3m Innovative Properties Company

Jeffrey P Adolf

Steven P Floeder

Gregory P Moriarty

Cory D Sauer

Gilles J Benoit

Brian E Brooks

Raghunath Padiyath

Aaron K Nienaber

Nicholas A Johnson

Jon A Kirschhoffer

Glenn E Casner

Maureen Ann Kavanagh

Frederick J Arsenault

Himanshu Nayar

Peter O Olson

Tyler W Olson

Jonathan B Arthur

James A Masterman

Carl J Skeps

Brett R Hemes

Thomas J Strey

Christopher M Brown

Patrick G Zimmerman

Nathan J Herbst

Mark W Orlando

Timothy J Clemens

Lori A Sjolund

John W Henderson

Andreas Backes

Peter J Schneider

Miaoding Dai

Carter C Hughes

Nathaniel S Rowekamp

Sarah M Mullins

Xin Yu

Zachary M Schaeffer

Scott L Barnett

Viruru Phaniraj

Samuel S Schreiner

Shane T Van Kampen

An example method for selecting product images for training a machine-learning model includes obtaining product images to include in an image population; receiving an indication of an image selection strategy for determining if a product image is to be included in a set of images of interest; determining image transforms based on configuration data for the indicated image selection strategy, wherein the image transforms perform image manipulation operations to obtain transformed image data for each of the product images in the image population; selecting a subset of images from the image population for inclusion in the set of images of interest based on the indicated image selection strategy and the transformed image data; determining one or more descriptive labels and applying the one or more descriptive labels to the respective sets of images; and training an inspection model for a product inspection system based on the labeled images.

Active learning management system for automated inspection systems

An inspection system includes an inspection device having at least one image capture device. The image capture device captures image data of a sheet part passing through the inspection device. A processing unit of the inspection device provides the image data representative of the sheet part to a plurality of neural networks, where each of the neural networks is trained to identify a corresponding defect in the sheet part and output data indicative of the presence of the corresponding defect. The processing unit determines a quality category of the sheet part based on the data indicative of the presence of the corresponding defect output by each corresponding neural network. The processing unit can further output the quality category of the sheet part to a sorter that can sort the sheet part based on the quality category.

Inspecting sheet goods using deep learning

A system includes a robot, a measuring device, and a processor. The robot is configured to dispense, based on at least one process parameter, a liquid adhesive bead onto a substrate. The measuring device is configured to measure at least one characteristic of the bead shape. The processor is configured to determine, based on a reference bead shape and at least one reference process parameter, response surface profile of the liquid adhesive. The processor is configured to compare the measured bead shape to a reference bead shape and, responsive to determining the measured bead shape is different than the reference bead shape, determine, based on the response surface profile, at least one updated process parameter. The updated process parameter is configured to cause the robot to dispense a second bead having the reference bead shape.

Automated liquid adhesive dispensing using portable measuring device

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a process of manufacturing a product. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of input settings for manufacturing a product, based on a causal model that measures causal relationships between input settings and a measure of a quality of the product; ii) determining the measure of the quality of the product manufactured using the configuration of input settings; and iii) adjusting, based on the measure of the quality of the product manufactured using the configuration of input settings, the causal model.

Manufacturing a product using causal models

A system for measuring dimensions of film to be applied to a window includes a plurality of removable cards for placement on the window, each of the plurality of removable cards having a plurality of visible correspondence points at pre-defined positions on the respective removable card. The system further includes a mobile device having an image capture device, and the image capture device is configured to capture an image of the window having a set of correspondence points visible in the image. Additionally, the system includes a server configured to receive the image of the window captured by the mobile device and process the image to determine dimensions of a film to be cut for the window based on the set of correspondence points and the pre-defined positions, and processing the image removes perspective distortions in the image. The system is configured to output the dimensions of the film.

Determining dimensions for providing a precut window film

An example system is described herein. The example system may include an inspection device comprising at least one image capture device, the at least one image capture device configured to capture a reference image of a sheet part. Additionally, the example system may include a processing unit configured to identify at least one primary point in the reference image and identify at least one secondary point in a mask image. The processing unit may transform the mask image based on the at least one primary point and the at least one secondary point. The processing unit may apply the transformed mask image to the reference image to identify an inspection region within the reference image, process the inspection region of the reference image to determine the quality of the sheet part, and output information indicative of the quality of the sheet part.

Automated inspection for sheet parts of arbitrary shape from manufactured film

Growing community of inventors

Rochester, MN, United States of America

Jeffrey P Adolf