Sigma Labs, Inc.

Divergent Technologies, Inc.

Sigma Additive Solutions, Inc.

B6 Sigma, Inc.

Massachusetts Institute of Technology

Harting Electric & Stiftung & Co. Kg

Vivek R Dave

Mark J Cola

R Bruce Madigan

Alberto M Castro

Martin S Piltch

Lars Jacquemetton

Glenn Wikle

Peter Campbell

Darren Beckett

David D Clark

Matias Roybal

Daniel A Hartman

Jeffrey Hastings Lang

Robert E Swanson

Felix Loske

Scott Betts

Lutz Tröger

Geremy Kleiser

Roger Frye

Joel W House

Enger Knop

Alan E Casallas

C Robert Kline, Jr

This invention teaches a multi-sensor quality inference system for additive manufacturing. This invention still further teaches a quality system that is capable of discerning and addressing three quality issues: i) process anomalies, or extreme unpredictable events uncorrelated to process inputs; ii) process variations, or difference between desired process parameters and actual operating conditions; and iii) material structure and properties, or the quality of the resultant material created by the Additive Manufacturing process. This invention further teaches experimental observations of the Additive Manufacturing process made only in a Lagrangian frame of reference. This invention even further teaches the use of the gathered sensor data to evaluate and control additive manufacturing operations in real time.

Multi-sensor quality inference and control for additive manufacturing processes

This disclosure describes various system and methods for monitoring photons emitted by a heat source of an additive manufacturing device. Sensor data recorded while monitoring the photons can be used to predict metallurgical, mechanical and geometrical properties of a part produced during an additive manufacturing operation. In some embodiments, a test pattern can be used to calibrate an additive manufacturing device.

Systems and methods for additive manufacturing operations

This invention teaches a quality assurance system for additive manufacturing. This invention teaches a multi-sensor, real-time quality system including sensors, affiliated hardware, and data processing algorithms that are Lagrangian-Eulerian with respect to the reference frames of its associated input measurements. The quality system for Additive Manufacturing is capable of measuring true in-process state variables associated with an additive manufacturing process, i.e., those in-process variables that define a feasible process space within which the process is deemed nominal. The in-process state variables can also be correlated to the part structure or microstructure and can then be useful in identifying particular locations within the part likely to include defects.

Defect detection for additive manufacturing systems

This disclosure describes an additive manufacturing method that includes monitoring a temperature of a portion of a build plane during an additive manufacturing operation using a temperature sensor as a heat source passes through the portion of the build plane; detecting a peak temperature associated with one or more passes of the heat source through the portion of the build plane; determining a threshold temperature by reducing the peak temperature by a predetermined amount; identifying a time interval during which the monitored temperature exceeds the threshold temperature; identifying, using the time interval, a change in manufacturing conditions likely to result in a manufacturing defect; and changing a process parameter of the heat source in response to the change in manufacturing conditions.

Methods and systems for quality inference and control for additive manufacturing processes

A multi-sensor, real-time, in-process current and voltage estimation system is disclosed including sensors, affiliated hardware, and data processing algorithms that allow accurate estimation of currents and voltages from magnetic and electric field measurements, respectively. Aspects of the system may be embodied in a detector that is readily attachable to conductors of an energized system for contactless current and/or voltage sensing of the conductors without requiring the conductors to be disconnected from the energized system.

Method and apparatus for contactless voltage and current estimation through measurements of electric and magnetic fields with redundant sensor arrays

The disclosed embodiments relate to the monitoring and control of additive manufacturing. In particular, a method is shown for removing errors inherent in thermal measurement equipment so that the presence of errors in a product build operation can be identified and acted upon with greater precision. Instead of monitoring a grid of discrete locations on the build plane with a temperature sensor, the intensity, duration and in some cases position of each scan is recorded in order to characterize one or more build operations.

Layer-based defect detection using normalized sensor data

This disclosure describes various methods and apparatus for characterizing an additive manufacturing process. A method for characterizing the additive manufacturing process can include generating scans of an energy source across a build plane; measuring an amount of energy radiated from the build plane during each of the scans using an optical sensor; determining an area of the build plane traversed during the scans; determining a thermal energy density for the area of the build plane traversed by the scans based upon the amount of energy radiated and the area of the build plane traversed by the scans; mapping the thermal energy density to one or more location of the build plane; determining that the thermal energy density is characterized by a density outside a range of density values; and thereafter, adjusting subsequent scans of the energy source across or proximate the one or more locations of the build plane.

Systems and methods for measuring radiated thermal energy during an additive manufacturing operation

An optical manufacturing process sensing and status indication system is taught that is able to utilize optical emissions from a manufacturing process to infer the state of the process. In one case, it is able to use these optical emissions to distinguish thermal phenomena on two timescales and to perform feature extraction and classification so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process. In other case, it is able to utilize these optical emissions to derive corresponding spectra and identify features within those spectra so that nominal process conditions may be uniquely distinguished from off-nominal process conditions at a given instant in time or over a sequential series of instants in time occurring over the duration of the manufacturing process.

Optical manufacturing process sensing and status indication system

This invention teaches a quality assurance system for additive manufacturing. This invention teaches a multi-sensor, real-time quality system including sensors, affiliated hardware, and data processing algorithms that are Lagrangian-Eulerian with respect to the reference frames of its associated input measurements. The quality system for Additive Manufacturing is capable of measuring true in-process state variables associated with an additive manufacturing process, i.e. those in-process variables that define a feasible process space within which the process is deemed nominal. The in-process state variables can also be correlated to the part structure or microstructure and can then be useful in identifying particular locations within the part likely to include defects.

Method and system for monitoring additive manufacturing processes

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Concord, NH, United States of America

Vivek R Dave