Milling Parameters Research Articles

From Powders to Alloys: A Study of the Mechanical Alloying Process and Sintering

This study explores the intricacies of mechanical alloying, aiming to unlock its potential in modern engineering. It investigates the impact of milling duration, ball-to-powder ratio, and sintering temperature on the microstructure and mechanical properties of Al-7Zn-2.5Mg-2.5Cu alloy and Al7075 alloy. Mechanical alloying can produce alloys with exceptional hardness, strength, and ductility, while grain size can be controlled by adjusting milling parameters. Experimental techniques like X-Ray diffraction and transmission electron microscopy reveal microstructural changes during mechanical alloying, aiding in understanding metastable phases and element segregation, which influence the alloy’s properties. Sintering, the subsequent consolidation step, determines final properties, with a trade-off between grain size and mechanical qualities observed at different sintering temperatures. This trade-off presents an intriguing avenue for developing materials with optimal properties. The study also explores potential applications of mechanical alloying across industries, including aerospace, biomedical, and energy. These unique mechanical alloys are attractive for various uses, from structural to catalytic and magnetic materials. They have the potential to revolutionize industries and drive technological advancements.

On the preparation and mechanical testing of nano to micron-scale specimens

Over the past two decades, means allowing to probe experimentally the mechanical behavior of materials specimens only a few micrometers or less in diameter have multiplied, largely owing to the widespread adoption and versatility of focused ion beam (FIB) milling for the preparation of small-scale test specimens. Despite its remarkable capabilities, the ion bombardment that is employed during FIB milling operations does not leave machined surfaces unscathed: it may induce a series of alterations in the near-surface microstructure of materials, which vary in severity depending on the material being milled, or milling parameters, or the type of ion used. These alterations in turn can strongly influence the local behavior, and as a result measured mechanical properties, of the milled material. In the first part of this manuscript, we review the different forms that FIB-induced microstructural alterations can take and their influence on small-scale mechanical test results. In the second part, we present alternative strategies that have been used to circumvent the issue. These include the use of entirely different small-scale sample fabrication processes, as well as approaches that do use FIB-milling but put effort in the test design to minimize or avoid the formation of FIB-induced defects in regions where micromechanical test data are collected. The advent of such methods can enhance our understanding of FIB-induced defects on the mechanical behavior of microsamples by comparing the results with those from ion-milled samples. This, in turn, should improve our ability to interpret test data when FIB-milling is the only method available for microsample production.

An experimental study on material removal rate and surface roughness of Cu-Al-Mn ternary shape memory alloys using CNC end milling

Abstract This study investigates the impact of Computer Numerical Control (CNC) milling parameters on Cu-Al-Mn SMAs (Shape memory alloys) to evaluate the effects on Surface Roughness (SR) and Material Removal Rate (MRR). The primary variables examined comprise of cutting speed, feed rate, and depth of cut. Results indicate that the Shape Memory Effect (SME) is higher in Copper Aluminium Manganese (CAM 3) compared to CAM 1 and CAM 2, with SME improving from 3.5% to 5.5% as Manganese (Mn) content increases, reflecting an increase in dislocations within the metal's crystal structure. Surface roughness increases with higher feed rates and depths of cut but decreases with increased cutting speed. MRR shows a positive correlation with feed rate, depth of cut, and cutting speed, though it decreases with higher Mn content. Notably, CAM 3 exhibits lower MRR compared to CAM 1 and CAM 2. Scanning Electron Microscopy (SEM) reveals that at lower feed rates (0.10 mm/rev), the surface is smooth and free of ridges or feed marks, while at higher feed rates (0.18 mm/rev), noticeable surface imperfections and plastic deformation occur. The addition of Mn improves surface smoothness and machinability, it also affects MRR. Further suggesting that Mn content and milling parameters significantly influence both the mechanical properties and machinability of Cu-Al-Mn SMAs respectively.

Research on the service life of bearings in the gearbox of rolling mill transmission system under non-steady lubrication state

A rolling bearing plays a key role in the gearbox of the cold rolling mill transmission system. As a result, the degradation and failure of bearings can lead to unplanned shutdowns of the entire rolling mill system. Since on-site cold rolling mills work usually in non-steady lubrication rolling conditions originating form variations of multiple parameters, the uncertainty affecting bearing performance in a cold rolling mill transmission system increases, making it more difficult to assess health status and predict the remaining service life of bearings, Therefore, by establishing a coupling model describing the relationship for the rolling mill and normal /faulty gearboxes under non steady rolling conditions, quantitatively studies the influence of various rolling parameters of a rolling mill on the fatigue service life of bearings in the gearbox of the rolling mill transmission system, and verify the effectiveness of linear cumulative damage theory for bearing fatigue service life through experiments and on-site bearing vibration data. The results indicate that as the thickness of the strip steel inlet, lubricant viscosity, and rolling speed increase, or the thickness of the strip steel outlet and rolling roll radius decrease, the service life of bearings gradually decreases. As the fluctuation amplitude of various rolling parameters in the cold rolling mill increases, the service life of bearings in the gearbox of the rolling mill transmission system gradually shortens. Moreover, when the rolling parameter value or fluctuation amplitude increases to a specific values, the remaining service life of bearings will sharply decrease. Under the same rolling conditions, the service life of bearings in gearboxes with faults decreases more significantly than that in normal gearboxes.

The influence of mechanochemical activation on the rheological properties and strength development of geopolymer

To address the significant carbon emissions caused by the cement industry and its contribution to the greenhouse effect, there is an urgent need for new construction materials that can substantially reduce the use of traditional Portland cement. Alkali-activated binders, known for their excellent performance and lower carbon footprint, have emerged as a promising alternative. Despite the numerous advantages of geopolymers, their practical application is hindered by challenges in processability required for construction techniques, such as pumping, spreading, and forming. This study focuses on enhancing the rheological properties of geopolymers through mechanochemical activation and exploring the modification mechanisms involved. Slag and fly ash raw materials were processed under different ball milling conditions, specifically varying ball milling rotation speed (BR) and ball milling time (BT). The mechanical properties, workability, and rheological behavior of geopolymer specimens were evaluated to identify the optimal milling parameters affecting these properties. Detailed analysis using XRD, SEM, and heat of hydration tests elucidated the phase composition, microstructural evolution, and thermal characteristics of mechanochemically modified geopolymers, providing insights into the fundamental mechanisms of mechanochemical activation modification. The results indicate a significant correlation between ball milling parameters and the rheological properties of geopolymers. The optimal milling regime was identified as grinding at a speed of 70r/min for 50 min. This specific milling condition imparts ideal properties to the geopolymer, including moderate yield stress (34.353 Pa), low plastic viscosity (0.452 Pa s), good thixotropy, and high 28d strength (53.28 MPa), without any significant shear thickening behavior.

Powder mixed micro-electric discharge milling of Ni-rich NiTi SMA: an investigation on machining performance and biocompatibility

ABSTRACT Nickel and titanium (NiTi) based shape memory alloys (SMAs) are novel materials endowed with exceptional super-elasticity, corrosion resistance, and shape memory capabilities, making them well-suited for biomedical and industrial applications. The current study explores the impact of process parameters in powder mixed micro-electrical discharge milling (µ-EDM) on NiTi SMA, focusing on pulse on time (TON), voltage, pulse off time (TOFF), and RPM individually, employing a one factor at a time (OFAT) methodology to analyze surface characteristics, surface roughness (Ra), and material removal rate (MRR). The impact of black phosphorus powder combined with sesame oil (dielectric) on the surface of fabricated channels in commonly used biomaterials was investigated. The cytotoxicity test for biocompatibility was carried out to enhance the cell viability up to 79.89% after machining, which showed enhanced cell viability. The highest MRR the lowest Ra were obtained at the best input factors combination of 24 µs (TON), 6 µs (TOFF), 240 V, and 50 RPM tool rotation. Improved micro-channel quality was observed with lower TON, voltage, and RPM.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

Multi-objective optimization of EN19 steel milling parameters using Taguchi, ANOVA, and TOPSIS approaches

This study focuses on optimizing milling parameters for EN19 steel through the Taguchi method. Utilizing an L9 orthogonal array with three parameters each set at three levels, the study evaluated four quality characteristics in every run. The optimization process involved the use of Signal-to-Noise Ratio (SNR), Analysis of Variance (ANOVA), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine optimal conditions and identify critical parameters influencing surface roughness, temperature, cutting force, and material removal rate.ANOVA results underscore the significant impact of spindle speed, cutting fluids, and depth of cut (DOC). The TOPSIS analysis identified the optimal conditions as a spindle speed of 710 rpm, a DOC of 0.5 mm, and the use of Neem oil combined with graphene as the cutting fluid. Among these factors, the depth of cut was found to be the most influential, followed by spindle speed and cutting fluid. Confirmation tests corroborated the effectiveness of the optimization approach, affirming its potential to improve milling outcomes. This research offers valuable insights into enhancing machining efficiency and product quality in the milling of EN19 steel.

Pulse current enhanced densification and mechanical property of Inconel 718 superalloy fabricated by spark plasma sintering

The Inconel 718 superalloy was fabricated using spark plasma sintering (SPS) technology with varying sintering temperatures and heating rates. The densification and mechanical property of the sintered superalloys were characterized through tensile and compression tests at room temperature, as well as microscopic analyses. The evidence suggests that the plasma generated between the particles results in evaporative melting on the particle surface and plastic deformation of the particle boundaries, accelerating the densification process is by 3∼4 times. Besides, the grain size refinement coefficient affected by pulse current in the holding stage is 0.7–0.8, leading to a significant grain boundary strengthening during SPS process. The dislocation dominated by ball milling and SPS parameters also helps improve the strength and plasticity of the sintered superalloy. The superalloy sintered at 1050 °C with a heating rate of 100 °C/min demonstrated the highest tensile and compressive fracture strengths, measuring 1410 MPa and 1983 MPa, respectively, with the tensile and compressive yield strength of 712 MPa and 1557 MPa, respectively. Lower sintering temperatures or higher heating rates results in lower levels of densification and decreased strength. In addition, plasticity is reduced at higher sintering temperatures or lower heating rates due to excessive precipitation of brittle phases.

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

The effect of cutting parameters on defect detection based on recurrence analysis of cutting force signals obtained from GFRP composite milling

This article presents the results of a recurrence analysis conducted on the milling of GFRP material. The novelty lies in applying variable milling parameters to assess the detectability of defects. It appears that cutting parameters play a significant role in detecting defects. The research aimed to determine the sensitivity of recurrence analysis in detecting defects of various sizes in composites machined with different milling parameters. Furthermore, the study identified the most appropriate quantifications for defects detection. The values of some quantifications derived from the cutting force showed significant changes compared to the indicators obtained during machining without defects. It was demonstrated that recurrence quantification analysis could identify defects as small as 0.25 mm, while recurrence diagrams alone could detect defect sizes of 0.75 mm or larger. The most effective indicators were RR, T1, and T2. Additionally, it was demonstrated that certain indicators depend on cutting parameters, complicating their use for defect detection.

Analysis and optimization of machining parameters of AZ91 alloy nanocomposite with the Influences of nano ZrO2 through vacuum diecast process

The magnesium alloy composite is a vital material for automotive applications due to its features like high stiffness, superior damping resistance, high strength, and lightweight. Here, the motto of research is to establish the AZ91 alloy nanocomposite with the exposures of 0, 1, 3, and 5 volume percentages (vol%) of nano zirconium dioxide (ZrO2) particles (50nm) through fluid stir metallurgy route associated with 1x105 Pa vacuum die cast process. Exposures on structural morphology, hardness, and impact toughness of composite are analyzed and identified as the nano AZ91 alloy composite enclosed with 5vol% is homogenous particle dispersion, enhanced hardness (97.6HV), and optimum toughness of 21.2J/mm2. However, composite faces machining difficulties due to the hard abrasive particles with higher hardness, resulting in tool wear. This experiment predicts the optimum mill parameters during the end mill operation of magnesium alloy nanocomposite (AZ91/5vol%) by using a tungsten carbide coated end mill cutter to attain the maximum metal removal rate with low surface roughness and tool wear analyzed via the general linear model (GLM) ANOVA approach. The input conditions for end milling operation vary, like feed rate (0.1 -0.4mm/rev), depth of cut (0.05 -0.2mm), and spindle speed (250-1000rpm). During the ANOVA GLM approach, the L16 design experiment is fixed for further interaction analysis. The results predicted by the depth to cut and feed rate were dominant and played a major role in deciding the tool wear, surface roughness, and MRR.

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

PurposeThis 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/approachIn 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.FindingsThis 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/valueThis 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.

Modelling and optimization method for energy saving of computer numerical control machine tools under operating condition

The widespread application of machine tools in industry results in very high energy consumption. In order to save energy, it is an effective means to reduce the processing power while improving the processing efficiency of machine tools. Since milling parameters directly affect machine performance, the selection of suitable milling parameters is particularly important. In this regard, this study proposes an integrated modelling and optimization method for energy saving of computer numerical control (CNC) machine tools under operating condition. Firstly, the operating condition of the CNC machine are analyzed and the processing power and efficiency mathematical models are established based on the four milling parameters (n, fz, ae, ap). Subsequently, the Pareto optimal solution is introduced to establish an optimization model integrating the processing power and time. To solve the optimization problem, an adaptive chaotic multi-objective chimp algorithm with fast convergence and satisfactory optimization is proposed considering three aspects: search space, convergence factor, and chaotic mapping. Afterwards, further degrees of freedom and sensitivity analyses were carried out on the constructed model using the Sobol method. Finally, comparative analysis and validation are conducted through simulation cases and actual processing experiments, respectively. The results show that both validation scenarios have the same conclusion. Compared with three existing algorithms, the proposed method reduces the processing power by 5.63 %, 4.65 % and 4.51 % and improves the processing efficiency by 27.88 %, 15.11 % and 15.31 %, respectively. Based on the above enhancements, the energy consumption is reduced by 58.21 %, 45.54 % and 41.45 % respectively.

Predicting error for machining thin-walled blades considering initial error

A multistage machining process is employed to machine the blades with low stiffness. Nonetheless, machining errors can be transferred and accumulate throughout the multistage machining process, complicating the precise prediction of the final accuracy of thin-walled blades. Consequently, this paper introduces a machining accuracy prediction model for thin-walled blades that takes into account initial error. The machining error prediction model of thin-walled blades is developed using Gaussian process regression optimized by the sparrow search algorithm (SSA-GPR) with the initial contour error, depth of cut, feed per tooth, and spindle speed as inputs, and the machining error as the output. And the results show that the prediction accuracy of the SSA-GPR is 6.73 % higher than that of the Gaussian process regression (GPR), 13.73 % higher than that of the back propagation neural network (BPNN), and 32.32 % higher than that of the support vector machine regression (SVR). The influence of the initial error and milling parameters on the machining error is analyzed through the length-scales of the Gaussian kernel function. The findings indicate that the depth of cut, feed per tooth and initial error significantly affect the machining error, whereas the spindle speed has a minor impact on the machining error. Furthermore, the 3D graph based on the SSA-GPR shows that the increase of the initial error will increase the machining error of thin-walled blades. This research provides a theoretical foundation for the process optimization of thin-walled blades.

A Microscopy Evaluation of Emergence Profile Surfaces of Dental Custom CAD-CAM Implant Abutments and Dental Implant Stock Abutments.

Recently, due to the high demand for dental implants, the use of dental implant stock abutments has increased significantly, especially dental custom CAD/CAM implant abutments milled by dental technicians in their laboratories. The purpose of this study is to analyze the surface quality of the emergence profile of dental custom CAD/CAM implant abutments made by a non-industrial milling machine, compared to original and compatible dental implant stock abutments made by industrial machines. Thirty dental implant abutments were divided into six study lots. Lot 1 (control group): original dental implant stock abutments-industrial machined; lot 2 (study group): compatible dental implant stock abutments-industrial machined; lots 3, 4, 5, and 6 (study groups): compatible custom CAD/CAM dental implant abutments-non-industrial milled with hyperDENT CAM software and Paragon Tools. The Nikon SMZ745T stereomicroscope was used to analyze the emergence profile surface of each dental implant abutment. The structure of the analyzed surfaces did not show significant differences between original and compatible abutments that were industrially machined. As for the customized dental implant abutments, the greatest similarity with the original was obtained for lot 6, and a significant statistical difference was obtained for lot 4. Stepover and Feed Rate parameters of the milling process influenced the surface roughness of the emergence profile for the customized dental implant abutments. The digital technology of non-industrial milling compatible custom CAD/CAM dental implant abutments is reliable and within the correct milling parameters.

Sustainable high-speed milling enhancement of GnP-reinforced titanium nanocomposites under dry environment

No studies have been done on using graphene nanostructure reinforcement material to improve titanium alloy's dry high-speed machining performance, which is crucial for green manufacturing. This study seeks to understand the impact of graphene on improving the high-speed machining of Ti6Al4V (Ti64) nanocomposites. High-density Ti64-based nanocomposite samples with varying amounts of GnPs (0 wt%, 0.6 wt%, 1.2 wt%, and 2.4 wt%) were fabricated using ball milling and HFIHS sustainable technologies. Subsequently, a detailed investigation was conducted to examine the impact of the GnP contents on high-speed milling performance, namely, cutting force components, surface quality, and machined surface and subsurface microhardness. The variable milling parameters include the cutting speed and feed rate. The results showed that the inclusion of GnPs significantly affected the dry high-speed milling of the nanocomposites. The nanocomposites containing 1.2 wt% and 2.4 wt% of GnP-Ti64 exhibited lower cutting forces than the base-Ti64 without GnP (0 wt% Gnp). At a 100 m/min cutting speed, the cutting and feed forces decreased by 35% and 44%, respectively, compared to the base-Ti64 specimens. This is attributed to lower machining friction due to the presence of graphene and TiC (formed due to the reaction of Ti and GnP particles during sintering) at grain boundaries, facilitating material removal and reducing cutting force. Although the nanocomposites had a higher microhardness, they all showed improved surface quality in comparison to the base-Ti64 specimens. For example, at a feed rate of 270 mm/min the nanocomposites contain 0.6 wt%, 1.2 wt%, and 2.4 wt.% GnPs showed roughness reductions of 6%, 27%, and 38%, respectively, compared to the base-Ti64. Regarding surface and sub-surface hardness after machining, the 2.4 wt.% GnP samples showed superior performance in terms of minimal post-milling hardness variations compared to the base hardness of the material. All GnP-Ti64 milled specimens showed considerably improved surface morphology compared to the base-Ti64 machined specimens. Overall, the nanocomposites containing 1.2 wt% and 2.4 wt% GnPs are strong contenders for reducing cutting forces, enhancing surface roughness, and lowering the fluctuations in microhardness, leading to green manufacturing.

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.

A study on end mill tool geometry parameters for end milling of 316L: finite element analysis and response surface methodology optimization based on resultant cutting force

A study on end mill tool geometry parameters for end milling of 316L: finite element analysis and response surface methodology optimization based on resultant cutting force

Statistical approach for the preparation of silicon-graphite anodes: The role of oxygen content and crystallite size on electrochemical performance

Increasing the overall performance of Si-based anodes is still challenging because of the influence of various parameters involved in the preparation processes. This study addresses this challenge by employing the design of experiment technique to assess the impact of ball milling parameters such as milling speed, time, ball to powder and medium to powder ratio on the properties of silicon/graphite (Si/Gr) powders, with a focus on their electrochemical performance. Si/Gr powders in 20:80 weight ratio and 4 factor - 2 level full factorial design were used to find the main effects and interactions. Crystallite sizes were calculated using the Scherrer equation, and span values were obtained from the particle size distribution (PSD) analysis. SEM analyses were carried out to check the microstructure of powders. Ultimately, regression equations were created with high adjusted R2 values for crystallite size (93%), contamination (92%), and span (91%), respectively. Optimization experiments were carried out using the created regression equations, and the models were verified. It was found that crystallite size obtained by XRD data is more reliable to assess powder properties on the performance instead of PSD because of the agglomeration at the particle level throughout the milling. Further milling experiments were performed to elaborate the role of oxygen content and crystallite size. Results showed that while initial capacity is strongly related to total oxygen content, decay in the first cycles is correlated to the crystallite size of the silicon powder.