The Procter Gamble Company

University of California

Eric Christopher Berg

Louis J Cedrone

Daniel Richard Royce

Jeffrey Michael Kent

William Lawrence Lightcap

Helen Louise Von Den Steinen

Stephen Douglas Congleton

Arthur Joseph Koehler

Todd Michael Yeagle

Myron Lee Stuebe

Michael S Hamada

Harry F Martz

Systems and processes herein may be configured to correlate manufacturing parameters and performance feedback parameters with individual absorbent articles manufactured by a converting apparatus. Embodiments of the systems herein may include inspection sensors configured to inspect substrates and/or component parts advancing along the converting line and communicate inspection parameters to a controller and historian. The systems may also include process sensors configured to monitor equipment on the converting line and communicate process parameters to the controller and historian. The systems herein may also be adapted to receive performance feedback parameters based on the packaged absorbent articles. The systems may correlate inspection parameters, process parameters, and/or performance feedback parameters with individual absorbent articles produced on the converting line. The controller may also be configured to perform various functions based on the performance feedback parameters.

Systems and methods for adjusting target manufacturing parameters on an absorbent product converting line

A control arrangement decoupled two driven inputs for driven belt web accumulators using gear trains, gear trains with torque feed-forward control or gear trains with toque feed-forward control and velocity feedback control.

Web accumulator having limited torque disturbance

A computer-implemented process determines optimum configuration parameters for a buffered industrial process. A population size is initialized by randomly selecting a first set of design and operation values associated with subsystems and buffers of the buffered industrial process to form a set of operating parameters for each member of the population. An availability discrete event simulation (ADES) is performed on each member of the population to determine the product-based availability of each member. A new population is formed having members with a second set of design and operation values related to the first set of design and operation values through a genetic algorithm and the product-based availability determined by the ADES. Subsequent population members are then determined by iterating the genetic algorithm with product-based availability determined by ADES to form improved design and operation values from which the configuration parameters are selected for the buffered industrial process.

Optimizing the availability of a buffered industrial process

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Maineville, OH, United States of America

Eric Christopher Berg