The potential of a new design of adhesive microstructures in the micrometer range for enhanced dry adhesion is investigated. Using a two‐photon lithography system, complex 3D master structures of funnel‐shaped microstructures are fabricated for replication into poly(ethylene glycol) dimethacrylate polymer. The diameter, the flap thickness, and the opening angle of the structures are varied systematically. The adhesion of single structures is characterized using a triboindenter system equipped with a flat diamond punch. The pull‐off stresses obtained reaches values up to 5.6 MPa, which is higher than any values reported in literature for artificial dry adhesives. Experimental and numerical results suggest a characteristic attachment mechanism that leads to intimate contact formation from the edges toward the center of the structures. van der Waals interactions most likely dominate the adhesion, while contributions by suction or capillarity play only a minor role. Funnel‐shaped microstructures are a promising concept for strong and reversible adhesives, applicable in novel pick and place handling systems or wall‐walking robots.
High precision ultrasonic time-of-flight measurement is a well known part of non-destructive evaluation used in many scientific and industrial applications, for example stress evaluation or defect detection. Although ultrasonic time-of-flight measurements are widely used there are some limitations where high noise and distorted ultrasonic signals are conflicting with the demand for high precision measurements. Cross-correlation based time-of-flight measurement is one strategy to increase reliability but also exhibits some ambiguous correlation states yielding to wrong time-of-flight results. To improve the reliability of these measurements a new machine learning based approach is presented based on experimental data collected on tightened bolts. Due to the complex structure of the bolts the ultrasonic signal is influenced by boundary conditions of the geometry which lead to high number of the ambiguous cross-correlation results in practice. In this particular application, bolts are in practice evaluated discontinuously and without knowledge of the time-of-flight in the unloaded condition which prevents the use of all other available comparative preprocessing techniques to detect time-of-flight shifts. Three different preprocessing strategies were investigated based on variations in the bolting configurations to ensure a machine learning based model capable of predicting the state of the cross-correlation function for different bolting parameters. With this approach, we achieve up to 100% classification accuracy for both longitudinal and transversal ultrasonic signals under laboratory conditions. In the future the method should be extended to become more robust and be applicable in real-time for industrial applications.
The cross-correlation function (CCF) is an established technique to calculate time-of-flight for ultrasonic signals. However, the quality of the CCF depends on the shape of the input signals. In many use cases, the CCF can exhibit secondary maxima in the same order of magnitude as the main maximum, making its interpretation less robust against external disturbances. This paper describes an approach to optimize ultrasonic signals for time-of-flight measurements through coded excitation sequences. The main challenge for applying coded excitation sequences to ultrasonic signals is the influence of the piezoelectric transducer on the outgoing signal. Thus, a simulation model to describe the transfer function of an experimental setup was developed and validated with common code sequences such as pseudo noise sequences (PN), Barker codes and chirp signals. Based on this model an automated optimization of ultrasonic echoes was conducted with random generated sequences, resulting in a decrease in the secondary positive maximum of the CCF to 56.6%. Based on these results, further empiric optimization leveraging the nonlinear regime of the piezoelectric transducer resulted in an even lower secondary positive maximum of the CCF with a height of 25% of the first maximum. Experiments were conducted on different samples to show that the findings hold true for small variations in the experimental setup; however, further work is necessary to develop transfer functions and simulations able to include a wider parameter space, such as varying transducer types or part geometry.
Ultrasonic metal welding is an energy-efficient, fast and clean joining technology without the need of additional filler materials. Single spot ultrasonic metal welding of aluminum to steel sheets using automotive materials has already been investigated. Up to now, further studies to close the gap to application-relevant multi-metal structures with multiple weld spots generated are still missed. In this work, two different spot arrangements are presented, each consisting of two weld spots, joined 0.9 mm thick sheets of wrought aluminum alloy AA6005A-T4 with 1 mm sheets of galvannealed (galvanized and annealed) dual-phase steel HCT980X. An anvil equipped with variable additional clamping punches was used for the first time. The tensile shear forces reached 4076 ± 277 N for parallel connection and 3888 ± 308 N for series connection. Temperature measurements by thermocouples at the interface and through thermal imaging presented peak temperatures above 400 °C at the multi-metal interface. Microscopic investigations of fractured surfaces identified the Zn layer of the steel sheets as the strength-limiting factor. Energy-dispersive X-ray spectroscopy (EDX) indicated intermetallic phases of Fe and Zn in the border areas of the weld spots as well as the separation of the zinc layer from the steel within these areas.
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