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Conference papers
Effect of Viscoelastic Properties on 3D Printability of CNF Hydrogels, 19NANO

Effect of Viscoelastic Properties on 3D Printability of CNF Hydrogels, 19NANO

Conference papers
Monocomponent nanocellulose for biobased 3D printing, 19NANO

Monocomponent nanocellulose for biobased 3D printing, 19NANO

Conference papers
Thiol-ene Modifications to alter CNF Film Properties, 19NANO

Thiol-ene Modifications to alter CNF Film Properties, 19NANO

Conference papers
CNC-based Glass Fiber Sizing for Composite Reinforcement, 19NANO

CNC-based Glass Fiber Sizing for Composite Reinforcement, 19NANO

Conference papers
Inverse thermo reversible methylcellulose/cellulose nanocrystal nanocomposite hydrogel for fiber spinning, 19NANO

Inverse thermo reversible methylcellulose/cellulose nanocrystal nanocomposite hydrogel for fiber spinning, 19NANO

Conference papers
Research and Development of Cellulose Nanomaterials in China, 19NANO

Research and Development of Cellulose Nanomaterials in China, 19NANO

Journal articles
Magazine articles
Open Access
Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning, TAPPI Journal November 2019

ABSTRACT: Packaging manufacturers are challenged to achieve consistent strength targets and maximize pro-duction while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning.The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under ever-changing machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Journal articles
Magazine articles
Over the Wire, Paper360º November/December 2019

Over the Wire, Paper360º November/December 2019

Journal articles
Magazine articles
Which Safety Conversations Have the Most Impact?, Paper360º November/December 2019

Which Safety Conversations Have the Most Impact?, Paper360º November/December 2019

Journal articles
Magazine articles
Working Toward a Closed-Loop Future, Paper360º November/December 2019

Working Toward a Closed-Loop Future, Paper360º November/December 2019

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