A simple and effective method without vacuum to control the wetting properties of AISI 316L stainless steel using femtosecond laser pulses at high repetition rate has been developed. Both hydrophilic and hydrophobic surfaces were formed by creating micro-conical structures on the surface with femtosecond laser irradiation in air. The scan speed was found to be an effective parameter in controlling micro-cone morphology, size and number densities and contact angles during surface wettability experiments. It was found during surface wettability experiments that the contact angle of water varied from 0 • (superhydrophilic) to 113 • on laser micro-cone textured surfaces depending on processing conditions. Additionally, a superhydrophobic AISI 316L stainless steel surface was created (contact angle ∼150 •) with a functionalized silane coating on already hydrophobic surface geometry.

Direct writing of multi-depth microchannel branching networks into a silicon wafer with femtosecond pulses at 200 kHz is reported. The silicon wafer with the microchannels is used as the mold for rapid prototyping of microchannels on polydimethylsiloxane. The branching network is designed to serve as a gas exchanger for use in artificial lungs and bifurcates according to Murray's law. In the development of such micro-fluidic structures, processing speed, machining range with quality surface, and precision are significant considerations. The scan speed is found to be a key parameter to reduce the processing time, to expand the machining range, and to improve the surface quality. By fabricating a multi-depth branching network as an example, the utilization of femtosecond pulses in the development of microfluidic devices is demonstrated.

Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including finite element modeling, but in this study, a data-driven approach consisting of two supervised deep-learning models was proposed. The first model was used for data transformation from an optical microscopic image to a material segmentation map, which characterizes the shape and location of the two sheets and the rivet by applying a convolutional neural network (CNN)based deep-learning structure. To validate the developed models, two types of sheet combinations were tested, namely, carbon-fiber-reinforced plastic (CFRP) and galvanized dual-phase steel (GA590DP) sheets, and steel alloy (SPFC590DP) and aluminum alloy (Al5052-H32) sheets. The transformation was performed with a mean intersection-over-union of 98.50% and a mean pixel accuracy of 99.78%. The next model, which was a novel generative model based on a CNN and conditional generative adversarial network with residual blocks, was then trained to predict the cross-sectional shape from the input punch force. The predicted cross-sectional shapes were compared with the experimental results of SPR. The overall accuracy was 94.20% for CFRP-GA590DP and 96.31% for SPFC590DP-Al5052, with respect to three key geometrical indexes, namely, rivet head height, interlock length, and bottom thickness.

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