Optimization Of Milling Parameters Research Articles

A comprehensive guide to milling techniques for smoothing the surfaces of 3D-printed thermoplastic parts

Purpose This study aims to thoroughly examine the milling process applied to fused filament fabrication (FFF) parts. The primary objective is to identify the key variables in creating smooth surfaces on FFF specimens and establish trends about specific parameters. Design/methodology/approach In this study, PLA and ABS samples fabricated by FFF are subjected to side milling in several experiments. Achievable surface quality is studied in relation to material properties, milling parameters, tooling and macrostructure. The surface finish is quantified using profile measurements of the processed surfaces. The study classifies the created chips into categories that can be used as criteria for the anticipated quality. Spectral analysis is used to examine the various surface formation modes. Thermal monitoring is used to track chip formation and surface temperature changes during the milling process. Findings This study reveals that effective heat dissipation through proper chip formation is vital for maintaining high surface quality. Recommended methodology demands using a tool with a substantial flute volume, using high positive rake and clearance angles and optimizing the feed-per-tooth and cutting speed. Disregarding these guidelines may cause the surface temperature to surpass the material’s glass transition, resulting in inferior quality characterized by viscous folding. For FFF thermoplastics, optimal milling can bring the average surface roughness down to the micron level. Originality/value This research contributes to the field by providing valuable guidance for achieving superior results in milling FFF parts. This study includes a concise summary of the theoretically relevant insights, presents verification of the key factors by qualitative analysis and offers optimal milling parameters for 3D-printed thermoplastics based on systematic experiments.

Optimizing Milling Parameters for Enhanced Machinability of 3D-Printed Materials: An Analysis of PLA, PETG, and Carbon-Fiber-Reinforced PETG

Fused deposition modeling (FDM) is widely applied in various fields due to its affordability and ease of use. However, it faces challenges such as achieving high surface quality, precise dimensional tolerance, and overcoming anisotropic mechanical properties. This review analyzes and compares the machinability of 3D-printed PLA, PETG, and carbon-fiber-reinforced PETG, focusing on surface roughness and burr formation. A Design of Experiments (DoE) with a full-factorial design was used, considering three factors: rotation 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. Machinability was evaluated by roughness and burr height using a profilometer for all the materials under the same milling conditions. To evaluate the statistical significance of the influence of various processing parameters on surface roughness and burr formation in 3D-printed components made of three different materials—PLA, PETG, and carbon-fiber-reinforced PETG—an analysis of variance (ANOVA) test was conducted. This analysis investigated whether variations in rotational speed, feed rate, and depth of cut resulted in measurable and significant differences in machinability results. Results showed that milling parameters significantly affect roughness and burr formation, with optimal conditions for minimizing any misalignment highlighting the trade-offs in parameter selection. These results provide insights into the post-processing of FDM-printed materials with milling, indicating the need for a balanced approach to parameter selection based on application-specific requirements.

Optimization of complex surface milling parameters based on HSS-MFM and OBL-NSGA-II

Optimization of complex surface milling parameters based on HSS-MFM and OBL-NSGA-II

Machinability of wood-plastic composites from the CNC milling process using the Box-Behnken design and response surface methodology for building applications

This research investigated the optimization of CNC milling parameters on hardness and surface roughness properties resulting from variations in speed (220, 470, and 720 rpm), feed rate (200, 300, and 400 mm/min), and depth of cut (3, 5, and 7 mm) based on experimental design. This study aimed to evaluate the effects and relation of milling parameters using different end mill diameters (8 and 12 mm) for machining wood-plastic composites (WPCs). All of the experimental runs were determined by the Box-Behnken design and optimized using the response surface methodology. The findings from this study revealed that the main milling parameters significantly affected the hardness, average roughness ( Ra), and mean peak-to-valley height ( Rz) of the WPCs. Increasing the speed in a range from 220 to 720 rpm resulted in higher hardness values. On the other hand, the Ra and Rz was decreased. Additionally, this experimental result is different from the morphological structure and surface observation. It was seen that the CNC milling conditions using high speed at 720 rpm displayed smooth surfaces, which resulted in visible evenness on the WPC surfaces. Finally, numerical optimization is a good technique for the experimental results and the predicted values. The predicted conditions for the CNC milling process using end mill diameters of 8 and 12 mm included a speed of 720 rpm, a feed rate of 300 mm/min, and a depth of cut of 3 mm with the best desirability of 0.973 (97.30%). These conditions were verified in the response models and confirmed the optimal values from observed values for the variables included in the models. Also, these optimal conditions for the CNC milling parameters can be used on other types of WPCs with melting points of plastic higher than 180°C.

Optimization of Milling Process Parameters for Fe45 Laser-Clad Molded Parts Based on the Nondominated Sorting Genetic Algorithm II

The milling process parameters of laser-clad molded parts have an essential influence on improving the surface quality of the coating. Generally speaking, optimizing a single property often leads to a reduction in another property. In this paper, we systematically investigated a milling process parameter optimization method for Fe45 laser-clad molded parts, and designed L9 (33) sets of orthogonal experiments by taking the spindle speed, feed rate, and cutting depth as input variables, and taking the milling force and material removal rate as optimization indices. The significance ranking of the milling process was analyzed by using the extreme difference method. Then, the multi-objective optimization of the milling process was realized by using the NSGA-II algorithm with the empirical index model as the objective function. The optimum milling parameters obtained were N = 2000 r/min, V = 120.0266 mm/min, and P = 0.45 mm. Finally, the reliability of the optimization results of the algorithm was proved by comparing and verifying the optimal results obtained from the algorithm with the optimal process obtained from the extreme difference analysis. The results provide a theoretical basis for the selection of milling parameters and parameter optimization of laser fusion-coated Fe45 alloys.

Taguchi and ANOVA-based Optimization of CNC Milling Parameters for Aluminium 7075 Alloy

This study is aimed at optimizing the CNC milling parameters for Aluminium 7075 alloy, using Taguchi and Analysis of Variance (ANOVA) approaches. The high-strength alloy has applications in the automobile and aerospace industries. The cutting depth, speed of milling and rate of feed were adjusted to methodically enhance the machining performance and efficiency; surface roughness was studied in these metrics. Taguchi and ANOVA approaches were helpful in optimizing the parameters to lower cost, improve machining quality and enhance the productivity of Aluminium 7075 alloy.

The milling parameters of mechanical parts are optimized by NC machining technology

In the field of mechanical manufacturing, CNC machining technology plays an important role in improving the precision and efficiency of part processing. However, how to further improve the effect of NC machining by optimizing milling parameters is still a key problem. The aim of this study is to optimize CNC milling parameters through systematic research and experiments to improve the machining efficiency and quality of mechanical parts. By adjusting key parameters such as tool speed, feed speed, and removal rate cutting depth, the influence of these parameters on the milling process was systematically studied using advanced CNC machining equipment. Through the collection and analysis of experimental data, the mathematical model is established, and the optimization algorithm is applied to find the best combination of milling parameters. The experimental results show that under the optimal combination of parameters, the surface quality of parts can be significantly improved, the machining time can be reduced, and the tool wear can be reduced. This research successfully realizes the optimization of milling parameters of mechanical parts by CNC machining technology and provides an effective solution for improving machining efficiency and reducing costs. This not only has guiding significance for the application of CNC machining technology but also has important promotion value in actual production.

Fabrication of TiB whiskers reinforced Ti6242 matrix composites by direct laser deposition: Powder preparation, microstructure and mechanical property

Direct laser deposition (DLD) technique has great potential in manufacturing titanium matrix composites (TMCs) components with complicated shape. The TiB whiskers reinforced Ti6242 matrix composites were fabricated from the premixed TiB2/Ti6242 powders via DLD technique. Quantitative characterization of the extent of mixing uniformity and the deformation rate of the ball milled TiB2/Ti6242 powders was performed so as to select the optimal ball milling parameters. The TiB2/Ti6242 powders with two different weight fractions of TiB2 (2 wt%TiB2/Ti6242 and 5 wt%TiB2/Ti6242) were used to obtain different distributions of TiB whiskers in the composites due to the change in the solidification path in DLD process. The TiB whiskers in the TMCs-Ⅰ obtained from the 2 wt%TiB2/Ti6242 powders exhibited a discontinuous network structure following solidification in hypoeutectic zone, while the TiB whiskers were randomly distributed in the TMCs-Ⅱ obtained from the 5 wt%TiB2/Ti6242 powders due to the solidification in hypereutectic zone. In comparison with the DLDed Ti6242, the α colonies and β grains of the TMCs-Ⅰ and TMCs-Ⅱ were significantly refined. In addition, both equiaxed and lamellar α phase were formed in the TMCs-Ⅱ, in which various orientation relationships (ORs) between TiB and α phase occurred and the presence or absence of OR between TiB and β phase was responsible for the different morphologies of α phase. The yield strength of the DLDed TMCs-Ⅰ and TMCs-Ⅱ increased by 10.7 % and 38.8 % in comparison with that of the DLDed Ti6242, and both the load-bearing strengthening and fine-grain strengthening effects effectively enhanced the strength of the composites.

Optimized Material Removal and Tool Wear Rates in Milling API 5ST TS-90 Alloy: AI-Driven Optimization and Modelling with ANN, ANFIS, and RSM

One of the performance measures and responses in manufacturing is the optimization of the Material Removal Rate (MRR) and Tool Wear Rate (TWR). This study used the response surface method (RSM) and AI-based models of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) to model and optimize the MRR and TWR for milling API 5ST TS-90 alloys. A ZX6350C milling machine was used to conduct twenty (20) experimental runs using a 10 mm HSS end-mill cutter. The experiment was designed using Central Composite Design (CCD) in Design Expert 14 software. RSM, ANN, and ANFIS models were applied in the predictive modelling of the milling process with a coefficient of determination above 0.85. Comparatively, ANFIS and RSM were marginally better than ANN in the predicted MRR for the milling process. The ANFIS and RSM were slightly better than ANN in the predicted TWR for the milling operation. Generally, ANFIS showed a better predictive capability than RSM and ANN for both MRR and TWR. The optimum process milling parameters for the alloy were a 720-rpm spindle speed, 24 mm/min feed rate and 0.979 mm depth of cut to achieve an optimized predicted MRR of 1272.163 mm3/min and TWR of 0.781 mm/min. The optimum milling process was validated at the suggested experimental conditions by carrying out the milling experiment at those conditions, and the results of 1270.05 mm3/min for MRR and 0.77 mm/min for TWR showed a close correlation between the predicted and validated optimum. The optimum milling parameters from this study would enhance production at reduced tooling costs associated with tool wear.

Multi-objective Optimization of CNC Milling Parameters of 7075 Aluminium Alloy Using Response Surface Methodology

Multi-objective Optimization of CNC Milling Parameters of 7075 Aluminium Alloy Using Response Surface Methodology

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.

Optimization of CNC Milling Parameters Based on Tool Life and Process Carbon Emission Prediction Models

Aiming at the problems of short tool life and high carbon emission in the process of CNC milling, a multi-objective optimization method of process parameters based on the BP neural network and NSGA-II algorithm is proposed. Based on the data sets generated by CNC milling under different process parameters, with tool life and process carbon emissions as outputs and different process parameter combinations as inputs, a BP neural network-based tool life and process carbon emissions prediction model is constructed. Taking the maximum tool life and minimum process carbon emission as the optimization goals, the NSGA-II process parameter multi-objective optimization main model is constructed. The model is then used as the objective function of the main model and optimized to obtain the Pareto optimal solution set. The optimal solution decision is made on the Pareto optimal solution set using the Topsis method, and the optimal process parameter combination is obtained. The optimization results show that the method can accurately predict the tool life and process carbon emission of CNC milling. It can also effectively optimize the process parameters, which provides a new method for the low-carbon optimization of CNC milling parameters.

Accuracy of on-site teleoperated milling with haptic assistance

PurposeIn bone surgery specialties, like orthopedics, neurosurgery, and oral and maxillofacial surgery patient safety and treatment success depends on the accurate implementation of computer-based surgical plans. Unintentional plan deviations can result in long-term functional damage to the patient. With on-site teleoperation, the surgeon operates a slave robot with a physically-decoupled master device, while being directly present at the operation site. This allows the surgeon to perform surgical tasks with robotic accuracy, while always remaining in the control loop.MethodsIn this study the master- and slave-side accuracy of an on-site teleoperated miniature cooperative robot (minaroHD) is evaluated. Master-side accuracy is investigated in a user study regarding scale factor, target feed rate, movement direction and haptic guidance stiffness. Scale factors are chosen to correspond to primarily finger, hand, and arm movements. Slave-side accuracy is investigated in autonomous milling trials regarding stepover, feed rate, movement direction, and material density.ResultsMaster-side user input errors increase with increasing target feed rate and scale factor, and decrease with increasing haptic guidance stiffness. Resulting slave-side errors decrease with increasing scale factor and are < 0.07 mm for optimal guidance parameters. Slave-side robot position errors correlate with the feed rate but show little correlation with stepover distance. For optimal milling parameters, the 95th percentile of tracked slave-side position error is 0.086 mm with a maximal error of 0.16 mm.ConclusionFor optimal guidance and milling parameters, the combined error of 0.23 mm is in the range of the dura mater thickness (< 0.27 mm) or mandibular canal wall (~ 0.85 mm). This corresponds to safety margins in high-demand surgical procedures like craniotomies, laminectomies, or decortication of the jaw. However, for further clinical translation, the performance and usability of on-site teleoperated milling must be further evaluated for real-life clinical application examples with consideration of all error sources in a computer-assisted surgery workflow.

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.

Optimal Machining Parameters Determination of Polymers-Ceramic Composite for Hybrid Manufacturing

The use of technical ceramics is growing thanks to their high mechanical properties. To attain the tolerances and surface finish, operations including machining or grinding are performed. Due to the risk of fracture, they are limited to low material removal rates.One promising option is to rely on the hybridization of manufacturing process. This paper is linked to the project HyProPAM that combines additive and substractive techniques. An experimental methodology to determine the optimal milling parameters parts in Zirconia is proposed. Cutting forces and surface topography are used as indicator to determine the quality of the operation.

A Study on Improving the Machining Performance of Scrolls

To improve machining efficiency and product precision in scroll manufacturing, we studied three adaptive milling processes: adaptive feed rate planning, chatter suppression measures, and optimization of milling parameters during the rough, semi-fine, and fine machining of scrolls. In the rough machining of scrolls, adaptive feed rate planning was used to compute the cutting area per cutter tooth in real time in order to adjust the feed rate and optimize the material removal rate (MRR) under a given maximum acceptable cutting load. To suppress the possibility of chatter in the semi-fine and fine machining processes, the chatter frequencies were detected with a microphone and the spindle speeds were promptly modified using a developed program in combination with the controller of the milling machine. Based on the Taguchi method and analysis of variance (ANOVA), we determined the optimum milling parameters for the fine machining processes to improve contour characteristics, such as the profile errors and surface roughness of scrolls. Experimental tests were implemented to demonstrate and verify the feasibility of the adaptive milling processes proposed in this study.

Microscopic and structural study on the formation of mechanochemical synthesized BaTiO3 and ZnTiO3 perovskites

AbstractIn this work, the properties of mechanochemically produced (by using mills made from different materials) barium-titanate (BaTiO3) and zinc-titanate (ZnTiO3) perovskites are compared. Mechanochemistry is a process that can cover the energy demand of some reaction pathways between solid materials. This process is called “high-energy milling”, for which not all types of mills are suitable. In our case, a planetary ball mill provided the necessary energy. Using a model, the required energy is determinable; the energy released during an impact of a milling ball (Eb – ball-impact energy), as well as during the whole milling (Ecum – cumulative milling energy). Thus, a milling-energy map was created, with which the applied Eb and Ecum values were visualized depending on the different grinding parameters. The parameters changed were the material of the grinding vessels, the number of grinding balls, and the rotational speed. The transformation was tracked by X-ray diffraction (XRD) measurements, and electron microscopic images (TEM and SEM) of the perovskites produced were taken. This study aimed to draw conclusions that will help later in the synthesis of materials with other perovskite structures by choosing optimal milling parameters.

Nanocrystal Preparation of Poorly Water-Soluble Drugs with Low Metal Contamination Using Optimized Bead-Milling Technology.

Nanocrystal preparation using bead milling is an important technology to enhance the solubility of poorly water-soluble drugs. However, there are safety concerns regarding the metal contaminants generated during bead milling. We have previously reported optimized bead-milling parameters that could minimize metal contamination and demonstrated comparable performance to NanoCrystal®, a world-leading contamination-free technology. This study aimed to investigate the applicability of optimized milling parameters for preparing nanocrystals of several poorly water-soluble drugs exhibiting various physicochemical properties. Using our optimized bead-milling parameters, we found that all the tested drugs could be ground into nanosized particles within 360 min. Notably, fenofibrate, which has a low melting point, could be ground into nanosized particles owing to the low level of heat generated during bead milling. Additionally, the concentration of metal contaminants in all the drugs prepared using the optimized milling parameters were approximately ten to twentyfold lower than those prepared without the optimized parameters and were comparable to those prepared using polycarbonate beads, known to minimize metal contamination during bead milling. Our results provide insights into the development of drug nanocrystals with low metal contamination using bead milling.

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.

A novel milling parameter optimization method based on improved deep reinforcement learning considering machining cost

The cutting parameters in the part machining process have a great impact on the energy efficiency and economy of the machining system. The cutting parameters in traditional machining are often selected according to the operator's experience, which lack attentions to energy saving and economy. Therefore, a milling process parameter optimization method based on deep reinforcement learning (DRL) is proposed in this paper. Taking the machining cost composed of cutting energy efficiency and machining time cost as the optimization goal, the spindle speed and feed speed under the combination of different cutting depth and cutting width parameters are optimized. Firstly, the machine tool energy consumption model is established by back propagation neural network (BPNN) regression method to realize the continuity of machine tool energy consumption state prediction, and the processing cost model is established as the optimization objective function. Then, the process parameter optimization problem is formally expressed as a Markov decision process (MDP), and the corresponding states, actions, reward functions and constraints are defined. Finally, combined with the machine tool power consumption model and machining cost model, the simulation environment is established, and the BP-TD3 method is proposed to solve the Markov decision problem of milling parameter optimization. Taking the machining center as an example, the aluminum alloy workpiece is milled. Compared with the classical optimization algorithm, the proposed method can save 95 % optimization calculation time, and ensure that the average processing cost after optimization is close to the minimum processing cost obtained by the classical optimization algorithm.