Mechanical Alloying Process Applied for Obtaining a New Biodegradable Mg-xZn-Zr-Ca Alloy

The aim of the present paper is to apply the mechanical alloying process to obtain from powder components a new biodegradable Mg-based alloy powder from the system Mg-xZn-Zr-Ca, with high biomechanical and biochemical performance. Various processing parameters for mechanical alloying have been experimented with the ultimate goal to establish an efficient processing route for the production of small biodegradable parts for the medical domain. It has been observed that for the same milling parameters, the composition of the powders has influenced the powder size and shape. On the other hand, for the same composition, the highest experimented milling speed and time conduct to finer powder particles, almost round-shaped, without pores or various inclusions. The most uniform size has been obtained for the powder sample with 10 wt.%Zn. These powders were finally processed by selective laser melting, an additive manufacturing technology, to obtain a homogeneous experimental sample, without cracking, for future more systematical trials.

Optimization of Process Parameters on Surface Hardness and Energy Consumption in Milling of 7050 Aluminum Alloy using Enhanced NSGA-II

Aluminum alloy has high strength and light weight. It is widely used for aircraft fuselage, propellers and other parts which work under high load conditions. High-quality parts made of aluminum alloy processed by computerized numerical control (CNC) machine often have the characteristics of high cost in their processing. In order to achieve high surface quality and control processing costs, this article takes the workpiece surface hardness and machining energy consumption as targets. Intelligent optimization algorithm is used to find the optimal combination of milling parameters to obtain ideal targets. CNC milling parameter optimization is a multi-parameter, multi-objective, multi-constraint, discrete nonlinear optimization problem which is difficult to solve. For this challenge, an improved NSGA-II is presented, named enhanced population diversity NSGA-II (EPD-NSGA-II). EPD-NSGA-II is improved with the normal distribution crossover, adaptive mutation operator of differential evolution, crowding calculation method considering variance and modified elite retention strategy to achieve enhanced population diversity. 12 test functions are chosen for experimentation to verify the performance of the EPD-NSGA-II. The values of three evaluation indicators show that the proposed approach has good distribution and convergence performance. Finally, the approach is applied in the milling parameters optimization of 7050 aluminum alloy to get the optimal solutions. Results indicate that the EPD-NSGA-II is effective in optimizing the problem of milling parameters.

Prediction Modeling and Sensitivity Analysis of Robot Bone Milling Temperature Operated by a Doctor

Bone milling is a common method in robot orthopedic surgery. However, excessive milling temperature will cause thermal necrosis of bone cells and tissues. It is necessary to carry out further research and analysis on the robot bone milling process considering the lamina milling skills of spinal surgeons and clinical practice to reduce the damage to bone cells and nearby tissues and obtain good cutting surface quality. Considering the randomness of milling parameters during operation, a prediction method of milling temperature model for ball milling cutter considering the doctor’s surgical skills was proposed based on response surface method. Because of material anisotropy and microstructure difference between the cortical bone and cancellous bone, this paper would analyze the influencing factors in different bone layers to establish the prediction model of milling temperature in the segments of cortical bone and cancellous bone. Also, the influence and distribution of milling parameters on temperature in three cutting modes such as parallel cutting mode, cross cutting mode, and vertical cutting mode in the cortical bone region were analyzed. The parameter sensitivity of the milling temperature prediction model was analyzed by the Sobol method, and the influence of the input parameters on the output milling temperature was analyzed quantitatively.

Optimization of Milling Parameters of Unmodified Calotropis Procera Fiber-Reinforced PLA Composite (UCPFRPC)

This study involves the optimization of the milling parameters of unmodified Calotropis Procera fiber-reinforced PLA composite (UCPFRPC). The material is prepared from the combination of 20% Calotropis-Procera and 80% of PLA by weight. The experiments are designed using the Taguchi methodology, where 16 experiments are obtained using the spindle rotational speed, depth of cut, and feed rate as the parameters. These experiments were conducted while obtaining thermal images using an infrared camera and recording the machining time. The change in mass was then determined and the material removal rate computed. The machined workpieces were then investigated for surface roughness. The study shows that the optimal milling parameters in the machining of UCPFRPC for the lowest surface roughness are 400 rpm, 400 mm/min, and 0.2 mm, for the rotational spindle speed, feed rate, and depth of cut. The parameters were 400 rpm, 100 mm/min, and 1.2 mm for the largest MRR, and 400 rpm, 400 mm/min, and 0.2 mm for the least average milling temperature. In all the responses, the depth of cut is the most significant factor.

Milling Force Coefficients-based Tool Wear Monitoring for Variable Parameters Milling

Tool wear is an important factor that affects the aeronautical structural parts' quality and machining accuracy in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

Optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm

According to the characteristics of NC milling, an approach for optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm is proposed. Taking the carbon emission and processing time as the objectives, the cutting rate, feed per tooth, and cutting width as the optimization variables. A multi-objective optimization model of NC milling parameters is established. An non-dominated sorting gravity search algorithm (NSGSA) is used to solve the multi-objective model, and the position update backoff operation is introduced. Finally, taking NC machining process as an example, the multi-objective optimization results and the single objective optimization results are compared respectively, the actual data show that when the optimization objective is high efficiency and low carbon, the processing time and carbon emissions are 173 and 192 respectively. The comparison results show that the combination of processing parameters obtained by multi-objective optimization is the best, the optimal parameter combination obtained by NSGSA algorithm is verified by grey correlation analysis, and the grey correlation degree of the optimal solution set is 0.81, which is the largest in all solution sets. This approach can help the decision-makers flexibly select the corresponding milling parameters, and provide decision-makers with flexible selection decisions suitable for various scenarios.

The Effect of High-Energy Ball Milling Conditions on Microstructure and Hydrogen Desorption Properties of Magnesium Hydride and Single-Walled Carbon Nanotubes

Magnesium hydride is considered to be one of the most promising hydrogen storage materials, although it nevertheless has some problems, such as the high value of the activation energy of hydrogen desorption. To solve this problem, some scientists have proposed adding nanocarbon materials, in particular carbon nanotubes, to magnesium hydride. Currently, a detailed understanding of the mechanisms of obtaining composites based on magnesium hydride and carbon nanotubes is lacking, as is our understanding of the effect of nanocarbon additives on the activation energy and temperature of hydrogen desorption depending on the parameters of the composite synthesis. In addition, the data obtained at various values of milling parameters are very different, and in some works the effect of carbon nanomaterials on the hydrogen properties of magnesium hydride was not confirmed at all. Thus, it is important to determine the effect of nanocarbon additives on the properties of hydrogen storage of magnesium hydride under various milling parameters. This work is devoted to the study of the effect of nanocarbon additives on magnesium hydride and the determination of the dependences of the hydrogen desorption temperature and activation energy on the synthesis parameters. Composite powders containing MgH2 with 5 wt.% single-walled carbon nanotubes (SWCNT) were prepared using a planetary ball mill. The milling was carried out at various milling speeds, namely 300, 660, and 900 rpm. Results suggested that the structure of the nanotubes is preserved with prolonged grinding of magnesium hydride and SWCNT in a ball mill for 180 min at a relatively low grinding speed of 300 rpm. The composite obtained with these parameters has the lowest temperature of hydrogen desorption and an activation energy of H2 desorption of 162 ± 1 kJ/mol H2, which is 15% lower than that of the magnesium hydride MgH2 (189 ± 1 kJ/mol H2).