A new hybrid electrochemical-mechanical process (PEMEC) for polishing complex and rough parts

A new polishing process for metallic parts has been developed by simultaneously coupling drag finishing and electrochemical polishing. This new hybrid process, called PEMEC, enables an improvement in the surface roughness within some minutes, and at the same time, preserves the shape of the edges. This hybrid process is based on a synergistic effect between abrasive and chemical mechanisms. It has confirmed its suitability to be applied on complex and rough surfaces produced by additive manufacturing SLM parts.     

Manufacturing by combining Selective Laser Melting and Selective Laser Erosion/laser re-melting

This study presents an experimental investigation to improve Selective Laser Melting (SLM) regarding aspects such as surface roughness, density, precision and micro machining capability by employing secondary processes such as Selective Laser Erosion (SLE) and laser re-melting. SLM is a layered additive manufacturing technique for the direct fabrication of functional parts by fusing together metal powder particles. Laser re-melting, applied after each layer or only on the top surfaces, is used to improve the roughness and density while SLE, a subtractive process, is combined with SLM to improve the precision and micro machining capability.     

Surface roughness prediction using measured data and interpolation in layered manufacturing

Layered manufacturing (LM) technology can efficiently fabricate 3D physical models without the restriction of geometric complexities. However, because of the LM process itself, the surface quality of processed parts is often unsatisfactory compared to that of machined parts made using traditional numerically controlled manufacturing technology. Hence, the surface roughness has become a matter of utmost concern despite the many potential advantages of LM. In the initial step of the LM process, reasonable process planning can be achieved by predicting the surface roughness in advance. Therefore, we propose an elaborate methodology to predict the surface roughness of LM processed parts. Theoretical and real characteristics of surface roughness distributions are investigated to reflect actual roughness distributions in the predictions, and a roughness distribution expression that can obtain surface roughness values for all surface angles is introduced using measured roughness data and interpolation. A prediction application is presented, and the validity and effectiveness of the proposed approach are demonstrated through several application examples.     

Additive manufactured porous titanium structures: Through-process quantification of pore and strut networks

Titanium and its alloys are successfully used in aerospace through to marine applications. Selective laser melting (SLM) is an additive manufacturing technique, which promises to allow production of novel Ti structures. However, there is still a paucity of accepted methods for quantifying build quality. The viability of using X-ray microtomography (μCT) to quantify and track changes in morphology of SLM Ti porous structures at each stage of the post-laser melting production was tested, quantifying its quality through process. Quantification was achieved using an accessible volume tool to determine pore and strut sizes. Removal of partially sintered struts by cleaning was visualised and quantified. Eighty-eight percent of the struts broken by the cleaning process were found to have connecting neck diameters of less than 180 μm with a mean of 109 μm allowing build criteria to be set. Tracking particles removed during cleaning revealed other methods to improve build design, e.g. avoiding low angle struts that did not sinter well. Partially melted powder particles from strut surfaces were quantified by comparing surface roughness values at each cleaning step. The study demonstrates that μCT provides not only 3D quantification of structure quality, but also a feedback mechanism, such that improvements to the initial design can be made to create more stable and reliable titanium structures for a wide variety of applications.     

选区激光熔化Al-Si合金组织特征及腐蚀行为研究进展

选区激光熔化(selective laser melting, SLM)制备的Al-Si合金在航空航天材料定制化和轻量化的发展中有着巨大潜力。然而,影响SLM成形件寿命的腐蚀行为尚未引起广泛关注。本文结合当前文献报道,根据SLM过程中的凝固特征分析了冶金缺陷和微观组织的形成原因,结合传统铸造Al-Si合金在含氯化合物中的腐蚀机理,进一步论述了SLM制备的Al-Si合金的腐蚀机理,归纳总结了冶金缺陷、微观组织和热处理工艺对于腐蚀行为的影响。发现试样的致密度,表面状态(粗糙度)和共晶Si的含量、形态及分布对腐蚀性能有重要影响。在此基础上,指出了目前SLM工艺下Al-Si合金腐蚀研究中存在的工艺参数不够系统,忽视Mg,Fe等元素的作用,研究手段和范围不够完善等不足以及未来进一步发展的方向。     

On Optimization of Surface Roughness of Selective Laser Melted Stainless Steel Parts: A Statistical Study

In this work, the effects of re-melting parameters for postprocessing the surface texture of Additively Manufactured parts using a statistical approach are investigated. This paper focuses on improving the final surface texture of stainless steel (316L) parts, built using a Renishaw SLM 125 machine. This machine employs a fiber laser to fuse fine powder on a layer-by-layer basis to generate three-dimensional parts. The samples were produced using varying angles of inclination in order to generate range of surface roughness between 8 and 20 µm. Laser re-melting (LR) as post-processing was performed in order to investigate surface roughness through optimization of parameters. The re-melting process was carried out using a custom-made hybrid laser re-cladding machine, which uses a 200 W fiber laser. Optimized processing parameters were based on statistical analysis within a Design of Experiment framework, from which a model was then constructed. The results indicate that the best obtainable final surface roughness is about 1.4 µm ± 10%. This figure was obtained when laser power of about 180 W was used, to give energy density between 2200 and 2700 J/cm² for the re-melting process. Overall, the obtained results indicate LR as a post-build process has the capacity to improve surface finishing of SLM components up to 80%, compared with the initial manufactured surface.     

基底形貌及润湿性对表面异质形核的影响

采用化学刻蚀及氟硅烷修饰的方法,制备了具有不同粗糙程度及润湿特性的纯铝基底,将基底置于NH4Cl-70％H2O溶液中,以激冷的方式触发NH4Cl晶粒形核,并考察了基底形貌及润湿性对表面异质形核的影响规律和机制.结果表明,未修饰的粗糙纯铝基底表面与氯化铵晶胚之间的反应性润湿特性,使异质形核较易发生,随着粗糙度因子的增大,形核点增加;修饰后的粗糙基底表面覆有的氟硅烷抑制了反应性润湿特性,使异质形核不易发生,且随着粗糙度因子的增大,形核点减少.所观察到的实验现象与基于Wenzel模型的粗糙基底异质形核分析结论有较好的吻合.     

切削速度对切屑形态和加工表面微观形貌的影响

为了研究钛合金车削过程中切削速度和切屑形态特征对已加工表面微观形貌的影响。采用硬质合金涂层刀具车削钛合金TC4,研究了切削速度对切屑形态特征、已加工表面粗糙度和轮廓最大高度的影响规律,分析了切屑底部毛边形貌、已加工表面形貌与表面粗糙度三者之间的关系。结果表明:在切削速度40~120m/min时,切屑底边出现锯齿形毛边,且随切削速度增大锯齿越明显。切屑底部锯齿毛边的形成是造成已加工表面波峰损伤形成的主要原因。影响已加工表面粗糙度的微观形貌特征包括硬质颗粒、粘结现象和波峰损伤。因此,为了获得高质量的加工表面,加工参数选择时必须规避硬质颗粒和严重的粘结现象,并控制切屑底部毛边形态特征。     

基于RBF神经网络的磨削表面粗糙度预测模型

工件表面粗糙度是反映表面完整性指标中极为重要的一个参数,也是衡量磨削加工质量的重要因素之一,准确地预测磨削表面粗糙度对于快速合理地选择磨削加工工艺参数具有重要意义。通过开展实际磨削实验获得磨削加工数据,对获取的样本数据进行归一化处理以适应RBF神经网络的学习。同时采用循环算法比较得出隐层的最优神经元个数,最终建立了基于径向基函数神经网络的磨削表面粗糙度预测模型,并利用MATLAB进行仿真预测。仿真结果表明:该预测模型准确率很高,能为表面粗糙度预测研究提供可靠数据。     

切削刃数量对球墨铸铁铣削性能及表面形貌特征的影响

目的 提高球墨铸铁铣削表面质量和刀具寿命。方法 通过刀具轨迹计算和切削试验,研究球墨铸铁平面铣削过程中切削刃数量对切削性能、刀具磨损和表面形貌特征的影响,并用分形维数和表面粗糙度共同表征表面形貌。结果 刀具轨迹分析表明,由于铣削过程中,刀具切削方向和进给方向间的夹角不断变化,铣削表面不同位置和方向的表面形貌存在差异,进而导致表面粗糙度存在较明显的差异。通过铣削试验研究切削刃数量对铣削表面不同位置和方向的几何特征的影响规律发现,随着切削刃数量的成倍增加,切削力显著增加,同时刀具磨损量降低了36.5%,表面粗糙度值降低了39.2%,表面轮廓分形维数值增加了4.8%。结论 增加切削刃数量可以使每齿切削力和刀具磨损均显著减小,刀具寿命显著增加,同时表面粗糙度减小,分形维数增大,即切削刃数量的增加使表面质量更好,表面轮廓结构更复杂。