Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things ( IIOT ) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

中文翻译：

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基于AI的建模和数据驱动的智能制造流程评估

智能制造是指通过利用高级分析方法在生产操作中实施的优化技术。随着在制造过程中部署工业物联网（IIOT）传感器的广泛增加，对数据管理的最佳，有效方法的需求不断增长。拥抱机器学习和人工智能以利用制造数据可以带来高效和智能的自动化。在本文中，我们基于进化计算和神经网络算法进行了全面分析，以使半导体制造更智能。我们提出一种动态算法，以获取有关半导体制造工艺的有用见解并应对各种挑战。我们详细介绍了如何利用遗传算法和神经网络提出一种智能特征选择算法。我们的目标是为控制制造过程提供先进的解决方案，并获得各种角度的观点，使制造商能够使用有效的预测技术。