Abstract

The high versatility of laser direct-write (LDW) systems offers remarkable opportunities for Industry 4.0. However, the inherent serial nature of LDW systems can seriously constrain manufacturing throughput and, consequently, the industrial scalability of this technology. Here we present a method to parallelise LDWs by using acoustically shaped laser light. We use an acousto-optofluidic (AOF) cavity to generate acoustic waves in a liquid, causing periodic modulations of its refractive index. Such an acoustically controlled optical medium diffracts the incident laser beam into multiple beamlets that, operating in parallel, result in enhanced processing throughput. In addition, the beamlets can interfere mutually, generating an intensity pattern suitable for processing an entire area with a single irradiation. By controlling the amplitude, frequency, and phase of the acoustic waves, customised patterns can be directly engraved into different materials (silicon, chromium, and epoxy) of industrial interest. The integration of the AOF technology into an LDW system, connected to a wired-network, results into a cyber-physical system (CPS) for advanced and high-throughput laser manufacturing. A proof of concept for the computational ability of the CPS is given by monitoring the fidelity between a physical laser-ablated pattern and its digital avatar. As our results demonstrate, the AOF technology can broaden the usage of lasers as machine tools for industry 4.0

Highlights

  • The fourth industrial revolution or Industry 4.0 (I4) exploits machines that are augmented by means of internet connections and sensor networks and are controlled by intelligent systems capable of monitoring the entire production chain and making autonomous decisions [1], [2]

  • The AOF technology can broaden the usage of lasers as machine tools for industry 4.0

  • Examples include laser beam parallelisation with passive optical elements or beam shaping by means of mask projection systems [29]. These methods lack tunability in terms of the number, position, and shape of the laser beams, impeding the realtime correction and adjustment of the manufacturing process. This problem can be solved with active optical elements such as digital micromirror devices (DMD) [30] and spatial light modulators (SLM) [31] that allow for the generation of tuneable light patterns; they suffer from pixellation issues and long response times and can be damaged

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Summary

INTRODUCTION

The fourth industrial revolution or Industry 4.0 (I4) exploits machines that are augmented by means of internet connections and sensor networks and are controlled by intelligent systems capable of monitoring the entire production chain and making autonomous decisions [1], [2]. LDW can use a laser beam to either alter a controlled volume of material causing localised damage to a surface (subtractive operation) or can transfer a specified amount of material onto a workpiece (additive operation) In both cases, the desired pattern is first drawn with computer-aided design (CAD) and sequentially etched or built by scanning the sample with the laser beam. These methods lack tunability in terms of the number, position, and shape of the laser beams, impeding the realtime correction and adjustment of the manufacturing process This problem can be solved with active optical elements such as digital micromirror devices (DMD) [30] and spatial light modulators (SLM) [31] that allow for the generation of tuneable light patterns; they suffer from pixellation issues and long response times and can be damaged. We illustrate a simple (two-level) laser-based CPS with AOF functionalities for the high throughput additive and subtractive machining of various materials such as metals, polymers, and semiconductors

ACOUSTICALLY SHAPED LASER BEAM
Laser diffraction through the acoustic cavity
Implementation of the physical space of a laser-based CPS
Digital assessment of the AOF-generated pattern fidelity
MATERIAL PROCESSING WITH THE AOF-LDW STATION
CONCLUSION
Optical Characterisation Setup
Laser Polymerisation
Sample Characterisation
Measurement of the Transfer Function
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