Abrasive waterjet micro-machining of channels in metals: Comparison between machining in air and submerged in water

Abrasive water jet technology can be used for micro-milling using recently developed miniaturized nozzles. Abrasive water jet (AWJ) machining is often used with both the nozzle tip and workpiece submerged in water to reduce noise and contain debris. This paper compares the performance of submerged and unsubmerged abrasive water jet micro-milling of channels in 316L stainless steel and 6061-T6 aluminum at various nozzle angles and standoff distances. The effect of submergence on the diameter and effective footprint of AWJ erosion footprints was measured and compared. It was found that the centerline erosion rate decreased with channel depth due to the spreading of the jet as the effective standoff distance increased, and because of the growing effect of stagnation as the channel became deeper. The erosive jet spread over a larger effective footprint in air than in water, since particles on the jet periphery were slowed much more quickly in water due to increased drag. As a result, the width of a channel machined in air was wider than that in water. Moreover, it was observed that the instantaneous erosion rate decreased with channel depth, and that this decrease was a function only of the channel cross-sectional geometry, being independent of the type of metal, the jet angle, the standoff distance, and regardless of whether the jet was submerged or in air, in either the forward or backward directions. It is shown that submerged AWJM results in narrower features than those produced while machining in air, without a decrease in centerline etch rate.     

Micro-end milling of NiTi biomedical alloys,burr formation and phase transformation

This paper focuses on burr formation in micro-end milling of two nickel–titanium shape memory alloys (SMA), an austenitic and a martensitic NiTi. Phase transformation during machining is also examined.     

Fuzzy c-means clustering based colour image segmentation for tool wear monitoring in micro-milling

Micro-milling is an extensively used micro-machining process for producing high precision 3D components from varied materials. However, tool wear in micro-tools is a big concern, as component accuracy directly depends on it. Also, size effects limit the monitoring by the naked eye, but it can be compensated by implying a proper wear monitoring mechanism. Various direct and indirect methods have earlier been used for monitoring purposes, and considering the needs of the fourth industrial revolution, one of the direct methods, machine vision, when combined with image processing algorithms, can play a more prominent role. Current work focuses on creating a wear monitoring algorithm based on fuzzy c-means clustering technique directly implied on acquired colour micro-tool images. The proposed algorithm has three steps: the first step is Region of Interest (ROI) extraction, where the background is removed, orientation correction is done, and ROI on each tooth is extracted from micro-tool colour images. The second uses the fuzzy c-means technique on ROI to cluster them, from which wear cluster is chosen and morphologically enhanced. The last step performs pixel level measurement and results in numerical wear width. Overall, quantitative results at each step are correlation coefficient of 99 % after image registration, segmentation accuracy of 92 % and wear measurement accuracy of 97 %. A comparison is also made between the proposed algorithm, k-means clustering and RGB thresholding technique, where the proposed algorithm outshines. Lastly, the wear measurement error of the proposed algorithm is less than 5 %, indicating its repeatable, reliable, and robust nature.     

微细铣削铝合金表面粗糙度的实验研究

利用直径为2 mm的立铣刀对铝合金进行铣槽实验,研究分析切削速度与每齿进给量交互作用对加工槽底表面粗糙度的影响.实验结果表明:每齿进给量小于4μm时,随着切削速度的提高表面粗糙度值呈先减小后增大的变化趋势;而每齿进给量大于6μm时,随切削速度的提高表面粗糙度值先增大后减小.切削速度较低时,表面粗糙度值随每齿进给量的增大先减小后增大;但切削速度大于30 m/min时,随着每齿进给量的增大,表面粗糙度值呈现逐渐增大的趋势.     

Femtosecond laser micro-milling dental glass ceramics: An experimental analysis and COMSOL finite element simulation

Dental glass ceramic materials are widely used in all-ceramic restoration technology. In order to effectively solve the problems existing in the process of traditional diamond cutter milling dental glass ceramic materials, such as severe needle loss, large tool wear and general milling efficiency, a new method of ultrafast laser milling dental glass ceramics is proposed. In this paper, 1030 nm femtosecond laser with pulse width of 600fs was used to micro-mill dental glass ceramics. Confocal laser microscopy was used to measure the milling depth and surface roughness of single-layer milling under selected laser processing parameters. The pre-layered milling software was developed to control the z-axis lifting and to compensate the focal length synchronously. Scanning electron microscope (SEM), Raman spectrometer and Vickers micro-hardness tester were used to characterize the dental glass ceramics after femtosecond laser milling. The results showed that under the specific laser processing parameters, the infrared femtosecond laser milling system can achieve a good processing morphology without changing the surface composition and surface hardness of dental glass ceramics. This new dental glass ceramics processing method based on ultrafast laser technique indicated a new direction for further chair processing of dental all-ceramic restoration technology.     

Development of a 2D Vibration Stage for Vibration-assisted Micro-milling

Vibration-assisted machining (VAM) has the advantages of extending tool life, reducing cutting force and improving the surface finish. Implementation of vibration assistance with high frequency and amplitude is still a challenge, especially for a micro-milling process. In this paper, a new 2D vibration stage for vibration-assisted micro-milling is developed. The kinematics of the milling process with vibration assistance is modeled, and the effects of vibration parameters on the periodic tool-workpiece separation (TWS) is analyzed. The structure of the vibration stage is designed with flexure hinges, and two piezoelectric actuators are used to drive the stage in two directions. An amplifier is integrated into the vibration stage, and the dynamics of the whole vibration system are identified and analyzed. Micro-milling experiments are conducted to determine the effects of vibration assistance on cutting force and surface quality.     

Experimental investigation on CO2 laser-assisted micro-slot milling characteristics of borosilicate glass

Demands for micro-machining on glass have been increasing in various industries due to the unique properties of glass, such as transparency or biocompatibility. However, micro-channel fabrication on glass with high precision has been challenging due to its brittle characteristics. This research presents the CO₂ laser-assisted micro-milling process and investigates the machining characteristics experimentally. Micro-channels without cracks were fabricated using micro-end mill and CO₂ laser irradiation as an assisting heat source. Compared to the process without laser heating in the same matching conditions, the average surface roughness was reduced by 96%, and cutting force was reduced by 28% and 66% for the feed and thrust direction, respectively. Continuous and sheared chips were observed with laser heating, indicating the process is in ductile-regime machining. Through the investigation of machining parameters, it was found that micro-channels with low average surface roughness can be achieved at the proper laser power when the workpiece is heated up to the strain point at tool position, at low feed rate, and at high axial depth of cut, as long as the tool withstands the cutting forces. Consequently, it can be concluded that it is possible to increase the material removal rate in micro-milling of borosilicate glass with high quality by using the CO₂ laser, which was found to be an effective and suitable heating method.     

基于BP神经网络的轮胎模具微铣削能耗预测

针对子午线轮胎模具微铣削加工过程中能耗计算问题,以主轴转速、每齿进给量、切削深度3个重要铣削参数作为变量,设计轮胎模具微铣削加工能耗实验.根据实验数据构建基于BP神经网络的微铣削能耗预测模型.通过改进预测模型的激活函数,提高模型的预测精度.结果表明:所提的预测模型有效,可以实现不同铣削参数组合下的能耗预测.     

Using two-dimensional vibration cutting for micro-milling

The purpose of this paper is to investigate the effects of assisted vibration cutting (VC) on the micro-milling quality of aluminum alloy Al 6061-T6. The desired vibration is proposed from the workpiece side by a two-dimensional vibrating worktable we developed. The slot produced by end milling is studied by examining its geometrical shape and machining accuracy. Through extensive experiments with end mills of diameter 1 mm, we found that slot oversize, displacement of slot center and slot surface roughness could be improved by imposing VC. The employment of VC increases the number of slots produced within the tolerance when high amplitude and proper frequency are imposed. With the help of Taguchi method and analysis of variance (ANOVA), we analyzed the effect of VC in end milling by investigating the slot-width accuracy. It is found that the use of second directional VC to minimize slot-width oversize in end milling is helpful.     

Cutting force denoising in micro-milling tool condition monitoring

An independent component analysis (ICA) algorithm for cutting force denoising was applied in micro-milling tool condition monitoring. In micro-milling, the comparatively small cutting force signal is prone to contamination by relatively large noise, and as a result it is important to denoise the force signal before further processing it. However, the traditional denoising methods, based on Gaussian noise assumption, lose here because the noise is identified as containing a high non-Gaussian component in the experiment. ICA was recently developed to deal with the blind source separation (BSS) problem. It solves the BSS problem by measuring the non-Gaussianity of the signal and it is particularly effective in the separation of non-Gaussian signals. This approach employs fixed-point ICA (FastICA), assuming the noises are sources and the force signal is an instantaneous mixture of sources and by treating the signal denoising process as a BSS. The results are illustrated both in time and frequency domains. The FastICA denoising performances are compared with the popular wavelet thresholding. The results show that FastICA performs better than wavelet. Theoretical discussion of the nature of ICA and wavelet thresholding supports the results: ICA separates both Gaussian and non-Gaussian noise sources, while wavelet only suppresses Gaussian noise.