The final finish of workpieces and molds with microstructures on the surface requires maintaining the global precision of entire surface. On the basis of the requirement, we investigated the precision polishing on V-shaped microgrooves carved on a plate with utilizing a magnetic compound fluid involving abrasive grains (MCF-based processing fluid). It was elucidated that the overall surfaces of the V-groove can be precisely polished while maintaining the surface profile in case DC electric field is applied in addition to a pulsed magnetic field. In contrast, the application of just DC or pulsed magnetic field induces non-polishing of the V-shaped microgrooves. The synchronous application of electric and magnetic fields is effective. Referring the result from our previous studies that the amount of polishing removal is correlated with the torque acting on the tool and inferring the experimental data of the torque characteristics on the polishing of the plate measured by viscosity-rheometer, the ER effect is a significant factor which has relation to the amount of polishing removal.

Directed energy deposition (DED) irradiates a laser beam on a metal surface making a melt pool and supplies metal powder onto the pool to model a three-dimensional shape. The process requires keeping the optimum standoff distance (SOD) between the nozzle and a melt pool for achieving high-accuracy, -efficiency, and -quality modeling. However, it is difficult to optimally maintain the SOD because of complicated parameters in DED processes. In this study, three systems are developed for high-accuracy, -efficiency, and -quality modeling by DED. The first system measures the SOD in-process using a CMOS camera. The second generates modeling paths to model the N+1th layer according to the measured SOD of the Nth layer during the modeling of the Nth layer. The third generates an NC program using the generated modeling paths in real time and models a target object. The effectiveness of the total system was confirmed through verification experiments.

Metal powder using in the powder bed fusion with a laser beam (PBF-LB/M) is required to have superior flowability for the uniform formation of powder bed and the smooth supply from powder tank. In this study, adding silica nanoparticles to metal powders used in the PBF-LB/M is proposed to improve the flowability of metal powders. The flowability of metal powders adding the silica nanoparticles is evaluated statically and dynamically, and the appropriate evaluation parameter is recommended for the metal powder used in the PBF-LB/M processes. In addition, the melt pool aspects during the PBF-LB/M processes are visualized by a high-speed imaging apparatus and the influence of flowability on the metal powder behaviours is investigated. As results, the avalanche energy was the effective parameter to know the flowability of metal powders used in the PBF-LB/M processes. The addition of silica nanoparticles had the effect of increasing the metal powders incorporated into the melt pool and the spatter particle behaviours depended on the amount of incorporated metal powders to the melt pool. In addition, the microstructure and hardness of built part were not influenced by the silica nanoparticles.

The rack method is often used in the plating process of fine workpieces. It is difficult to automate the rack loading task by the off-line teaching or the force feedback procedures since the deformation of racks and pins with repeated use. In this study, at first, we developed an autonomous rack loading algorithm for fine workpieces by measuring rack pin positions and attitudes using on-line non-contact sensing. In addition, we constructed an on-line non-contact sensing fine workpiece Rack Loading task support Robot System (RLRS) that implemented our newly algorithm. The RLRS consists of the Pin Sensing Device (PSD), a 6-DOF serial robot arm and a robot vision for correcting hole position of workpiece. The autonomous rack loading algorithm for the fine workpiece uses the pin measurement data from PSD and the hole position correction data from robot vision to plan the motion of the robot arm. The verification experiment of RLRS had conducted using 0.8mm diameter pin and 1.1mm diameter hole of workpieces, and resulted in 93.3% success rate. Although improved accuracy of the pin position detection is needed, our newly algorithm has been suggested to be useful for automating rack loading task of fine workpieces.