Programmable Thermo-Responsive Self-Morphing Structures Design and Performance

Abstract

Additive manufacturing (AM), also known as 3D printing, was introduced to design complicated structures/geometries that overcome the manufacturability limitations of traditional manufacturing processes. However, like any other manufacturing technique, AM also has its limitations, such as the need of support structures for overhangs, long build time etc. To overcome these limitations of 3D printing, 4D printing was introduced, which utilizes smart materials and processes to create shapeshifting structures with the external stimuli, such as temperature, humidity, magnetism, etc. The state-of-the-art 4D printing technology focuses on the “form” of the 4D prints through the multi-material variability. However, the quantitative morphing analysis is largely absent in the existing literature on 4D printing. In this research, the inherited material anisotropic behaviors from the AM processes are utilized to drive the morphing behaviors. In addition, the quantitative morphing analysis is performed for designing and controlling the shapeshifting. A material–process–performance 4D printing prediction framework has been developed through a novel dual-way multi-dimensional machine learning model. The morphing evaluation metrics, bending angle and curvature, are obtained and archived at 99% and 93.5% R2, respectively. Based on the proposed method, the material and production time consumption can be reduced by around 65–90%, which justifies that the proposed method can re-imagine the digital–physical production cycle.

Research on the Safety Evaluation Method for Quayside Container Cranes Based on the Best–Worst Method–Pythagorean Fuzzy VIKOR Approach

“…Concurrently, with the ongoing research and development in new technologies, the crane manufacturing industry is progressively incorporating an array of innovative processes, materials, and technological applications. The research findings of Li [1] and Pandeya [2] demonstrate substantial potential and prospects for application in future crane manufacturing endeavors.…”

“…Acquiring the Optimal Solution When the alternative with the smallest compromise value Q i , denoted as X (1) , satisfies the following two conditions, it is considered as the optimal solution: Condition 1: Q(X (2) ) − Q(X (1) ) ≥ DQ, where X (2) represents the alternative with the second smallest compromise value Q i when sorted in ascending order.…”

“…(b) If only Condition 2 is not satisfied, then both the alternatives X (1) and X (2) (the ones with the smallest and the second smallest Q i values, respectively) are considered as optimal solutions. In this case, both X (1) and X (2) are the compromise solutions.…”

Wind Speed Prediction Based on Error Compensation

“…As a cheap and effective method, it also plays a positive role in reducing operating costs and improving wind power competitiveness [ 8 ]. Through predicting the wind speed, it can guide the power scheme to adjust the power generation plan reasonably [ 9 ], change the torque of a wind turbine to maximize the use of wind power [ 10 ], protect the safety of the wind turbines so that the wind farm can use more advanced materials [ 11 , 12 , 13 ], and optimize the layout of wind turbines and improve the economic benefits of wind farms [ 14 ].…”