Ti-6Al-4V is a benchmark Ti alloy. Laser wire additive manufacturing (LWAM) offers advanced manufacturing capability to the alloy for applications possibly including exploration of outer space. As a typical multiple-variable process, LWAM is complex, which, however, can be analyzed, predicated or even optimized by artificial intelligence (AI) methods such as machine learning (ML). In this study, printing parameters of the Ti-6Al-4V is firstly optimized using single-track-single-layer experiments, and then single-track-multiple-layer samples are printed, whose properties in terms of hardness and compressive strength are analyzed subsequently by both experiments and ML. The two ML approaches, artificial neural network (ANN) and support vector machine (SVM), are employed to predict the experimental results, whose coefficients of determination R² show good values. Further optimized properties are realized by adopting genetic algorithm (GA) and simulated annealing (SA) approaches, which contribute to high mechanical properties achieved, for instance, an engineering compressive strength of about 1694 MPa. The results here indicate that important mechanical properties of the LWAM-prepared Ti alloys can be well predicted and enhanced using suitable ML approaches.

介绍了10 Gbit/s EPON(以太网无源光网络)系统中对称OLT(光线路终端)光模块的设计方案和关键技术, 重点描述在接收端如何实现双速率突发接收, 以及10 Gbit/s突发接收部分硬件电路的构成。测试结果表明: 该模块在突发接收条件下接收端的接收建立时间和灵敏度均满足10 Gbit/s EPON标准IEEE Std.802.3av的要求。