Modeling and monitoring methods for spatial and image data

ABSTRACT

Intelligent sensing and computerized data analysis are inducing a paradigm shift in industrial statistics applied to discrete part manufacturing. Emerging technologies (e.g., additive manufacturing, micro-manufacturing) combined with new inspection solutions (e.g., non-contact systems, X-ray computer tomography) and fast multi-stream high-speed sensors (e.g., videos and images; acoustic, thermic, power and pressure signals) are paving the way for a new generation of industrial big-data requiring novel modeling and monitoring approaches for zero-defect manufacturing. Starting from real industrial problems, some of the main challenges to be faced in relevant industrial sectors are discussed. Viable solutions and future open issues are specifically outlined.

KEYWORDS:

• additive manufacturing
• functional data
• images
• profile monitoring
• quality monitoring
• shapes
• signal
• statistical process control
• surfaces

Acknowledgments

I would like to thank the organizers of the 2017 Stu Hunter Conference for inviting me to share my viewpoint. Special thanks to the conference discussants (David M Steinberg, Judy Jin) for their fruitful and enlightening comments. A final thank goes to colleagues and friends I have been working with during the last years: Enrique del Castillo (PSU), Massimo Pacella (Unisalento), N. Senin (Università di Perugia – University of Nottingham), Q. Semeraro, B. Previtali, G. Moroni and M. Grasso (Politecnico di Milano).

Notes

1 https://www.nrwinvest.com/fileadmin/_processed_/csm_industrial-revolution_997c038c69.jpg

2 https://www.bcgperspectives.com/content/articles/engineered_products_project_business_industry_40_future_productivity_growth_manufacturing_industries/?chapter=2

3 The metal object was processed using a Renishaw AM 250 industrial SLM system. An off-axis high speed camera CMOS Oylmpus i-speed 3 camera (frame rate 10 kfps — spatial resolution 528×396) was used to acquire the video.

Additional information

Notes on contributors

Bianca Maria Colosimo

Bianca Maria Colosimo is Professor in the Department of Mechanical Engineering of Politecnico di Milano (Italy), where she received her Ph.D. degree in Industrial Engineering. Her research interest is mainly in the area of Quality Engineering (i.e., statistical process monitoring, control, and optimization), with special attention to complex data modeling and monitoring (e.g., functional data, surfaces, signals, and images) for advanced manufacturing processes. She is senior member of the American Society for Quality.