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Creators/Authors contains: "Li, Guoqiang"

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  1. Free, publicly-accessible full text available March 1, 2023
  2. Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35%more »higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.« less
  3. Cellular materials have been widely used in load carrying lightweight structures. Although lightweight increases natural frequency, low stiffness of cellular structures reduces natural frequency. Designing structures with higher natural frequency can usually avoid resonance. In addition, because of the less amount of materials used in cellular structures, the energy absorption capability usually decreases such as under impact loading. Therefore, designing cellular structures with higher natural frequency and higher energy absorption capability is highly desired. In this study, machine learning and novel inverse design techniques enable to search a huge space of unexplored structural designs. In this study, machine learning regression and Generative Neural Networks (GANs) were used to form an inverse design framework. Optimal cellular unit cells that surpass the performance of biomimetic structures inspired from honeycomb, plant stems and trabecular bone in terms of natural frequency and impact resistance were discovered using machine learning. The discovered optimal cellular unit cells exhibited 30–100% higher natural frequency and 300% higher energy absorption than those of the biomimetic counterparts. The discovered optimal unit cells were validated through experimental and simulation comparisons. The machine learning framework in this study would help in designing load carrying engineering structures with increased natural frequency and enhancedmore »energy absorption capability.« less
  4. Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internalmore »defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.« less
  5. Shape memory polymers (SMPs) are one of the intriguing functional materials and have been widely and intensively studied. In order to apply these new polymers to load bearing engineering structures and devices, developing physics-based thermomechanical constitutive models is mandatory. The aim of this Tutorial is to demonstrate how to establish a thermomechanical constitutive model for SMPs. It begins with classifications of SMPs, followed by a discussion on the underlying physics for different SMPs. After that, three classical SMP thermomechanical modeling frameworks are introduced, which include the visco-elasto-plastic based rheological framework, the storage strain-based phase transition framework, and the representative unit cell based multi-branch framework. Next, three commonly adopted new model establishment methods are presented within these frameworks with detailed examples. Finally, future perspectives on this research direction are discussed. We hope that this Tutorial will help readers understand the roadmap from physics to mathematical modeling of SMPs.

    Free, publicly-accessible full text available March 17, 2023