In recent studies, polyurethane has shown multiple properties that make it an excellent candidate material in 4D printing. In this study, we present a simple and inexpensive additive method to print waterborne polyurethane paint-based composites by adding carboxymethyl cellulose (CMC) and silicon oxide (SiO 2) nanoparticles to the paint. The first function of CMC and SiO 2 is to improve rheological properties of the polyurethane paint for making a printable precursor, which improves the printing resolution and enhances additive manufacturability. Second, the composite precursors improve the curing rate of the polyurethane paint without changing its inherited shape memory properties. Third, the printed composite parts shown enhanced mechanical strength compared with that of the parts printed with pure polyurethane. Finally, the 3D printed polyurethane-CMC and SiO 2 parts exhibit time-resolved shape transformation upon heat stimulation. To the best of our knowledge, this is the first study of using the polyurethane paint as the precursor for 4D printing, which would open new possibilities in future applications in biomedical engineering, soft robotics and so on.

Understanding and predicting morphing response of printed active structures remain a challenge in 4D printing. To tackle it, in this paper, we present a consolidated data-driven approach enabled by an ensemble of machine learning (ML) algorithms. First, three ML algorithms were employed to quantitatively correlate a geometrical feature (thickness) with the final morphing shapes indicated by curvatures and curving angles. Among them, the gradient boosting algorithm achieved correlation factors (R 2) of 0.96 and 0.94 when predicting the curvatures and curving angles by using the data collected from 150 experiments. The random forest model enabled to rank the importance of fabrication parameters in determining the shape morphing behaviors. To forecast the dynamic response of printed structures, three time series forecast algorithms were implemented based on the time-dependent image data during morphing processes of the printed active structures. Among them, the exponential smoothing method achieved an average mean absolute percentage error of 0.0139. This work offers a proof-of-concept on how the ensemble ML algorithms can be employed to delineate and predict morphing mechanism of printed active structures, thus providing a new paradigm for advancing the state-of-the-art research in 4D printing.

Shell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time‐consuming and labor‐intensive procedure even for an experienced technician. To tackle this challenge, in this study, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task‐specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform algorithm, respectively. Assisted by a guiding cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real‐time analyzed by convolutional neural networks, showing accuracy of nearly 100% for identifying defective and nondefective tubes and 85% for four types of defective tubes and nondefective tubes.

In order to ensure surface shape accuracy of ground optical parts produced by locus forming factors of precision grinding machme that affects surface shape accuracy of the part are theoretically analyzed Formulas used to calculate the error that can be canceled through the machme fine adjusting device are deduced Usmg calculated error values to adjust the fme adjustmg device is able to reduce the surface shape error and then surface shape accuracy ofparts is ensured Keywords locus formmg spherical optical parts surface shape machining error precision grmdmg