High-velocity walk-through programming for industrial applications

Traditionally, industrial robots are programmed by highly specialized workers that either directly write code in platform-specific languages, or use dedicated hardware (teach-pendant) to move the robot through the desired via-points. Unsurprisingly, the inherently complex and time-consuming nature of this task is one of the factors that are still preventing industrial manipulators from being massively adopted by companies that require a high degree of flexibility in order to cope with limited production volumes and rapidly changing product requirements. In this context, the introduction of sensor-based walk-through programming approaches represents the ideal solution as far as the need to reduce programming complexity and time is concerned. Nevertheless, the main shortcomings of these solutions typically consist in limited reachable velocities during the programming phase due to safety constraints and in relying on open robot controllers. To this regard, this paper proposes a control architecture for walk-through programming of industrial manipulators specifically designed in order to (i) reach high velocities while guaranteeing the operator’s safety; (ii) allow straightforward integration with a generic closed robotic controller. The proposed solution is extensively validated on an industrial manipulator.     

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.     

Finite element modeling of machining of hydrogenated Ti-6Al-4V alloy

The present study is undertaken to investigate the effect of hydrogen on the cutting performance of Ti-6Al-4V alloy by FEM. Mechanical behaviors of hydrogenated Ti-6Al-4V alloy are studied at elevated temperatures and high strain rates with split Hopkinson pressure bar. The Johnson–Cook model was developed combined with quasi-static experimental data. A numerical model is developed to simulate the cutting process. The results of the experiments and simulations agreed well. The results demonstrate that the presence of hydrogen has a significant effect on the cutting forces and temperature, and the cutting forces and temperature increase first and then decreased gradually with the increasing of hydrogen contents. The simulation results show that titanium alloys with 0.3% hydrogen have better machinability at high cutting speed.     

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.     

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。     

Buckling analysis of stiffened plates subjected to non-uniform biaxial compressive loads using conventional and super finite elements

This paper presents the buckling analysis of stiffened plates, using both conventional and super finite element methods. Mindlin plate and Timoshenko beam theories are utilized so as to formulate the plate and stiffeners, respectively. The arbitrary oriented stiffeners can be positioned anywhere within the plate element and are not limited to be placed on nodal lines. Therefore, any configuration of plate and stiffeners can be modeled. Furthermore, extensive boundary conditions as well as general in-plane loading conditions can be considered using the proposed method. As the applied in-plane loads are not uniform, the buckling load is evaluated in two steps. First, the elasticity problem is solved to determine the stress distribution in prebuckling stage. Applying the principle of minimum potential energy, based on derived stress distribution, yields to the buckling equation of stiffened plates. Numerical examples are proposed to study the accuracy and efficiency of the developed super elements. Effects of various combinations of biaxial loads along with different boundary conditions on buckling characteristics of stiffened panels are also investigated.     

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。