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：在材料科学与制造领域开发环境友好的可持续工艺具有重要意义。人工智能可在决策支持中发挥关键作用——我们前期研究已证实这一结论，并基于所提出的机器学习方法开发出相关工具。此类工具实现了计算预估与专家系统的功能。本研究通过使用再生和回收材料的敏捷制造决策支持，探索材料科学的环境可持续性。这是一种将特定废物流转化为高附加值产品安全且负责任的途径。我们提出在人工智能中采用数据驱动方法，通过应用机器学习模型进行预测分析以指导制造决策支持。具体包括：利用人工神经网络研究材料热处理参数及其对性能的影响；通过卷积神经网络等深度学习技术探索晶粒尺寸检测；运用随机森林等其他分类器分析相分数检测。所有方法的结果均显示出进一步研究的潜力（例如：人工神经网络在预测淬火回火热处理工艺中微观结构发展时准确率可达约90%）。未来工作面临多重挑战：研究各类计算机视觉模型（VGG、ResNet等）以获取可持续工艺所需的最优精度、效率和鲁棒性；开发基于机器学习的敏捷制造决策支持领域专用工具；通过整合再生材料使用率与产品有效性等指标评估可持续性影响。本研究为智能制造绿色技术带来突破，其研究动机源于人工智能在材料科学中这一极具前景领域的相关探索。