A laser peen forming is a sheet metal forming method using laser induced shock waves. The laser peen forming with an ultra-short-pulse laser is a kind of non-thermal and die-less forming process, and is favorable for micro forming. The authors applied the laser peen forming to the bending of pure titanium thin sheet with a picosecond laser and a femtosecond laser. The changes of bending properties with atmosphere and pulse duration were investigated. The femtosecond laser irradiation in air showed the best bending efficiency. The femtosecond laser is applicable to laser cutting, also. Some thin sheets were cut into complicated shapes and bent by laser peen forming with femtosecond laser. The combined process allowed the production of various complicated small parts.

No abstract

The demand for the mass production of micro-lens arrays (MLAs) is increasing. An MLA is fabricated through an injection molding process, and its mold is manufactured by a five-axis high-precision machine tool using a small diameter endmill. A visual examination is not available to judge the quality of the mold while machining. Therefore, an effective process monitoring technology must be developed. A promising approach is to apply a servomotor current to in-process monitoring because as long as the servomotor works well, no external sensors, capital investment, or maintenance processes are required. From this perspective, a machine learning-based shape error estimation method using only the servomotor current is proposed. To explore the relationship between the motor current generated during micro-milling and the shape error of the mold, the servomotor current in X-, Y-, and Z-axes was recorded, and the corresponding shape error of the MLA mold was measured after machining. Input data were prepared by converting time-domain servomotor current data to frequency-domain data using short-time Fourier transform and reducing the dimensions of the data via principal component analysis. In terms of a meaningful label for the output data, the average shape error in the machined area corresponding to each window was provided. The input/output relationships were used to train five different machine learning models, and the accuracy of shape error estimation using each model was evaluated. In addition, the estimation accuracies using the X-, Y-, and Z-axes were compared to find the axis that senses the shape error with the highest accuracy. The results show that the non-linear method using the X-axis servomotor current information closest to the machining point achieved the highest shape error estimation accuracy.

No abstract