Bianca Mara Colosimo, Ph.D.
Professor, Department of Mechanical Engineering
Politecnico di Milano
Abstract: Big data mining for Industry 4.0: opportunities and challenges in additive manufacturing.
Fostered by Industry 4.0, complex and massive data sets are currently available in many industrial settings and manufacturing is facing a new renaissance, due to the widespread of emerging process technologies (e.g., additive manufacturing, micro-manufacturing) combined to a paradigm shift in sensing and computing.
On the one hand, the product quality is characterized by free-form complex shapes, measured via non-contact sensors and resulting in large unstructured 3D point clouds. On the other hand, in-situ and in-line data are available as multi-stream signals, image and video-images.
In this scenario, traditional approaches for intelligent data analysis (i.e., statistical data modeling, monitoring and control) need to be revised considering functional data monitoring, manifold learning, spatio-temporal modelling, multi-fidelity data analysis. Starting from real industrial settings, opportunities and challenges to be faced in the current framework are discussed.
About Bianca Mara Colosimo, Ph.D.
Bianca Maria Colosimo is professor in the Department of Mechanical Engineering of Politecnico di Milano, where she is Deputy-Head of the Department and co-founder of the AddMe Lab, a research laboratory on novel solutions for additive manufacturing.
Politecnico di Milano is a technical university ranked among global top 20 in the area of Mechanical, Aeronautical & Manufacturing Eng. (QS international Ranking – 2020).
She is Editor-in-chief of the Journal of Quality Technology, senior editor of the Informs Journal on Data Science, and member of the editorial board of Additive Manufacturing Letters.
She is also member of the QSR Advisory Board at INFORMS, Council member of ENBIS (European Network of Business and Industrial Statistics), member of the Steering Committee of the World Manufacturing Forum (WMF) and of the Implementation Support Group of the platform Manufuture of the European Commission (http://www.manufuture.org).
She received her MSc and PhD in Industrial Engineering from Politecnico di Milano. After her PhD, she was visiting scholar at the Pennsylvania State University (PSU).
Her research interest is mainly in the area of industrial data modeling, monitoring and control, with special attention to big data challenges in advanced manufacturing. On these topics, she is author of 100+ peer-reviewed contributions, most of them published in peer-reviewed international journals and books.
She is included among the top 100 Italian woman scientists in STEM – (https://100esperte.it/)
Department of Industrial & Systems Engineering