In recent years, many investigations have been devoted to fused deposition modeling (FDM) of high-performance polymer-polyetheretherketone (PEEK) and carbon-fiber-reinforced PEEK (CF/PEEK) for biomedical and aerospace applications. However, the staircase effect naturally brought about by FDM restricts further applications of 3D-printed PEEK and its composites in high-temperature molds, medical implants, and precision components, which require better or customized surface qualities. Hence, this work aimed to reduce the staircase effect and improve the surface quality of 3D-printed PEEK and CF/PEEK parts by dry milling of the fluctuant exterior surface. The co-dependency between 3D printing parameters (raster angle and layer thickness) and milling parameters (depth of cut, spindle speed, and feed rate per tooth) were investigated through experiments. The difference in removal mechanisms for PEEK and CF/PEEK was revealed. It was confirmed that the smearing effect enhanced the surface quality based on the morphology analysis and the simulation model. Both the raster angle of +45°/−45° and the small layer thickness could improve the surface quality of these 3D-printed polymers after dry milling. A large depth of cut and a large feed rate per tooth were likely to deteriorate the finished polymer surface. The spindle speed could influence the morphologies without significant changes in roughness values. Finally, a demonstration was performed to verify that dry milling of 3D-printed amorphous PEEK and CF/PEEK parts could lead to a high surface quality for critical requirements.

Nowadays, there are time-critical applications involving linear equations, such as the fault reconstruction problem, where hard response time constraints and robustness to external disturbances are expected. Zeroing neural network (ZNN) is one of the effective solutions to time-variant problems including time-variant linear equations. This paper proposes a strictly predefined-time convergent and noise-tolerant ZNN (SPTC-NT-ZNN) to solve time-variant linear equations. Differing from the existing ZNN models, the designed SPTC-NT-ZNN is enhanced to be convergent in strictly predefined time while exhibiting noise tolerance. This guarantees the desirable timely convergence and robustness for time-critical applications. In theory, the strictly predefined-time convergence and noise-tolerance properties of the proposed SPTC-NT-ZNN are mathematically proved in a rigorous manner. Comparative validations are performed to verify that the SPTC-NT-ZNN outperforms existing typical ZNNs, regarding the convergence and robustness performance. To demonstrate potential applications, the SPTC-NT-ZNN is applied to 3D stereo reconstruction and motion control of a Franka Emika Panda robot, showing the efficacy of the proposed method.

A unique micro electrochemical machining (ECM) method based on a scanning micro electrochemical flow cell (SMEFC), in which the electrolyte is confined beneath the tool electrode instead of spreading on the workpiece surface, has been developed and its feasibility for fabricating mesoscale channels has been investigated. The effects of the surface conditions, the applied current, the feed rate, the concentration of the electrolyte and several geometrical parameters on the machining performance have been investigated through a series of experiments. The cross-sectional profile of the channels, the roughness of the channel bottom, the width and depth of the channel, the microstructures on the machined surface and the morphologies of the moving droplet have been analyzed and compared under different machining conditions. Furthermore, experiments with different overlaps of the electrolyte droplet traces have also been conducted, in which the SMEFC acts as a “milling tool”. The influences of the electrode offset distance (EOD), the current and the feed rate on the machining performance have also been examined through the comparison of the corresponding cross-sectional profiles and microstructures. The results indicate that, in addition to machining individual channels, the SMEFC system is also capable of generating shallow cavities with a suitable superimposed motion of the tool electrode.

Zr-based bulk metallic glasses (BMGs) have unique mechanical and chemical properties, which has attracted much attention from academia and industry. Electrochemical machining (ECM) is a non-contact subtractive method very suitable for these materials without grain boundaries. Nevertheless, past work on ECM of Zr-based BMGs indicated the infeasibility of the traditional non-acid water-based electrolytes. In this research, microsecond pulse ECM of Zr-based BMGs in the NaNO ₃ -ethylene glycol (EG) electrolyte has been explored to overcome the difficulties in conventional salt water-based electrolytes. First, polarization behaviors of four kinds of Zr-based BMGs and related elementals in NaNO ₃ -EG electrolyte were derived to examine the passive and transpassive performance. The dissolution mechanism of Zr-based BMGs according to the dwell time was investigated based on XPS analysis. The effects of the current density and pulse parameters were evaluated according to the surface quality, the groove profile, etc. The results show that pulse parameters have a great influence on stray corrosion, due to the high content of late transition metal components on the surface. Taking micro dimples as an example, the NaNO ₃ -EG electrolyte exhibit ability of reducing the pitting corrosion and offering smooth surfaces with acceptable stray corrosion and high machining localization.