Abstract
Soft materials such as polydimethylsiloxane (PDMS), silicone, and hydrogels are extensively used in flexible wearable devices and biomedical fields, thanks to their excellent flexibility and elasticity (Young's modulus of 0.01 GPa), biocompatibility, and chemical stability. Molding is the traditional method to create geometrical features on these soft materials, the drawbacks however include (i) time-consuming fabrication chain containing pre-polymermixing, degassing, coating, curing, and demolding, (ii) expensive mold cost in terms of both design and manufacturing, and (iii) high demolding failure rate especially for small features. In contrast, laser processing is a mold-free method, where a beam of laser can directly create microstructures on soft substances with no mold cost, high production rate, nearly the full ranges of applicable material classes, and the superior ability to create tiny features on soft materials.
The academic research on the laser processing of soft materials is commonly based on rudimentary equipment on the optical platform and is therefore challenging for large-scale production thanks to the low mechanical accuracy (positioning and repositioning accuracy larger than 1 μm) and the poor optical stability. The laser machines on the commercial market, on the other hand, are mostly designed for hard materials only and can not be applied to soft materials due to the lower melting temperature and the high possibility to be thermally deformed or damaged. The damage-free clamping and accurate positioning for soft materials are also challenging. All the above factors result in the difficulties on manufacturing small-sized features (50 μm) on soft materials.
To address these challenges, a vision-based laser cutting machine for soft materials is designed in this project from the following aspects:
(i) Mechatronics design. A galvo based three-axis motion platform is designed with the maximum laser focus movement speed (4.76 m/s), addressing the slow speed (60 m/min) problem of traditional three-axis machines. The positioning and repositioning accuracy are separately 530 nm and 190 nm for X axis, 370 nm and 210 nm for Y axis, and 420 nm and 150 nm for Z axis, while the perpendicularity of the Z axis to the XY axes are less than 2 μm. For electrical architecture, the industrial computer in tandem with the motion control card is
developed.
(ii) Optics design and modelling. The optics system is designed in terms of laser components selection, laser beam modelling, and optical layout. Simulation and experiments are also performed to evaluate the designed optics system from laser focus point size (8.7 μm), focal length (158.9 mm), and the maximum single pulse energy density (549.5 J/cm2 ).
(iii) Motion control in laser machining. The proposed motion control system can achieve distortion-free fabrication of any patterns in Drawing Exchange Format (DXF) files. For ellipse- and spline-based features, the multi-arc fitting algorithm is introduced to address the machining accuracy loss caused by the traditional straight-line fitting method. The non-linear galvo error compensation algorithm is also proposed to minimize trajectory distortions caused by the galvo system error. Based on above, the curved processing trajectory becomes smoother, and the distortion error is diminished by one fourth.
(iv) Machine vision algorithm. The coaxial vision module with the camera calibration and distortion correction algorithm is constructed, while the OpenCV-based image recognition algorithm is suggested for automatic positioning.
(v) PDMS machining tests. Based on the proposed machine, the influence of different processing parameters on machined features on soft materials is carefully investigated, based on which the optimal processing parameters are determined (laser power is 40 %, speed is 500 mm/s, fill spacing is 10 μm). The smallest achievable features (50 μm), complex patterns (Apple, UNNC, butterfly, and elephant), and biomedical examples (microfluidic chips and Janus membrane) are demonstrated to validate the machine machining capability.
The proposed machine is anticipated to have wide applications in manufacturing of flexible devices such as sensor-integrated wearables, wound dressing devices, and smart textiles.
| Date of Award | Mar 2024 |
|---|---|
| Original language | English |
| Awarding Institution |
|
| Supervisor | Haonan Li (Supervisor), Gongyu Liu (Supervisor) & Xu Sun (Supervisor) |