Advances in machine learning for sustainable manufacturing

This thesis is a collection of four papers with each paper presenting an advancement in machine learning for the sustainability of manufacturing systems. The first paper considers the application of machine learning to address the environmental pillar, the second and third paper consider advances for the economic pillar, the fourth paper considers both the environmental and the economic pillars.<br><br>Traditional manufacturing has two main flaws in terms of sustainability: it relies on raw materials extracted from finite natural reservoirs and it labels as ``waste'' any product that has lost its usefulness.

To address both the issues, the first paper identifies an emerging computer-vision-enabled material monitoring technology and it provides a survey of works relevant for its development.<br><br>Several greedy algorithms for unsupervised variable selection have been proposed in the past without a systematic benchmarking. Therefore, in the second paper we review and provide a comparative study of these methods. Their application is of interest in the semiconductor manufacturing industry to reduce the cost of silicon wafer monitoring. <br>The third paper primarily focuses on the same semiconductor application as second one.