Dislocation climb in Mo5SiB2 during high-temperature deformation

During high-temperature compression tests on intermetallic Mo₅SiB₂, the dislocation microstructures vary with increasing temperature and strain rate. At 1400 °C, an increasing tendency exists for slip planes to be of an unexpected type (e.g., {143) and {523)) as a function of the decreasing strain rate and increasing strain that originates from a dislocation climb. As the temperature increases to 1600 °C, the internal strain rate of 6.07 × 10^− 3 s^− 1 from the dislocation climb at 4% strain exceeds the applied value of 1.67 × 10^− 3 s^− 1, and thus, the climb mainly controls the plastic strain, as evidenced by a strength that is lower than that at 1200 °C under the same conditions.     

Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing

Defect detection is a critical measurement process for intelligent manufacturing systems to provide insights for product quality improvement. For complex products such as integrated circuit wafers, several types of defects are usually coupled in a piece of wafer to form a mixed-type defect, which poses a challenge to current defect detection methods. This paper proposed a knowledge augmented broad learning system with a knowledge module and broad selective sampling module, which provides a multichannel selective sampling network to decouple the mixed-type defects. In this model, each channel is equipped with a pre-trained deformable convolution model to extract the feature of a fixed single-type defect. The knowledge module is designed to activate the candidate network channel by pre-detection of wafer maps. The experiment results indicated that the proposed model outperforms conventional models and other deep learning models, which demonstrated that the knowledge augmented broad selective sampling mechanism is effective for mixed-type defect detection.     

A mechanism informed neural network for predicting machining deformation of annular parts

Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods.     

Laser damage resistance of plasma-sprayed alumina and honeycomb skeleton/alumina composite ceramic coatings

Al₂O₃ and honeycomb skeleton-Al₂O₃ composite coatings on Titanium alloy (Ti–6Al–4V) were prepared by atmospheric plasma spraying. A laser ablation experiment on as-sprayed coatings was performed. In this paper, the laser damage resistance, microstructure, phase composition of Al₂O₃ coatings were examined. 3D Dimensional Confocal Microscopy, Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Energy Dispersive Spectrometry (EDS) characterized the laser damage morphology, microstructure, phase composition, and element analysis, respectively. The influence of the honeycomb skeleton on the laser ablation damage on as-sprayed coatings was investigated by a comparative analysis of the laser damage morphology with different laser ablation times and gas flow. The results show that the honeycomb skeleton raises thermal conductivity and thermal diffusivity. Moreover, a “tower”-like dendrite was generated during the laser irradiation of the composite coating. The honeycomb skeleton refined the structure, suppressed crack propagation, and reduced the influence of gas flow on cracks. Under the same experimental laser ablation parameters, the laser damage area of the honeycomb skeleton-Al₂O₃ composite coating was smaller than that of the Al₂O₃ coating. It was demonstrated that the laser damage resistance of the honeycomb skeleton-Al₂O₃ composite coating was superior to that of the Al₂O₃ coating.     

Phase equilibria in the Fe-Mo-Ti ternary system at 1000 °C

An isothermal section of the Fe-Mo-Ti ternary system at 1000 °C has been constructed using data acquired from a series of seven alloys. The limit of solubility of Fe in the continuous A2 phase field between Ti and Mo has been determined, as have the extents to which Mo may be accommodated in the B2 TiFe phase, and Ti in the D8₅ Fe₇Mo₆ phase. The B2, D8₅ and C14 Fe₂ (Ti, Mo) intermetallics were found to have limited tolerance for non-stoichiometric compositions. The positions of the A2 + B2 + C14 and A2 + C14 + D8₅ three-phase fields were determined, along with the extents of the A2 + B2, A2 + D8₅, A2 + C14, C14 + B2 and C14 + D8₅ two-phase fields. No ternary phases were observed in any of the alloys studied.     

低盐溶液电火花-电解复合加工微小方孔试验

综合电火花加工快速蚀除材料与电解加工溶解重铸层的优势,提出了在低电导率的NaNO3溶液中使用电极逐层往复式铣削加工微小方孔的电火花-电解复合加工方法,并研究了电解液浓度、电压和电容参数对微小方孔加工质量的影响。结果表明:电解液浓度与加工电压对微小方孔加工质量影响较大,电容影响相对较小。选用最优加工参数在100μm厚的321不锈钢片上加工微小方孔,得到的方孔加工质量好、侧壁表面无重铸层,且工具电极相对损耗仅0.05%。     

进给速度对不同纤维方向CFRP铣削表面形貌的影响

采用金刚石涂层硬质合金铣刀对0°、45°、90°、135°4种纤维方向的碳纤维增强复合材料(CFRP)进行了顺铣加工试验。通过对铣削力和加工表面形貌的对比,分析了纤维方向和每转进给量对加工表面质量的影响。结果表明:主切削力随着每转进给量的增大而增大,0°方向纤维受到的主切削力最大,90°方向纤维受到的主切削力最小;0°方向纤维表面破碎树脂与纤维的残留随着每转进给量的增大而增多,135°方向纤维表面树脂粘附逐渐减小;90°方向纤维表面会有大小不同的微坑,同时在样件上、下表面易产生分层缺陷,45°方向纤维表面多呈现沟槽或波浪形形貌。     

基于MATLAB的声表面波标签数据采集与处理

利用Matlab控制数字示波器,实现声表面波-射频识别(SAW-RFID)系统数据采集的功能,以获取示波器采集的标签编码回波信号。上位机在Matlab上进行了显示界面的设计,利用最大相关法对采集的回波信号作算法解调处理,最终完成了声表面波射频识别系统的搭建。     

应用CVD金刚石涂层工具研磨单晶蓝宝石

通过热丝化学气相沉积(HFCVD)法制备了具有球状晶结构、棱锥形晶结构和棱柱形晶结构等3种不同表面特征的化学气相沉积(CVD)金刚石涂层工具,以提高其研磨效率。通过正交实验法研究了金刚石涂层晶粒形态、载荷、工作台转速、研磨时间等4个工艺参数对蓝宝石材料去除率和表面粗糙度的影响。结果表明:金刚石涂层的晶粒形态对材料去除率和表面粗糙度影响较大;球状晶结构金刚石涂层切向力较小,棱柱形晶结构金刚石涂层切向力较大;选择棱柱形晶CVD金刚石涂层工具研磨蓝宝石,在研磨加工参数为载荷0.15 MPa、转速100 r/min、研磨时间3 min时,其材料去除率为0.397μm/min,表面粗糙度为0.354μm。结果表明:提出的CVD金刚石涂层工具可用于进一步加工、研磨蓝宝石切片,去除其表面划痕,从而改善工件表面质量。