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.     

Microstructure and hydrogen storage properties of Ti–V–Cr based BCC-type high entropy alloys

In this work, the crystal structure and hydrogen storage properties of V₃₅Ti₃₀Cr₂₅Fe₁₀, V₃₅Ti₃₀Cr₂₅Mn₁₀, V₃₀Ti₃₀Cr₂₅Fe₁₀Nb₅ and V₃₅Ti₃₀Cr₂₅Fe₅Mn₅ BCC-type high entropy alloys have been investigated. It was found that high entropy promotes the formation of BCC phase while large atomic difference (δ) has the opposite effect. Among the four alloys, the V₃₅Ti₃₀Cr₂₅Mn₁₀ alloy shows the highest hydrogen absorption capacity while the V₃₅Ti₃₀Cr₂₆Fe₅Mn₅ alloy exhibits the highest reversible capacity. The cause of the loss of desorption capacity is mainly due to the high stability of the hydrides. The higher room-temperature desorption capacity of the V₃₅Ti₃₀Cr₂₅Fe₅Mn₅ alloy is due to higher hydrogen desorption pressure. After pumping at 400 °C, the hydrides can return to the original BCC structure with only a small expansion in the cell volume.     

Slow strain rate technique for studying hydrogen induced cracking in 34CrMo4 high strength steel

Alloy hardened steels offer excellent combination of mechanical properties, hardenability and corrosion resistance. 34CrMo4 is a medium carbon, low alloy steel widely used due to a good combination of high-strength, toughness and wear resistance. However, this steel experiences hydrogen embrittlement (HE), a complex phenomenon depending on the composition and microstructure. This work estimates de loss of the mechanical properties caused by hydrogen in electrochemically H-charged specimens in absence of mechanical stress but also, at low strain rate and constant load. H-charging for 2 and 6 h induce YS losses of about 40% and 71% and UTS losses of 39% and 59%, respectively. The synergistic effect of the stress and the H-charging process leads to a higher loss, 91%, and a faster brittle fracture even though hydrogen content is similar to those firstly H-charged and then tested in air.     

汽车锁零部件碱性锌镍合金电镀工艺

介绍了汽车锁零部件碱性挂镀锌镍合金电镀生产线的开发过程,包括前期市场调研、中期小槽试验、后期成本核算、电镀工艺流程确定、废水处理工艺制定以及阳极膜应用可行性分析。     

Design of tailored biodegradable implants: The effect of voltage on electrodeposited calcium phosphate coatings on pure magnesium

Magnesium, as a biodegradable metal, offers great potential for use as a temporary implant material, which dissolves in the course of bone tissue healing. It can sufficiently support the bone and promote the bone healing process. However, the corrosion resistance of magnesium implants must be enhanced before its application in clinical practice. A promising approach of enhancing the corrosion resistance is deposition of bioactive coating, which can reduce the corrosion rate of the implants and promote bone healing. Therefore, a well-designed substrate-coating system allowing a good control of the degradation behavior is highly desirable for tailored implants for specific groups of patients with particular needs. In this contribution, the influence of coating formation conditions on the characteristics of potentiostatically electrodeposited CaP coatings on magnesium substrate was evaluated. Results showed that potential variation led to formation of coatings with the same chemical composition, but very different morphologies. Parameters that mostly influence the coating performance, such as the thickness, uniformity, deposits size, and orientation, varied from produced coating to coating. These characteristics of CaP coatings on magnesium were controlled by coating formation potential, and it was demonstrated that the electrodeposition could be a promising coating technique for production of tailored magnesium-CaP implants.     

冲击载荷下TC18钛合金力学性能模拟研究

TC18钛合金在退火状态下的屈服强度非常高,进行动态力学性能测试存在一定困难,可优先考虑采用数值模拟方法对其动态力学性能进行研究,而数值模拟结果的精确性主要取决于合金本构方程的精确性。为了减少数值模拟与实验结果之间的差异,在Johnson-Cook模型的基础上做了调整,利用ABAQUS软件的子程序接口自定义了材料本构子程序UMAT,以代码的形式来扩展ABAQUS程序的功能。通过与ABAQUS/Standard软件分析结果以及实验结果的对比,验证了自定义材料本构子程序UMAT计算结果的精确性与可靠性。该研究可为TC18钛合金的结构设计提供参考。     

ChronoCorrelator: Enriching Events with Time Series

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.