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2022
Journal Article
Title
Real-time defect detection through lateral monitoring of secondary process emissions during ultrashort pulse laser microstructuring
Abstract
Ultrashort pulse (USP) laser machining is characterized both by high spatial precisions as well as rapid changes during the processing. Laser pulses with durations of only a few hundred femtoseconds are deflected over the workpiece surfaces at speeds of up to 10 m/s. Due to the tradeoff between the precision and the productivity, USP laser machining processes can last up to multiple days. Online defect detection and their elimination is therefore essential in order to increase the stability of the established processing as well as accelerate the process development. However, monitoring of USP laser micromachining represents a great challenge because of both the high requirements for the spatial accuracy and the cost-intensive sensor integration. In the scope of this work, this challenge is tackled by laterally collecting the optical process emissions with photodiodes for different wavelength ranges. The monitoring system, which had previously been developed and had undergone initial testing, is further evaluated in this work. These most recent analyses aim to investigate the detection of the surface roughness prior to as well as its evolution during the USP laser machining. In addition, successful localization of defects induced on the workpiece surface by the USP processing is shown. Furthermore, the possibility of online process control was demonstrated by transferring the analysis algorithms to a field programmable gate array (FPGA) and implementing a real-time defect detection and feedback to the user. A decision for each data point is generated within the 10-μs cycle of the data acquisition. Furthermore, the system can be programmed flexibly and thus expanded to include real-time data analyses for further applications as well as process control. In conclusion, the analyses of laterally recorded secondary emissions have shown great potential for differentiating between surface roughness above 1 μm as well as tracking changes for every pass over the surface and localizing defects during the USP-laser machining.