3m Innovative Properties Company

3m Innovation Properties Company

Steven P Floeder

James A Masterman

Carl J Skeps

Jeffrey P Adolf

Brandon T Berg

Kenneth G Brittain

Arthur L Kotz

Michael L Ruegsegger

Wenyuan Xu

Jason P Smith

Gregory P Moriarty

Nathaniel S Rowekamp

Matthew V Rundquist

Steven R Wageman

Cory D Sauer

Joseph R Nesbitt

Gilles J Benoit

Matthew B Johnson

Brian E Brooks

Raghunath Padiyath

Aaron K Nienaber

Jon A Kirschhoffer

Nicholas A Johnson

Glenn E Casner

Maureen Ann Kavanagh

Frederick J Arsenault

Himanshu Nayar

Tyler W Olson

Peter O Olson

Kristy A Gillette

Jonathan B Arthur

Brett R Hemes

Thomas J Strey

Jack Wing Lai

Christopher M Brown

Nathan J Herbst

Patrick G Zimmerman

Lanny L Harklau

John A Ramthun

Timothy J Clemens

Mark W Orlando

Gregory D Kostuch

John W Henderson

Lori A Sjolund

Andreas Backes

Peter J Schneider

Miaoding Dai

Carter C Hughes

Sarah M Mullins

Crystal A Dehn

Terry A Okonek

Wayne R Roller

Matthew P Peick

Xi Yu

Arthur T Manning

Xin Yu

Zachary M Schaeffer

Scott L Barnett

Viruru Phaniraj

Steven R Dreger

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

Techniques are described for automatically detecting defects in webs using ultraviolet light to excite fluorescing agents in various layers of the web. In one example. a system for detecting defects in the web includes an inspection device and a processing unit. The inspection device includes a lighting unit, a filter, and an image capture device. The lighting unit is configured to emit ultraviolet light having a first wavelength to the web, the web including a fluorescing agent configured to emit light having a second wavelength upon excitation by the ultraviolet light. The image capture device is configured to capture one or more images of the light having the second wavelength after the light has passed through the filter. The processing unit is configured to determine whether the web includes a defect based on the one or more images.

Ultraviolet light-based inspection for detecting coating defects in manufactured webs using fluorescing agents

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

System and methods used to inspect a moving web () include a plurality of image capturing devices () that image a portion of the web at an imaging area. The image data captured by each of the image capturing devices at the respective imaging areas is combined to form a virtual camera data array () that represents an alignment of the image data associated with each of the imaging areas to the corresponding physical positioning of the imaging areas relative to the web. The image output signals generated by each of the plurality of image capturing devices may be processed by a single image processor, or a number of image processors () that is less than the number of image capturing devices. The processor or processors are arranged to generate the image data forming the virtual camera array.

Virtual camera array for inspection of manufactured webs

System and technique for inspecting a moving film by measuring the levels of light transmission through a thickness dimension of the film are described. The system includes a light source configured to provide light including a particular wavelength, or a particular range of wavelengths. The light from the light source is directed toward a first surface of the film, and an image capturing device is located adjacent to the light source on a second side of the film opposite the first surface, the image capturing device configured to measure the levels of light intensity exiting a second surface of the film. Measurements of the level of the light intensity passing through the film may be spatially synchronized to physical positions along the film to generated at least one roll map indicative of light transmission characteristics of the film over the imaged portions of the film.

Infrared light transmission inspection for continuous moving web

Systems and methods are directed to a device comprising a product status synchronization device. Examples of the product status synchronization device comprise a plurality of input ports and an encoder input, each of the input ports coupled to one of a plurality of registers and configured to receive a signal indicative of a status assigned to a product by one of a plurality of process stations, and to register the status assigned to the product into the one of the plurality of registers coupled to the input port, wherein one or more of the plurality of process stations are located at different distances from a predetermined location along a processing line and are configured to asynchronously process one or more products moving through the processing line.

Synchronizing multiple processing systems

Optical films having an optical axis with an orientation that varies along a width of the optical film and having one or more indicia recording the orientation of the optical axis at a plurality of locations in the optical film are provided. Systems and methods for producing and for converting the optical films are provided.

Optical films having an optical axis and systems and methods for processing same

Techniques are described for maintaining synchronization of inspection data when a web roll is converted into intermediate smaller rolls prior to cutting the web into individual parts. A system comprises a database that stores anomaly data acquired from a manufactured web. The anomaly data specifies positions anomalies within a manufactured web relative to a set of fiducial marks on the manufactured web. A conversion processing line comprises a fiducial mark reader to output position information for the set of fiducial marks on the manufactured web, a slitter that cuts the manufactured web into slit rolls, and a fiducial mark printer to print a set of fiducial marks on each slit roll. A position monitoring system maintains spatial synchronization of the anomaly data by computing an updated position for the anomalies relative to the set of fiducial marks printed on the slit rolls.

Maintaining registration of spatially synchronized data for web products

Techniques are described for inspecting a web and controlling subsequent conversion of the web into one or more products. A system, for example, comprises an imaging device, an analysis computer and a conversion control system. The imaging device images the web to provide digital information. The analysis computer processes the digital information to identify regions on the web containing anomalies. The conversion control system subsequently analyzes the digital information to determine which anomalies represent actual defects for a plurality of different products. The web inspection system may preferentially apply different application-specific defect detection recipes depending on whether a given anomaly is a repeating or random anomaly.

Application-specific repeat defect detection in web manufacturing processes

Techniques are described in which an image capture device captures image data from web material. The image data comprises pixel values for the cross-web field of view of the image capture device. An analysis computer includes a computer-readable medium that stores parameters for a plurality of different normalization algorithms to normalize a cross-web background signal for the image capture device to a common desired value. The computer-readable medium further stores coefficients specifying a weighting for each of the plurality of normalization algorithms. The analysis computer computes a normalized value for each of the pixels of the image data as a weighted summation of results from application of at least two of the pixel normalization algorithms using the stored parameters.

Optical web-based defect detection using intrasensor uniformity correction

A system for the characterization of webs that permits the identification of anomalous regions on the web to be performed at a first time and place, and permits the localization and marking of actual defects to be performed at a second time and place.

Apparatus and method for the automated marking of defects on webs of material

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Shoreview, MN, United States of America

Steven P Floeder