 28.8%, and 40.1% respectively. These findings provide valuable insights into selecting the optimal vibration control method for precision milling.