It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties; deep learning via advances such as convolutional neural networks to explore grain size detection; and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90\% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes; creating domain-specific tools using machine learning for decision support in agile manufacturing; and assessing impacts on sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science.
翻译:有必要发展环境友好的材料科学和制造的可持续流程; 大赦国际可以在这方面在决策支持方面发挥重要作用,正如我们早先的研究所显示的那样,通过利用我们提议的基于机械学习的方法开发工具,这些工具有助于计算估计和专家系统的目的; 这项研究通过使用回收和再生材料的灵活制造中的决策支持,解决材料科学中的环境可持续性问题; 将具体的废物流转向增值产品,这是一个安全和负责的方法; 我们提议在AI中采用数据驱动方法,采用机器学习模型来进行预测分析,以指导对制造业的决策支持。 这包括利用人工神经网络研究影响材料热处理的参数和对其特性的影响; 通过革命神经网络等进步来探索粮食规模检测; 以及随机森林等其他分类者来分析碎片检测。 所有这些方法的结果似乎都有望进一步开展工作,例如,ANN的产量准确度约为90<unk>,用于预测微结构的发展,作为对生产过程中的决策支持。 未来的工作将面临若干挑战:调查各种计算机愿景模型(VGG、ResNet等智能科学网络等),以便研究影响材料的热度; 利用智能神经网络等先进技术,以最佳的准确性评估。</s>