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Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. Artificial neural networks (ANN) is the most popular machine learning model in recent years. Although many ANN models have been used in the design and analysis of composite materials and structures, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posting new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the nonlinear constitutive modeling, multiscale surrogate modeling, and design optimization of composite materials and structures. This review has been designed to focus on the discussion of the general frameworks and benefits of ANN models to the above problems. Moreover, challenges and opportunities in each key problem are identified and discussed. This paper is expected to open the discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.

Based on STC89C52RC Single Chip microcontroller, the control system of seedling vegetable harvester was designed with the help of modular design method. According to the mode of operation for seedling vegetable harvester, the infrared remote device and photoelectric sensors were selected, and then the hardware of the control system including running system, cutting device and conveyor device was set up. Through the performance test of the prototype, the harvester can go forward, backward, accelerate, decelerate, turn left, and turn right; the photoelectric sensor can control cutting device and transport device to start and stop automatically and easily, which shows the control system has good performance.