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.

As a PhD candidate, you will primarily address the following four topics: (1) planning & scheduling (2) prescriptive quality (3) predictive maintenance and (4) hybrid intelligence. These PhD positions are part of the Green Transport Delta, a public-private innovation programme (funded by the Dutch Ministry of Economic Affairs and Climate) that aims to make Dutch transport sectors futureproof and sustainable. You will be embedded in the consortium around electrification, which focuses on improving various aspects of battery-powered electric transport as a key component of the transition to climate-neutral mobility. In a joint collaboration with VDL Nedcar, the largest Dutch automotive manufacturing company, you will work in a team to investigate AI techniques within VDL Nedcar's manufacturing environment. The ultimate goal is to make intelligent decisions in a transparent and reliable way, reduce costs, and save energy and reduce overall CO2 emissions.

The issue of sustainability has never been more prominent. Around the world, headlines are full of warnings on the dangers of climate change as companies, people and governments campaign for greener policies and practices. One of the sectors most affected by this drive is chemical processing and manufacturing. Every year, more than a thousand new chemical substances are introduced into the U.S. For each one, the potential applications need to be weighed against myriad potential health and environmental impacts across a broad range of metrics, such as energy consumption, toxicity or biodegradability across product lifecycles. If chemical processors and manufacturers are able to shift toward more sustainable practices – e.g., processes that are more energy efficient, require lower input volumes and are more environmentally and biologically-friendly – the benefits for both the industry and the environment would be significant.