Effect of Machining Parameters and Tool Wear on Surface Uniformity in Micro-Milling

Multi-Objective Optimization of Process Parameters during Micro-Milling of Nickel-Based Alloy Inconel 718 Using Taguchi-Grey Relation Integrated Approach

This research investigates the machinability of Inconel 718 under conventional machining speeds using three different tool coatings in comparison with uncoated tool during milling operation. Cutting speed, feed rate and depth of cut were selected as variable machining parameters to analyze output responses including surface roughness, burr formation and tool wear. It was found that uncoated and AlTiN coated tools resulted in lower tool wear than nACo and TiSiN coated tools. On the other hand, TiSiN coated tools resulted in highest surface roughness and burr formation. Among the three machining parameters, feed was identified as the most influential parameter affecting burr formation. Grey relational analysis identified the most optimal experimental run with a speed of 14 m/min, feed of 1 μm/tooth, and depth of cut of 70 μm using an AlTiN coated tool. ANOVA of the regression model identified the tool coating parameter as most effective, with a contribution ratio of 41.64%, whereas cutting speed and depth of cut were found to have contribution ratios of 18.82% and 8.10%, respectively. Experimental run at response surface optimized conditions resulted in reduced surface roughness and tool wear by 18% and 20%, respectively.

Dimensional Accuracy and Surface Quality of AZ91D Magnesium Alloy Components after Precision Milling

This study investigates a precision milling process conducted with the use of conventional end mills and a standard CNC (Computer Numerical Control) machine tool. Milling tests were performed on samples of AZ91D magnesium alloy using TiB₂- and TiAlN-coated three-edge end mills measuring 16 mm in diameter. The following technological parameters were made variable: cutting speed, feed per tooth and axial depth of cut. The effects of precision milling were evaluated by analysing the scatter of dimension values obtained in successive tool passes. In addition to that, deviations from the assumed nominal depth as well as obtained ranges of dimension varation were analysed. The study also examined surface quality obtained in the precision milling process, based on the basic surface roughness parameters: Ra, Rz and RSm. Results have confirmed that the use of conventional cutting tools and a standard CNC machine tool makes it possible to manufacture components characterized by relatively small scatter of dimension values and high accuracy classes. Additionally, the results have shown that the type of tool coating and variations of individual technological parameters exert impact on the dimensional accuracy and surface quality obtained.

Experimental Optimization of Process Parameters in CuNi18Zn20 Micromachining

Ultraprecision micromachining is a technology suitable to fabricate miniaturized and complicated 3-dimensional microstructures and micromechanisms. High geometrical precision and elevated surface finishing are both key requirements in several manufacturing sectors. Electronics, biomedicals, optics and watchmaking industries are some of the fields where micromachining finds applications. In the last years, the integration between product functions, the miniaturization of the features and the increasing of geometrical complexity are trends which are shared by all the cited industrial sectors. These tendencies implicate higher requirements and stricter geometrical and dimensional tolerances in machining. From this perspective, the optimization of the micromachining process parameters assumes a crucial role in order to increase the efficiency and effectiveness of the process. An interesting example is offered by the high-end horology field. The optimization of micro machining is indispensable to achieve excellent surface finishing combined with high precision. The cost-saving objective can be pursued by limiting manual post-finishing and by complying the very strict quality standards directly in micromachining. A micro-machining optimization technique is presented in this a paper. The procedure was applied to manufacturing of main-plates and bridges of a wristwatch movement. Cutting speed, feed rate and depth of cut were varied in an experimental factorial plan in order to investigate their correlation with some fundamental properties of the machined features. The dimensions, the geometry and the surface finishing of holes, pins and pockets were evaluated as results of the micromachining optimization. The identified correlations allow to manufacture a wristwatch movement in conformity with the required technical characteristics and by considering the cost and time constraints.

Effect of Tool Coating and Cutting Parameters on Surface Roughness and Burr Formation during Micromilling of Inconel 718

Surface roughness and burr formation are among the most important surface quality metrics which determine the quality of the fabricated parts. High precision machined microparts with complex features require micromachining process to achieve the desired yet stringent surface finish and dimensional accuracy. In this research, the effect of cutting speed (m/min), feed rate (µm/tooth), depth of cut (µm) and three types of tool coating (AlTiN, nACo and TiSiN) were analyzed to study their effect on surface roughness and burr formation during the micromachining of Inconel 718. The analysis was carried out using an optical profilometer, scanning electron microscope and statistical technique. Machining tests were performed at low speed with a feed rate (µm/tooth) below the cutting-edge radius for 10 mm cutting length using a carbide tool of 0.5 mm diameter on a CNC milling machine. From this research, it was determined that the depth of cut was the main factor affecting burr formation, while cutting velocity was the main factor affecting the surface roughness. In addition, cutting tool coating did not significantly affect either surface roughness or burr formation due to the difference in coefficient of friction. The types of burr formed during micromilling of Inconel 718 were mainly influenced by the depth of cut and feed rate (µm/tooth) and were not affected by the cutting velocity. It was also concluded that the results for the surface finish at low-speed machining are comparable to that of transition and high-speed machining, while the burr width found during confirmation experiments at low-speed machining was also within an acceptable range.

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm’s hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R² = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R² = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

Analytical Investigation of the Micro Groove Surface Topography by Micro-Milling

Micro-milling is an emerging processing technology for machining micro- and high-precision three dimensional parts that require the use of various materials (with sizes ranging from tens of micrometers to a few millimeters) in the field of advanced manufacturing. Therefore, it can be applied to manufacture the micro parts, but new challenges are raised about parts with high surface quality. Herein, both surface formation and micro machined surface roughness models are studied, with the aim of solving complicated problems regarding the quality of surface finish when micro-milling metallic materials. From a theoretical point of view, the first model for surface formation processes considering the strain gradient plasticity theory was built in the area around the cutting edge, and the minimum uncut chip thickness equation was derived. The model accounts for the properties of the work material in tertiary and quaternary zones on the minimum chip thickness. A second model for micro machined surface roughness based on the relationship of kinematics between cutting process and cutter edge was also developed, which takes the influences of tool run out into account. Both proposed models were introduced to analyze the tendency of surface roughness for micro grooves. Both models were also used to justify experimental results. The results show that the developed surface roughness model could be useful in predicting both roughness parameters and trends as a function of cutting parameters.